Computational Identification of Indole Alkaloids as Novel Hsp90 ATPase Inhibitors with Anticancer Potential

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Aboalroub This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7419782/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The ATPase activity of heat shock protein 90 (Hsp90) is crucial for stabilizing and regulating many oncogenic client proteins, thereby supporting cancer progression and tumor cell survival. Although several small-molecule inhibitors have demonstrated preclinical promise, their clinical use remains limited due to toxicity and moderate effectiveness, highlighting the need for new chemotypes with better therapeutic profiles. Indole alkaloids, a diverse group of natural compounds with wide-ranging biological activities—including anticancer, antimicrobial, and enzyme-inhibition effects—were explored here as potential Hsp90 ATPase inhibitors through an extensive computer-based approach. Molecular docking of natural-product derivatives showed strong binding affinities (–10.004 to –10.691 kcal/mol), favorable pharmacokinetic and toxicity predictions, and key interactions with catalytic residues Asp93, Lys58, Gly97, and Thr184. Physicochemical and ADME profiling further validated favorable drug-like properties, including adherence to key medicinal chemistry filters, acceptable solubility, moderate lipophilicity, high oral bioavailability, and no structural alerts. Several indole-alkaloid derivatives also exhibited off-target interactions with several kinases, indicating potential for polypharmacological anticancer effects but emphasizing the importance of selectivity profiling. Overall, this research presents indole alkaloids as promising Hsp90-targeted anticancer candidates. Additional mechanistic studies and preclinical validation are necessary to advance these compounds toward clinical development. Hsp90 inhibitors Indole alkaloids Molecular docking Anticancer drug discovery ATPase activity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Heat shock protein 90 (Hsp90) is a highly conserved, ATP-dependent molecular chaperone essential for the folding, maturation, stabilization, and activation of various client proteins [1]. These include kinases, transcription factors, steroid hormone receptors, and signaling molecules, all vital for maintaining cellular homeostasis, growth, and survival [2]. Generally, Hsp90 supports protein balance by aiding proper folding of its clients and preventing aggregation during cellular stress like heat shock, oxidative stress, and hypoxia [1]. Beyond normal functions, misregulation of Hsp90 is linked to many diseases [2–4]. In cancer, it is often overproduced in an active form that binds and stabilizes oncogenic proteins such as HER2, AKT, RAF, and mutant p53 [4, 5]. This stabilization promotes cancerous behaviors by supporting cell growth signals, inhibiting cell death, encouraging blood vessel formation, and enabling metastasis [1, 4]. Hsp90 also plays a role in neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and Huntington’s by affecting the fate of misfolded and aggregating proteins [6, 7]. Moreover, certain pathogens hijack Hsp90 to fold their virulence factors, highlighting its importance as a therapeutic target in both cancer and infectious diseases [8]. The justification for targeting Hsp90 in therapy rests on its critical role in maintaining the function of many oncogenic proteins. Blocking Hsp90 interrupts its chaperone cycle, leading to the degradation of client proteins via ubiquitin–proteasome pathways, which in turn disables multiple cancer-driving pathways simultaneously [9]. This broad action provides an advantage over kinase inhibitors that target only a single signaling pathway [4, 9, 10]. Most small-molecule Hsp90 inhibitors work by competitively binding to the N-terminal domain (NTD), which contains a highly conserved ATP-binding site crucial for chaperone activity [11–13]. Although first-generation inhibitors like geldanamycin derivatives show promising anticancer effects, their clinical use is restricted by toxicity at high doses, poor solubility, and limited bioavailability [14]. Therefore, ongoing research is essential to identify new chemical structures that can effectively inhibit Hsp90 with better pharmacokinetic properties and safety. Indole alkaloids are a vast and chemically diverse group of natural products containing an indole moiety—a bicyclic structure made of a benzene ring fused to a pyrrole ring [15]. They originate biosynthetically from tryptophan through various enzymatic processes, leading to a wide array of chemical structures [16]. These alkaloids are found across multiple biological kingdoms, including plants, marine organisms, fungi, and bacteria. Indole alkaloids possess a wide range of biological activities, making them highly valuable in medicinal chemistry as pharmacophores [15, 16]. Many key drugs and lead compounds are derivatives of indole alkaloids, highlighting their therapeutic importance [15]. Examples include vincristine and vinblastine (microtubule inhibitors for cancer treatment), camptothecin derivatives (topoisomerase I inhibitors), and reserpine (used for hypertension and psychosis) [17]. Their bioactivities span anticancer, antibacterial, antiviral, antifungal, antiparasitic, anti-inflammatory, and neuroactive effects [15–17]. In cancer therapy, indole alkaloids often work by disrupting essential cellular processes like microtubule assembly, DNA replication, and the cell cycle [16–18]. Their high affinity for various biological targets stems from the indole ring’s ability to engage in π–π stacking, hydrogen bonds, and hydrophobic interactions, allowing recognition by numerous enzymes and receptors [16]. As pharmacophores, they exhibit remarkable structural flexibility, enabling modifications to improve potency, selectivity, and pharmacokinetic profiles [15–18]. The planar aromatic system and electron-rich nitrogen help interactions with both hydrophobic and polar regions of target proteins [15, 16]. This versatility has led to extensive exploration in structure-based drug design, such as kinase inhibitors, protease inhibitors, and chaperone modulators [17]. Overall, their adaptability makes indole alkaloids promising candidates for targeting Hsp90, where rational modifications of the indole core and its substituents can optimize engagement of the ATP-binding site. In this study, we used a comprehensive computational approach to explore indole alkaloid–based molecules as potential Hsp90 inhibitors. We conducted molecular docking, ADMET predictions, and off-target and cytotoxicity predictions to evaluate their binding strength, pharmacokinetic features, and stability within the Hsp90 binding site. This integrated strategy aims to identify promising indole alkaloid frameworks that could be further developed into effective and selective anticancer agents targeting Hsp90. Numerous indole-alkaloids were identified from various chemical compound databases as potential inhibitors of Hsp90 ATPase activity. These compounds show promising drug-like characteristics, favorable pharmacokinetic profiles, and cytotoxic effects. Their strong binding affinities, between -10.004 and -10.691 kcal/mol, indicate their potential for further development. Examination of their binding interactions highlights key hydrogen bonds and hydrophobic interactions with essential catalytic residues, such as Lys58, Gly97, and Thr184, supporting their role as Hsp90 inhibitors. Furthermore, these indole-alkaloids showed strong multi-cancer cytotoxicity, highlighting their potential as lead anticancer candidates. Collectively, these results suggest that indole-alkaloids could constitute a new chemotype for Hsp90-targeted cancer treatments. Additional mechanistic studies and preclinical testing are necessary to move these compounds toward clinical use. Materials and Methods 2.1 Ligand Library Preparation A targeted library of indole alkaloid–based molecules was assembled by gathering compounds from various public chemical and natural product databases, including ZINC (https://zinc.docking.org), Mcule (https://mcule.com), LOTUS Natural Products Database, PubChem (https://pubchem.ncbi.nlm.nih.gov), and relevant scientific literature. Selection criteria prioritized maximizing structural diversity and key pharmacophoric features. This meant focusing on compounds with an indole core and potential substituents that could engage in different interactions within the Hsp90 binding site. The initial compound set was curated to remove duplicates, inorganic species, and molecules over 500 Da. Additionally, the library was filtered using Lipinski’s Rule of Five (Ro5), which states that an orally active drug generally has a molecular weight of 500 Da or less, a calculated LogP value not greater than 5, no more than five hydrogen bond donors (HBD), and no more than ten hydrogen bond acceptors (HBA) [19]. Compounds that violated more than one of these parameters were excluded, as such deviations are often linked to poor pharmacokinetic behavior, including limited absorption, distribution, metabolism, and excretion (ADME). This filtering step was essential to increase the chance of identifying bioavailable and pharmacologically active molecules suitable for further development. The shortlisted compounds were then converted into their SMILES (Simplified Molecular Input Line Entry System) representations, which served as input for subsequent computational analyses in the virtual screening workflow. 2.2 Molecular Docking The N-terminal domain of human Hsp90 in complex with a co-crystallized inhibitor (PDB ID: 6LTI, 1.59 Å resolution) was obtained from the RCSB Protein Data Bank (https://www.rcsb.org) [20]. Protein preparation was carried out using AutoDock Tools (1.5.7) by removing water molecules, ions, and non-essential heteroatoms, and extracting the co-crystallized ligand to define the binding site [21]. Missing hydrogens were added, polar hydrogens included for hydrogen-bond calculations, and Gasteiger charges assigned; non-polar hydrogens were merged with their parent carbons. A grid box with a volume of 10 ų and a default spacing of 0.375 Å was generated, centered at coordinates x = 33.00, y = -14.00, and z = -20.00, to define the docking search space. The actual docking simulations of the selected indole-alkaloids were performed using AutoDock Vina (1.2.3) with Sampling exhaustivity of 40. The resulting conformations were analyzed from the AutoDock log files, with emphasis placed on the lowest binding energy (LBE) values to identify the most favorable poses. To enhance reliability, the conformer with the largest cluster size was selected for further analysis. The final chosen conformers were exported and visualized using ChimeraX-1.8 [22]. 2.3 In silico prediction of physicochemical properties, drug-likeness, and lead-likeness for the top-ranked molecules The physicochemical properties, drug-likeness, and lead-likeness profiles of the top-ranked indole alkaloid molecules identified from docking studies were evaluated using SwissADME (http://www.swissadme.ch/) [23]. Compounds were first screened according to Lipinski’s Rule of 5. Additional drug- and lead-likeness assessments were performed by applying medicinal chemistry filters, including the Veber (polar surface area ≤ 140 Ų and ≤ 10 rotatable bonds), Ghose (molecular weight 160–480 Da, logP −0.4 to +5.6, molar refractivity 40–130), and Egan (logP ≤ 5.88 and polar surface area ≤ 131 Ų) rules, thereby providing a comprehensive evaluation of the compounds’ oral bioavailability and suitability as potential leads. 2.4 In Silico Cytotoxicity Prediction in Tumor Cell Lines Evaluating cytotoxicity in tumor cell lines is crucial in anticancer drug development, as it helps assess both therapeutic effectiveness and safety. Computational methods for predicting cytotoxicity provide a quick and cost-efficient alternative to laboratory testing, reducing resource use during early candidate screening. In this study, the cytotoxic potential of top-ranked indole alkaloid–based molecules was evaluated using CLC-Pred 2.0 (Cell Line Cytotoxicity Predictor), a web platform for in silico prediction of activity against cancerous and non-cancerous cell lines (https://www.way2drug.com/Cell-line) [24]. CLC-Pred 2.0 employs QSAR models trained on extensive experimental cytotoxicity datasets, predicting activity by correlating the compound's structural features with known bioactive molecules in its database. For each indole alkaloid derivative, the SMILES notation was generated and submitted to the CLC-Pred server. The results indicated the probability of cytotoxic activity across various tumor cell lines, aiding in the identification of candidates with the highest predicted anticancer potential for further investigation. 2.5 Off-Target Prediction To assess the potential off-target effects of the top-ranked indole alkaloid compounds, an in silico screening was conducted using the Way2Drug PASS (Prediction of Activity Spectra for Substances) platform (http://www.way2drug.com/PASS) [25]. This tool predicts a wide range of biological activities, including both desired pharmacological effects and possible adverse or toxicological outcomes, based on structural similarity to known bioactive compounds. Each prediction is given as the probability of activity (Pa) versus inactivity (Pi), with higher Pa values indicating a greater likelihood that the compound will exhibit the predicted activity. For this study, activities with Pa > 0.7 were considered highly probable, while those with 0.5 < Pa < 0.7 were regarded as moderately probable. This method enabled the identification of potential off-target interactions, offering insights into the safety profile and pharmacological significance of the selected indole alkaloids. Results Computer-aided drug discovery (CADD) is a rapidly advancing field that helps identify and analyze molecules with desired biological effects [11, 13, 26–28]. Recent advances in computational chemistry and machine learning have significantly improved the accuracy of predicting and ranking chemical compounds for specific biological functions. In this study, we used in silico methods to find indole alkaloid–based molecules capable of inhibiting Hsp90 ATPase activity. Using bioactive small molecules to influence protein function is a promising approach for developing targeted anticancer therapies. Compared to larger therapeutics like monoclonal antibodies and polypeptides, small molecules offer advantages such as lower manufacturing costs, oral bioavailability, improved patient compliance, and favorable pharmacokinetics [29, 30]. They can also target various proteins, including kinases, proteasomes, and chaperones like Hsp90 [10, 31, 32]. This research employed virtual screening and molecular docking to identify diverse indole alkaloid scaffolds with high binding affinity and firm interaction profiles at the Hsp90 ATP-binding site. These computational findings serve as a valuable initial step in drug discovery, highlighting promising candidates for biological activity. However, confirming the therapeutic potential of these compounds will require further validation through in vitro biochemical tests, cell-based cytotoxicity assessments, and detailed pharmacokinetic and pharmacodynamic studies. 3.1 Ligand Library Preparation and Shortlisting A diverse library of indole alkaloid–based molecules was compiled from public databases such as ZINC, Mcule, LOTUS, and PubChem, along with relevant literature. The selection prioritized maintaining the indole core while adding substituents capable of hydrogen bonding, hydrophobic interactions, and ion–π stacking with Hsp90. The dataset was curated to eliminate duplicates, inorganic compounds, and molecules larger than 500 Da, ensuring drug-like properties. After initial structural optimization, a filtering process based on Lipinski’s Ro5 was applied to favor bioavailable, pharmacologically relevant Hsp90 inhibitors. Compounds were evaluated for molecular weight (≤500 Da), cLogP (≤5), hydrogen bond donors (≤5), and acceptors (≤10). Those exceeding these thresholds were excluded, mainly due to high lipophilicity or molecular weight, reducing the initial 1,240 compounds to 812 (a 34.5% reduction). This improved dataset suited molecular docking, focusing on scaffolds with balanced physicochemical properties to enhance binding potential and ADME profiles. The filtered compounds were converted into SMILES format for subsequent computational analyses. 3.2 Evaluation of Indole Alkaloid–Based Molecules as Potential Hsp90 Inhibitors via Molecular Docking After assembling the indole alkaloid library, compounds that did not meet Lipinski’s Rule of Five were removed, and the remaining molecules underwent molecular docking against the Hsp90 N-terminal domain (PDB ID: 6LTI). Binding affinities were estimated as docking scores (ΔG, kcal/mol), and compounds were ranked based on these scores, with more negative values indicating stronger predicted binding. As shown in Table 1 and depicted in Figure 1, a subset of ligands had docking scores of ≤ –10.0 kcal/mol, indicating strong binding potential and good complementarity to the Hsp90 active site. Notably, the top ten compounds showed a narrow energy range (–10.691 to –10.004 kcal/mol), reflecting consistently high predicted affinity for the ATP-binding pocket. The LBE value derived from molecular docking is used to gauge binding strength: values more negative than –10.0 kcal/mol are considered strong binders, those between –9.0 and –10.0 kcal/mol are moderate binders, while values less negative than –9.0 kcal/mol typically indicate weak binding affinity [33]. Table 1. Top 10 ranked indole alkaloid–based molecules docked into the Hsp90 ATP-binding site, with their predicted docking scores, molecular weights, and SMILES strings. Rank PubChem CID Docking Score (kcal/mol) SMILES 1 4200841 -10.691 C1OC2=C(O1)C=C(C=C2)C(C3=CNC4=CC=CC=C43)C5=CNC6=CC=CC=C65 2 2940578 -10.119 C1(C2=CC=CC=C2NC=1)C(C1C=CC(=C(OC)C=1)OCC(O)=O)C1C2C=CC=CC=2NC=1 3 24718647 -10.109 C12=CC(CNC(CC(C3C=CC4OCOC=4C=3)C3C4C=CC=CC=4NC=3)=O)=CC=C1OCO2 4 2924030 -10.038 C1(C2=CC=CC=C2NC=1)C(C1C=CC=C(C)C=1O)C1C2C=CC=CC=2NC=1 5 2940798 -10.015 C1(C2=CC=CC=C2NC=1)C(C1C=CC=C(C=1)N(=O)=O)C1C2C=CC=CC=2NC=1 6 2923908 -10.012 C1(C2=CC=CC=C2NC=1)C(C1C=C(OC)C=CC=1O)C1C2C=CC=CC=2NC=1 7 4292821 -10.010 C1(C2=CC=CC=C2NC=1)C(C1C=CC(=CC=1)C(O)=O)C1C2C=CC=CC=2NC=1 8 2949399 -10.009 C1(C2=CC=CC=C2N(C)C=1)C(C1C=CC(=CC=1)O)C1C2C=CC=CC=2N(C)C=1 9 2923935 -10.007 C1(C2=CC=CC=C2NC=1)C(C1C=CC=C(OCC)C=1O)C1C2C=CC=CC=2NC=1 10 2949436 -10.004 C1(C2=CC=CC=C2N(C)C=1)C(C1C=CC=NC=1)C1C2C=CC=CC=2N(C)C=1 All top-ranking hits shared a 3,3′-di(indolyl)methane-like (DIM) scaffold, composed of two indole- or carbazole-type aromatic systems connected by a central carbon atom (Figure 2A). This conjugated π-surface enabled extensive hydrophobic and cation-π interactions, supported by limited hydrogen-bonding capability from the indole NH group (absent in N-methylated analogues). In control docking, ATP adopted the expected binding pose—anchored within the adenine sub-pocket via backbone hydrogen bonds, with its triphosphate group extending toward the solvent-exposed phosphate-binding region (Figure 2B). Similarly, the DIM core aligned within the canonical ATP-binding pocket, overlapping with the adenine-binding region (Figure 2B–D) [34, 35]. The central linking carbon positioned the fused aromatic systems parallel to the pocket’s hydrophobic wall, facilitating multiple interactions with non-polar residues (Figure 2E). Typically, one indole ring occupied the adenine sub-pocket, while the second extended toward the pocket entrance, where substituents such as phenol, methoxy, benzodioxole, or carboxylate groups interacted with polar residues and solvent-exposed features at the rim. Despite the structural diversity at these peripheral positions, docking scores for DIM derivatives remained closely grouped, emphasizing the dominant role of the scaffold’s hydrophobic and cation-π interactions in driving binding. Substituents such as phenol, methoxy, nitro, carboxylic acid, and benzodioxole were all well tolerated, with minimal loss of predicted affinity. Significantly, N-methylation of the indole nitrogen (CIDs 2949399 and 2949436) did not reduce docking scores, indicating that the indole NH is not essential for target engagement. The top-ranked compound (CID 4200841, ΔG = –10.691 kcal/mol) featured a benzodioxole substituent, likely improving shape complementarity and providing additional hydrogen-bond acceptor sites at the binding pocket edge. Similarly, derivatives with polar acidic groups, such as CID 4292821 with a benzoic acid group, maintained strong affinities, possibly through salt-bridge formation—though such groups might affect membrane permeability. Nitro-substituted analogues (e.g., CID 2940798) kept docking performance but raised concerns about mutagenicity and metabolic stability. Flexible substituents, such as the phenoxyethyl linker in CID 2923935, offered no advantage over more rigid analogues, suggesting that the Hsp90 binding pocket prefers compact, rigid aromatic systems. Overall, the SAR from these docking studies highlights the DIM scaffold as a key core for Hsp90 binding, with peripheral modifications acting as modulators of solubility, permeability, and selectivity. The tolerance for both hydrogen-bond donors (phenol) and acceptors (methoxy, pyridine) at the pocket entrance shows that polar interactions can be strategically used to adjust ligand orientation and pharmacokinetics without disrupting core binding. Future optimization should focus on rigid aromatic substituents to enhance shape fit, replace metabolically unstable or toxic groups (e.g., nitro), and incorporate bioisosteres for acidic functionalities—aiming to improve drug-like properties while maintaining high affinity. Docking analysis of Hsp90 NTD showed that indole-alkaloids bind in ways similar to typical ATP-site inhibitors. Most compounds formed consistent hydrogen bonds with Asp93, Gly97, Asn51, and Thr109 (Table 2), which are crucial for anchoring nucleotides in the adenine-binding pocket [36]. Additional stabilizing interactions involved Ser52, Ser50, Thr184, and sometimes backbone contacts with Gly137, while Phe138 was mainly stabilized through aromatic stacking. A preserved hydrophobic cluster—including Leu107, Met98, Ala55, Gly108 (backbone), and Thr109—helped support the indole core and its groups, further strengthening binding stability. Table 2: Key Residues of Hsp90 Involved in Indole-Alkaloid Interactions PubChem CID Interactions H-Bonds Hydrophobic ion-π Ionic 4200841 Thr109, Asn51, Gly97, Asp93, Ser52 Gly97, Gly108, Asn51, Lys58, Ala55, Thr184, Leu107 Lys58 - 2940578 Asp93, Ser52, Gly97, Asn51, Phe138, Gly137 Gly137, Phe138, Asn51, Gly97, Asp93, Se52 - - 24718647 Thr109, Ser50, Lys58, Thr109, Asn51, Asp93, Thr184, Gly135 Asn51, Met98, Gly108, Gly97, Leu107, Ser50, Thr108 - - 2924030 Gly97, Leu107, Thr109, Ser50, Asp93, Gly108 Leu107, Asn51, Asp54, Gly97, Lys58, Thr109, Met98 Lys58 - 2940798 Asn51, Phe138, Thr109, Gly97, Asp93 Met98, Leu107, Thr109, Asn51, Gly97, Asp54 Lys58 - 2923908 Thr109, Asp54, Asn51, Asp93, Gly97, Ala55 Leu107, Gly97, Ala55, Thr184, Gly108, Asp54 Lys58 - 4292821 Thr109, Ser50, Asp54, Ser52, Asp93, Gly97 Asp93, Ser52, Gly97, Thr109, Ala55, Asp54 Lys58 His154 2949399 Gly97, Lys58, Ile96 Gly109, Leu107, Asn51, Lys58, Met98 Lys58 - 2923935 Gly108, Thr109, Asp93, Gly97 Asp54, Leu107, Gly97, Thr109, Phe138 Lys58 - 2949436 Lys58, Thr109, Gly108 Asp54, Phe138, Leu107, Gly108, Asn51 - - A prominent feature across most ligands was the engagement of Lys58 in ion–π interactions with the indole ring, observed in eight of the ten top compounds (Figure 3D–E). This emphasizes the central role of the indole scaffold in stabilizing binding. Two ligands (CIDs 2940578 and 2949436) lacked Lys58-mediated contacts but compensated through extensive hydrogen-bond networks involving Asp93, Gly97, Asn51, and Ser52, highlighting alternative strategies for high-affinity binding. Unique binding features were also observed. CID 4292821 (Rank 7) not only formed typical hydrogen bonds with Thr109, Asp93, Gly97, and Ser52, and hydrophobic contacts with Ala55, Leu107, and Asp93, but also engaged His154 via its carboxylate group (Figure 3F). Since His154 is not commonly involved in ATP-site recognition, this suggests that CID 4292821 adopts a deeper or altered orientation, exploiting an underutilized region of the pocket. This interaction underscores the chemical flexibility of indole-alkaloids and presents opportunities to design derivatives with increased selectivity. Comparisons among ligands revealed distinct binding preferences. CID 24718647 exhibited the broadest network of interactions, engaging eight residues through hydrogen bonds and hydrophobic contacts, indicating a very stable fit. In contrast, CID 2949399 achieved strong affinity despite fewer hydrogen bonds, relying mainly on compact hydrophobic interactions with Leu107, Met98, and Gly109. The top-ranked compound, CID 4200841, combined extensive hydrogen bonding (Asp93, Gly97, Ser52, dioxole oxygens) with hydrophobic contacts (Leu107, Thr184, Gly108, Ala55, Asn51), along with a stabilizing Lys58 ion–π interaction. Similarly, CID 2940578 achieved strong binding through multiple hydrogen bonds (Asp93, Gly97, Ser52, Asn51, Gly137, Phe138) and complementary hydrophobic stabilization. CID 24718647 further demonstrated a rich interaction profile, with its amide group engaging Asn51, Thr109, and Gly135, while its dioxole substituents contacted Lys58, Ser50, and Met98, enhancing stability through both polar and hydrophobic contributions. These findings align with established Hsp90 inhibitors. Classical inhibitors such as geldanamycin and ansamycin derivatives depend on Asp93 and Gly97, while purine-based scaffolds like PU-H71 mimic the adenine ring through interactions with Asp93 and Asn51. Indole-alkaloids not only replicate these canonical interactions but also introduce new features—particularly Lys58-mediated ion–π contacts absent in most purine analogs, and in the case of CID 4292821, a novel ionic interaction with His154. In summary, indole-alkaloids serve as potent ATP-competitive inhibitors of Hsp90 NTD. Their high predicted affinities come from conserved hydrogen bonds (Asp93, Gly97, Asn51, Thr109), hydrophobic stabilization within the Leu107–Met98–Ala55 cluster, and frequent ion–π interactions with Lys58. The unique His154 interaction observed for CID 4292821 broadens the pharmacophoric landscape of the ATP pocket, suggesting that rationally designed indole derivatives could target underexplored residues to achieve enhanced potency and selectivity. 3.3 Physicochemical and Drug-Likeness Properties of Indole-Alkaloids Predicted by SwissADME Physicochemical profiling of the selected indole-alkaloid compounds was conducted using various drug-likeness and lead-likeness criteria (Table 3). The molecular weights (MW) of the compounds ranged from 351.44 to 442.46 Da, all below the 500 Da threshold specified by Lipinski's Ro5 for oral bioavailability [19]. The number of rotatable bonds varied from 3 to 7, within the Veber guideline limit (≤10), which favors molecular flexibility and absorption [37]. HBA ranged from 1 to 5, and HBD from 0 to 3, both within recommended ranges for permeability and oral activity [19, 37]. The topological polar surface area (TPSA) values ranged from 22.75 to 87.87.34 Ų, well below the 140 Ų limit, indicating potential for good membrane permeability and CNS penetration for the lower TPSA compounds (e.g., CID 2949436 with TPSA = 22.75 Ų) [38]. Lipophilicity, measured by consensus Log P, ranged from 3.88 to 4.80, indicating moderate to high hydrophobicity, which may enhance membrane crossing but could affect solubility [39]. The ESOL-predicted solubility (Log S) values (-5.19 to −5.99) showed moderate solubility for all compounds, a common trait of hydrophobic scaffolds [39]. Drug-likeness evaluation confirmed that all compounds adhered to Lipinski's, Ghose's, Veber's, and Egan's rules, with only isolated violations in Ghose's or Egan's parameters for a few (e.g., CID 2940798 and CID 2923935). The Muegge filter flagged most compounds (except CID 24718647 and CID 2949436) with one violation, often related to hydrophobicity [23]. The bioavailability scores were generally high (0.55), with one compound (CID 4292821) scoring 0.85, indicating excellent oral bioavailability prospects [40]. No PAINS (Pan Assay Interference Compounds) or Brenk alerts were detected, suggesting low risk of assay interference or chemical reactivity issues. However, all compounds exhibited two lead-likeness violations related to molecular weight, many above 350, and increased hydrophobicity, which could hinder further optimization for smaller fragment-like leads [37]. Overall, these ADME and drug-likeness profiles suggest the investigated indole-alkaloids have favorable physicochemical properties for oral drug development [41, 42]. They show moderate lipophilicity, acceptable solubility, high compliance with medicinal chemistry filters, and no major structural alerts. Along with their predicted activity against Hsp90 NTD and promising cytotoxicity profiles, these features make them strong candidates for further refinement. Table 3. Physicochemical and Drug-Likeness Properties of Indole-Alkaloids Predicted by SwissADME PubChem CID MW (g/mol) #RB HBA HBD TPSA LogP LogS Solubility Lipinski Violations Ghose Violations Veber Violations Egan Violations Muegge Violations Bioavailability Score PAINS Alerts Lead likeness Violations 4200841 366.41 3 2 2 50.04 4.67 -5.92 Moderately soluble 0 0 0 0 1 0.55 0 2 2940578 426.46 7 4 3 87.34 4.2 -5.78 Moderately soluble 0 0 0 0 1 0.56 0 2 24718647 442.46 7 5 2 81.81 3.88 -5.19 Moderately soluble 0 0 0 0 0 0.55 0 2 2924030 352.43 3 1 3 51.81 4.78 -5.99 Moderately soluble 0 1 0 0 1 0.55 0 2 2940798 367.4 4 2 2 77.4 4.27 -5.88 Moderately soluble 0 1 0 1 1 0.55 0 2 2923908 368.43 4 2 3 61.04 4.39 -5.75 Moderately soluble 0 0 0 0 1 0.55 0 2 4292821 366.41 4 2 3 68.88 4.37 -5.68 Moderately soluble 0 0 0 0 1 0.85 0 2 2949399 366.45 3 1 1 30.09 4.5 -5.76 Moderately soluble 0 0 0 0 1 0.55 0 2 2923935 382.45 5 2 3 61.04 4.8 -5.98 Moderately soluble 0 1 0 1 1 0.55 0 2 2949436 351.44 3 1 0 22.75 4.21 -5.24 Moderately soluble 0 0 0 0 0 0.55 0 2 3.4 Cytotoxicity Prediction Analysis of Screened Indole-Alkaloids CLC-Pred 2.0 was used to predict the cytotoxic potential of selected indole-alkaloid compounds against various human cancer cell lines [24]. The analysis revealed a range of predicted activities (Pa) and probabilities of inactivity (Pi), highlighting several compounds with strong potential anticancer effects (Table 4 and Figure 4). A compound was considered potentially active when Pa > 0.5 and higher than the corresponding Pi value. Compound CID 4200841 showed the highest predicted activity toward Hs 683 oligodendroglioma cells (Pa = 0.721, Pi = 0.008), suggesting strong anti-brain cancer potential. It also showed high predicted cytotoxicity against lung carcinoma cell lines DMS-114 (Pa = 0.570) and NCI-H460 (Pa = 0.545), alongside moderate activity for breast carcinoma MCF-7 (Pa = 0.544) and ovarian adenocarcinoma OVCAR-5 (Pa = 0.506). CID 2940578 showed a relatively selective effect on MCF-7 breast carcinoma (Pa = 0.582), while CID 24718647 exhibited moderate activity against NCI-H460 (Pa = 0.415). CID 2924030 demonstrated vigorous predicted activity on Hs 683 oligodendroglioma (Pa = 0.608) and moderate potential for NCI-H460 (Pa = 0.513). CID 2940798 showed moderate cytotoxicity toward NCI-H460 (Pa = 0.501). CID 2923908 had activity against both Hs 683 (Pa = 0.583) and NCI-H460 (Pa = 0.563), with additional potential against MCF-7 (Pa = 0.536). CID 4292821 also favored NCI-H460 (Pa = 0.543) and showed moderate activity for MCF-7 (Pa = 0.507). CID 2949399 emerged as a strong candidate for lung cancer cytotoxicity with the highest activity against NCI-H460 (Pa = 0.722, Pi = 0.005) and moderate activity for ovarian adenocarcinoma OVCAR-5 (Pa = 0.523) and MCF-7 (Pa = 0.509). CID 2923935 showed moderate potential against Hs 683 (Pa = 0.503). Notably, CID 2949436 was the most potent compound in the dataset, showing exceptional predicted activity toward NCI-H460 non-small cell lung carcinoma (Pa = 0.906, Pi = 0.004), PANC-1 pancreatic carcinoma (Pa = 0.710), ACHN papillary renal carcinoma (Pa = 0.704), and HCT-116 colon carcinoma (Pa = 0.616). Overall, the predicted profiles suggest that several indole-alkaloids, especially CID 2949436, 2949399, and 4200841, have significant anticancer potential with multiple targets across different cancer types. The frequent high activity against lung carcinoma cell lines (NCI-H460, DMS-114, A549) indicates possible structural features within these compounds that confer selectivity for pathways essential to lung cancer cell survival. The broad-spectrum activity of CID 2949436, covering lung, pancreas, kidney, and colon cancers, points to its potential as a lead scaffold for developing multi-cancer therapeutic agents. However, this widespread activity also requires careful assessment of off-target effects. These in silico results provide valuable leads for experimental validation, highlighting CID 2949436 for its remarkable potency, along with CID 2949399 and CID 4200841 as promising candidates for further in vitro and in vivo studies targeting Hsp90 NTD-related anticancer mechanisms. Table 4. Predicted cytotoxicity of Indole-alkaloids by CLC-Pred 2.0 Compound CID Pa Pi Cell-line Cell-line name Tissue/organ 4200841 0.721 0.008 Hs 683 Oligodendroglioma Brain 0.570 0.015 DMS-114 Lung carcinoma Lung 0.545 0.015 NCI-H460 Non-small cell lung carcinoma Lung 0.544 0.043 MCF 7.00 Breast carcinoma Breast 0.540 0.052 A549 Lung carcinoma Lung 0.506 0.021 OVCAR-5 Ovarian adenocarcinoma Ovarium 2940578 0.582 0.035 MCF 7.00 Breast carcinoma Breast 24718647 0.415 0.032 NCI-H460 Non-small cell lung carcinoma Lung 2924030 0.608 0.024 Hs 683 Oligodendroglioma Brain 0.513 0.018 NCI-H460 Non-small cell lung carcinoma Lung 2940798 0.501 0.020 NCI-H460 Non-small cell lung carcinoma Lung 2923908 0.583 0.030 Hs 683 Oligodendroglioma Brain 0.563 0.013 NCI-H460 Non-small cell lung carcinoma Lung 0.536 0.044 MCF 7.00 Breast carcinoma Breast 4292821 0.543 0.015 NCI-H460 Non-small cell lung carcinoma Lung 0.507 0.051 MCF 7.00 Breast carcinoma Breast 2949399 0.722 0.005 NCI-H460 Non-small cell lung carcinoma Lung 0.523 0.020 OVCAR-5 Ovarian adenocarcinoma Ovarium 0.510 0.034 DMS-114 Lung carcinoma Lung 0.509 0.050 MCF 7.00 Breast carcinoma Breast 2923935 0.503 0.057 Hs 683 Oligodendroglioma Brain 2949436 0.906 0.004 NCI-H460 Non-small cell lung carcinoma Lung 0.710 0.003 PANC-1 Pancreatic carcinoma Pancreas 0.704 0.004 ACHN Papillary renal carcinoma Kidney 0.616 0.015 HCT-116 Colon carcinoma Colon 3.5 Predicted Protein Targets and Biological Relevance of Indole Alkaloids In addition to primary target prediction, in silico analysis was performed using PASS Targets to evaluate potential off-target interactions of the screened indole-alkaloid derivatives [25]. The predicted probabilities of activity (Pa) were used to assess the likelihood of compound–target associations, with Pa values of ≥ 0.70 considered high confidence, 0.50–0.69 as moderate confidence, and < 0.50 as low confidence [25]. Overall, the off-target prediction profile revealed a predominant association with serine/threonine kinase families, suggesting that structural features of the tested compounds may favor interactions with ATP-binding sites common to multiple kinases (Table 5 and Figure 5). Several compounds also showed predicted activity toward phosphoinositide kinases, GPCRs, and metabolic enzymes. Among the tested molecules, CID 4200841 exhibited a high-confidence off-target prediction for CaM kinase IV (Pa = 0.7070), along with moderate-confidence associations for dual specificity protein kinase CLK2 (Pa = 0.5677), endothelin receptor ET-A (Pa = 0.5642), serine/threonine-protein kinase PLK3 (Pa = 0.5608), and phosphatidylinositol-4-phosphate 3-kinase C2 domain-containing subunit gamma (Pa = 0.5287). The ET-A receptor interaction, in particular, could have cardiovascular implications if confirmed experimentally. CID 2949436 demonstrated the most extensive high-confidence kinase off-target profile, including Nek3 kinase (Pa = 0.8471) and PFTAIRE-2 kinase (Pa = 0.7630), as well as moderate-confidence predictions for phosphatidylinositol-4-phosphate 5-kinase type-1 gamma (Pa = 0.6357), PFTAIRE-1 kinase (Pa = 0.6200), and CaM kinase IV (Pa = 0.5117). This profile suggests a potential for multi-kinase activity that warrants in vitro selectivity evaluation. CID 2949399 also ranked highly for phosphatidylinositol-4-phosphate 5-kinase type-1 gamma (Pa = 0.7177) and Nek3 kinase (Pa = 0.6143), with additional predicted interactions involving rhodopsin kinase and homeodomain-interacting protein kinase 3. Meanwhile, CID 2940798 showed moderate predictions for MAP kinase ERK1 (Pa = 0.6042), microtubule-associated protein tau (Pa = 0.5441), cytochrome P450 2J2 (Pa = 0.5255), and CaM kinase IV (Pa = 0.5183), indicating possible effects on both cancer-related signaling and metabolic pathways. Several other compounds, including CIDs 2924030, 4292821, and 2923935, demonstrated moderate-confidence hits for CaM kinase IV, ERK1, and various mitotic kinases (e.g., NEK6, STK38-like, and BRK). In contrast, CIDs 2940578 and 2923908 exhibited only low-confidence predictions (Pa ~0.34–0.50), suggesting minimal off-target liabilities in the PASS model. The recurrence of CaM kinase IV as a predicted off-target across multiple scaffolds suggests specific structural motifs within the indole-alkaloid derivatives may mimic known ligands of this calcium/calmodulin-dependent kinase. Additionally, the frequent prediction of phosphoinositide kinases and NEK family members reflects a possible overlap in binding site compatibility with the ATP-binding region of Hsp90 NTD. While this could indicate polypharmacological potential, it also raises the need for kinase selectivity profiling during lead optimization. From a drug development perspective, the high Pa predictions for cancer-relevant kinases (e.g., ERK1, PLK3, Nek3) may offer opportunities for dual-targeting strategies, potentially enhancing anticancer efficacy. However, off-targets such as ET-A and CYP2J2 highlight the importance of early safety assessments to mitigate potential cardiovascular or metabolic adverse effects. Taken together, these findings underscore the necessity of integrating off-target prediction into the early stages of Hsp90 inhibitor development. The PASS-derived predictions provide a prioritized list of targets for follow-up validation using in vitro kinase assays, binding studies, and docking simulations. In particular, compounds such as CIDs 2949436, 2949399, and 4200841 merit focused investigation to confirm their selectivity profiles while harnessing their anticancer potential. Table 5. Off-Target Predictions for Indole-Alkaloids Using PASS Targets Prediction Software Compound CID Target name Confidence 4200841 CaM kinase IV 0.7070 Dual specificity protein kinase CLK2 0.5677 Endothelin receptor ET-A 0.5642 Serine/threonine-protein kinase PLK3 0.5608 Phosphatidylinositol-4-phosphate 3-kinase C2 domain-containing subunit gamma 0.5287 2940578 Serine/threonine-protein kinase OSR1 0.3435 Hematopoietic cell protein-tyrosine phosphatase 70Z-PEP 0.3432 24718647 Eukaryotic translation initiation factor 4H 0.5656 Polyadenylate-binding protein 1 0.5152 2924030 CaM kinase IV 0.5506 MAP kinase ERK1 0.5225 2940798 MAP kinase ERK1 0.6042 Microtubule-associated protein tau 0.5441 Cytochrome P450 2J2 0.5255 CaM kinase IV 0.5183 2923908 Serine/threonine-protein kinase OSR1 0.5022 4292821 CaM kinase IV 0.6184 2949399 Phosphatidylinositol-4-phosphate 5-kinase type-1 gamma 0.7177 Serine/threonine-protein kinase Nek3 0.6143 Rhodopsin kinase 0.5496 Homeodomain-interacting protein kinase 3 0.5398 cAMP-dependent protein kinase beta-1 catalytic subunit 0.5281 Serine/threonine-protein kinase PFTAIRE-2 0.5063 2923935 Serine/threonine-protein kinase NEK6 0.5625 Serine/threonine-protein kinase 38-like 0.5366 Tyrosine-protein kinase BRK 0.5178 2949436 Serine/threonine-protein kinase Nek3 0.8471 Serine/threonine-protein kinase PFTAIRE-2 0.7630 Phosphatidylinositol-4-phosphate 5-kinase type-1 gamma 0.6357 Serine/threonine-protein kinase PFTAIRE-1 0.6200 Testis-specific serine/threonine-protein kinase 1 0.5867 Serine/threonine-protein kinase tousled-like 1 0.5339 Homeodomain-interacting protein kinase 3 0.5335 cAMP-dependent protein kinase beta-1 catalytic subunit 0.5295 CaM kinase IV 0.5117 Conclusion This study highlights the important role of computer-aided drug discovery in identifying indole alkaloids as promising Hsp90 ATPase inhibitors. Using molecular docking and in silico profiling, these compounds showed strong binding affinities (–10.004 to –10.691 kcal/mol) and key interactions with catalytic residues Asp93, Lys58, Gly97, and Thr184. Computational ADME and toxicity predictions further confirmed their favorable drug-like properties, including compliance with medicinal chemistry filters, good solubility, moderate lipophilicity, high oral bioavailability, and no structural alerts. Notably, off-target interactions with kinases such as CaM kinase IV and Nek kinases indicate potential polypharmacological anticancer activity, though they also suggest a need to improve selectivity. Overall, this research demonstrates how computational methods can speed up the discovery of new chemotypes and help identify promising drug candidates. Supported by these in silico findings, indole alkaloids emerge as strong leads for developing Hsp90-targeted anticancer therapies, emphasizing the need for further mechanistic research and preclinical testing. References Jackson SE (2012) Hsp90: Structure and Function. pp 155–240 Wei H, Zhang Y, Jia Y, Chen X, Niu T, Chatterjee A, He P, Hou G (2024) Heat shock protein 90: biological functions, diseases, and therapeutic targets. MedComm (Beijing). https://doi.org/10.1002/mco2.470 Ciocca DR, Calderwood SK (2005) Heat shock proteins in cancer: diagnostic, prognostic, predictive, and treatment implications. Cell Stress Chaperones 10:86 Barrott JJ, Haystead TAJ (2013) Hsp90, an unlikely ally in the war on cancer. FEBS J 280:1381–1396 Keramisanou D, Aboalroub A, Zhang Z, Liu W, Marshall D, Diviney A, Larsen RW, Landgraf R, Gelis I (2016) Molecular Mechanism of Protein Kinase Recognition and Sorting by the Hsp90 Kinome-Specific Cochaperone Cdc37. 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J Comput Chem 30:2785–2791 Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera—A visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612 Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717 Lagunin AA, Rudik A V., Pogodin P V., et al (2023) CLC-Pred 2.0: A Freely Available Web Application for In Silico Prediction of Human Cell Line Cytotoxicity and Molecular Mechanisms of Action for Druglike Compounds. Int J Mol Sci 24:1689 Lagunin A, Stepanchikova A, Filimonov D, Poroikov V (2000) PASS: prediction of activity spectra for biologically active substances. Bioinformatics 16:747–748 Li H, Sun X, Cui W, et al (2024) Computational drug development for membrane protein targets. Nat Biotechnol 42:229–242 Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 152:9–20 Brogi S, Ramalho TC, Kuca K, Medina-Franco JL, Valko M (2020) Editorial: In silico Methods for Drug Design and Discovery. Front Chem. https://doi.org/10.3389/fchem.2020.00612 Avendaño C, Menéndez JC (2023) Miscellaneous small- molecule and biological approaches to targeted cancer therapy. In: Medicinal Chemistry of Anticancer Drugs. Elsevier, pp 743–822 Röhrig UF, Goullieux M, Bugnon M, Zoete V (2023) Attracting Cavities 2.0: Improving the Flexibility and Robustness for Small-Molecule Docking. J Chem Inf Model 63:3925–3940 Gelis I, Keramisanou D, Aboalroub A (2017) Protein Kinase Recognition and Sorting by the HSP90 Kinome-Specific Cochaperone CDC37. Biophys J 112:491a Kumar MV V, Ebna Noor R, Davis RE, Zhang Z, Sipavicius E, Keramisanou D, Blagg BSJ, Gelis I (2018) Molecular insights into the interaction of Hsp90 with allosteric inhibitors targeting the C-terminal domain. Medchemcomm 9:1323–1331 Reifs A, Fernandez-Calvo A, Alonso-Lerma B, et al (2024) High-throughput virtual search of small molecules for controlling the mechanical stability of human CD4. Journal of Biological Chemistry 300:107133 Eccles SA, Massey A, Raynaud FI, et al (2008) NVP-AUY922: A Novel Heat Shock Protein 90 Inhibitor Active against Xenograft Tumor Growth, Angiogenesis, and Metastasis. Cancer Res 68:2850–2860 Magwenyane AM, Lawal MM, Amoako DG, Somboro AM, Agoni C, Khan RB, Mhlongo NdumisoN, Kumalo HM (2022) Exploring the inhibitory mechanism of resorcinylic isoxazole amine NVP-AUY922 towards the discovery of potential heat shock protein 90 (Hsp90) inhibitors. Sci Afr 15:e01107 Pearl LH (2016) Review: The HSP90 molecular chaperone—an enigmatic ATPase. Biopolymers 105:594–607 Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD (2002) Molecular Properties That Influence the Oral Bioavailability of Drug Candidates. J Med Chem 45:2615–2623 Shityakov S, Neuhaus W, Dandekar T, Förster C (2013) Analysing molecular polar surface descriptors to predict blood-brain barrier permeation. Int J Comput Biol Drug Des 6:146 Morak-Mlodawska B, Jelen M, Martula E, Korlacki R (2023) Study of Lipophilicity and ADME Properties of 1,9-Diazaphenothiazines with Anticancer Action. Int J Mol Sci 24:6970 Martin YC (2005) A Bioavailability Score. J Med Chem 48:3164–3170 Shin HK, Kang Y-M, No KT (2016) Predicting ADME Properties of Chemicals. In: Handbook of Computational Chemistry. Springer Netherlands, Dordrecht, pp 1–37 Shen J, Cheng F, Xu Y, Li W, Tang Y (2010) Estimation of ADME Properties with Substructure Pattern Recognition. J Chem Inf Model 50:1034–1041 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7419782","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509354405,"identity":"de284ae0-d2b7-4ae2-b4d8-0076a60c844a","order_by":0,"name":"Adam A. Aboalroub","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYFCCBAYGxgYGfn4wu4AELZIzG0BsA1K0bDgA4hCjhb89+ekGxh21EsbnVyd+eGDAIM8vdgC/Fokzz8xuMJ45LmF24+1mCaDDDGfOTiBgzY0EoJa2Y3VmN85uAGlJMLhNQIv8jfRvIC0SxjPObv5BlBaDGzkgW2okDPh7txFni+GZN2U3EtsOSEjc4N1mkWAgQdgvcsfTt9342FYnwd9/dvPNHxU28vzSBLSAQQLDYWDYgVVKEKEcAuqAMXSAaNWjYBSMglEwwgAAZR5K60McW9kAAAAASUVORK5CYII=","orcid":"","institution":"Al-Ahliyya Amman University","correspondingAuthor":true,"prefix":"","firstName":"Adam","middleName":"A.","lastName":"Aboalroub","suffix":""}],"badges":[],"createdAt":"2025-08-20 17:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7419782/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7419782/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90748774,"identity":"411e7a91-5e0d-4626-a42e-e588e8432c6f","added_by":"auto","created_at":"2025-09-07 09:07:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":211272,"visible":true,"origin":"","legend":"\u003cp\u003eChemical structures of the top 10 ranked indole alkaloid–based molecules docked into the Hsp90 ATP-binding site.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7419782/v1/3f7cc8e7885b3c0dcfef12bd.png"},{"id":90748705,"identity":"c6348543-6e5d-426d-a9a2-44089f879b60","added_by":"auto","created_at":"2025-09-07 08:59:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":602435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural and docking analysis of the 3,3′-di(indolyl)methane (DIM) scaffold in the Hsp90 N-terminal ATP-binding pocket. (A) General chemotype of identified hits, featuring a DIM-like scaffold. (B) ATP docking shows the adenine ring in the sub-pocket via hydrogen bonds (green), with the triphosphate moiety facing the solvent-exposed region. (C–D) Docked DIM occupies the same N-terminal ATP-binding pocket as ATP, with the aromatic core overlapping the adenine-binding region. (E) The quaternary carbon center orients the polyaromatic surface parallel to the hydrophobic wall of the pocket, enabling extensive contacts with surrounding residues.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7419782/v1/cfea746bbd773341aa51b0aa.png"},{"id":90748707,"identity":"f9c8d6a3-7275-4ea8-99bb-a35d52f6fc67","added_by":"auto","created_at":"2025-09-07 08:59:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":735766,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInteraction profile of the top-ranked indole alkaloids with Hsp90, showing hydrogen bonds (green) and hydrophobic contacts (red) within the ATP-binding pocket.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7419782/v1/d0e34c7890bede079d9a8922.png"},{"id":90748709,"identity":"b2cb4933-48ea-46d0-ac9a-0d0359f9b04d","added_by":"auto","created_at":"2025-09-07 08:59:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":175406,"visible":true,"origin":"","legend":"\u003cp\u003eThe heatmap of \u003cem\u003ein silico\u003c/em\u003e cytotoxicity prediction of indole-alkaloids against cancer cell lines.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7419782/v1/1f28c4cbd6c7bbd95c4820df.png"},{"id":90748708,"identity":"eff03627-e458-41f8-b50b-97aa5136caf0","added_by":"auto","created_at":"2025-09-07 08:59:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":323104,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted Off-Target Protein Interactions of Indole-Alkaloids Based on PASS Target Prediction\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7419782/v1/5de8432e4791dfad7d45324a.png"},{"id":90749010,"identity":"0232c0ea-2cee-4257-b751-7f00e0640119","added_by":"auto","created_at":"2025-09-07 09:15:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3305520,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7419782/v1/14ecc301-c05a-4919-91ee-85ecdbe5df49.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computational Identification of Indole Alkaloids as Novel Hsp90 ATPase Inhibitors with Anticancer Potential","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeat shock protein 90 (Hsp90) is a highly conserved, ATP-dependent molecular chaperone essential for the folding, maturation, stabilization, and activation of various client proteins [1]. These include kinases, transcription factors, steroid hormone receptors, and signaling molecules, all vital for maintaining cellular homeostasis, growth, and survival [2]. Generally, Hsp90 supports protein balance by aiding proper folding of its clients and preventing aggregation during cellular stress like heat shock, oxidative stress, and hypoxia [1]. Beyond normal functions, misregulation of Hsp90 is linked to many diseases [2\u0026ndash;4]. In cancer, it is often overproduced in an active form that binds and stabilizes oncogenic proteins such as HER2, AKT, RAF, and mutant p53 [4, 5]. This stabilization promotes cancerous behaviors by supporting cell growth signals, inhibiting cell death, encouraging blood vessel formation, and enabling metastasis [1, 4]. Hsp90 also plays a role in neurodegenerative diseases such as Alzheimer\u0026rsquo;s, Parkinson\u0026rsquo;s, and Huntington\u0026rsquo;s by affecting the fate of misfolded and aggregating proteins [6, 7]. Moreover, certain pathogens hijack Hsp90 to fold their virulence factors, highlighting its importance as a therapeutic target in both cancer and infectious diseases [8]. The justification for targeting Hsp90 in therapy rests on its critical role in maintaining the function of many oncogenic proteins. Blocking Hsp90 interrupts its chaperone cycle, leading to the degradation of client proteins via ubiquitin\u0026ndash;proteasome pathways, which in turn disables multiple cancer-driving pathways simultaneously [9]. This broad action provides an advantage over kinase inhibitors that target only a single signaling pathway [4, 9, 10]. Most small-molecule Hsp90 inhibitors work by competitively binding to the N-terminal domain (NTD), which contains a highly conserved ATP-binding site crucial for chaperone activity [11\u0026ndash;13]. Although first-generation inhibitors like geldanamycin derivatives show promising anticancer effects, their clinical use is restricted by toxicity at high doses, poor solubility, and limited bioavailability [14]. Therefore, ongoing research is essential to identify new chemical structures that can effectively inhibit Hsp90 with better pharmacokinetic properties and safety.\u003c/p\u003e\n\u003cp\u003eIndole alkaloids are a vast and chemically diverse group of natural products containing an indole moiety\u0026mdash;a bicyclic structure made of a benzene ring fused to a pyrrole ring [15]. They originate biosynthetically from tryptophan through various enzymatic processes, leading to a wide array of chemical structures [16]. These alkaloids are found across multiple biological kingdoms, including plants, marine organisms, fungi, and bacteria. Indole alkaloids possess a wide range of biological activities, making them highly valuable in medicinal chemistry as pharmacophores [15, 16]. Many key drugs and lead compounds are derivatives of indole alkaloids, highlighting their therapeutic importance [15]. Examples include vincristine and vinblastine (microtubule inhibitors for cancer treatment), camptothecin derivatives (topoisomerase I inhibitors), and reserpine (used for hypertension and psychosis) [17]. Their bioactivities span anticancer, antibacterial, antiviral, antifungal, antiparasitic, anti-inflammatory, and neuroactive effects [15\u0026ndash;17]. In cancer therapy, indole alkaloids often work by disrupting essential cellular processes like microtubule assembly, DNA replication, and the cell cycle [16\u0026ndash;18]. Their high affinity for various biological targets stems from the indole ring\u0026rsquo;s ability to engage in \u0026pi;\u0026ndash;\u0026pi; stacking, hydrogen bonds, and hydrophobic interactions, allowing recognition by numerous enzymes and receptors [16]. As pharmacophores, they exhibit remarkable structural flexibility, enabling modifications to improve potency, selectivity, and pharmacokinetic profiles [15\u0026ndash;18]. The planar aromatic system and electron-rich nitrogen help interactions with both hydrophobic and polar regions of target proteins [15, 16]. This versatility has led to extensive exploration in structure-based drug design, such as kinase inhibitors, protease inhibitors, and chaperone modulators [17]. Overall, their adaptability makes indole alkaloids promising candidates for targeting Hsp90, where rational modifications of the indole core and its substituents can optimize engagement of the ATP-binding site.\u003c/p\u003e\n\u003cp\u003eIn this study, we used a comprehensive computational approach to explore indole alkaloid\u0026ndash;based molecules as potential Hsp90 inhibitors. We conducted molecular docking, ADMET predictions, and off-target and cytotoxicity predictions to evaluate their binding strength, pharmacokinetic features, and stability within the Hsp90 binding site. This integrated strategy aims to identify promising indole alkaloid frameworks that could be further developed into effective and selective anticancer agents targeting Hsp90. Numerous indole-alkaloids were identified from various chemical compound databases as potential inhibitors of Hsp90 ATPase activity. These compounds show promising drug-like characteristics, favorable pharmacokinetic profiles, and cytotoxic effects. Their strong binding affinities, between -10.004 and -10.691 kcal/mol, indicate their potential for further development. Examination of their binding interactions highlights key hydrogen bonds and hydrophobic interactions with essential catalytic residues, such as Lys58, Gly97, and Thr184, supporting their role as Hsp90 inhibitors. Furthermore, these indole-alkaloids showed strong multi-cancer cytotoxicity, highlighting their potential as lead anticancer candidates. Collectively, these results suggest that indole-alkaloids could constitute a new chemotype for Hsp90-targeted cancer treatments. Additional mechanistic studies and preclinical testing are necessary to move these compounds toward clinical use.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Ligand Library Preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA targeted library of indole alkaloid\u0026ndash;based molecules was assembled by gathering compounds from various public chemical and natural product databases, including ZINC (https://zinc.docking.org), Mcule (https://mcule.com), LOTUS Natural Products Database, PubChem (https://pubchem.ncbi.nlm.nih.gov), and relevant scientific literature. Selection criteria prioritized maximizing structural diversity and key pharmacophoric features. This meant focusing on compounds with an indole core and potential substituents that could engage in different interactions within the Hsp90 binding site. The initial compound set was curated to remove duplicates, inorganic species, and molecules over 500 Da. Additionally, the library was filtered using Lipinski\u0026rsquo;s Rule of Five (Ro5), which states that an orally active drug generally has a molecular weight of 500 Da or less, a calculated LogP value not greater than 5, no more than five hydrogen bond donors (HBD), and no more than ten hydrogen bond acceptors (HBA) [19]. Compounds that violated more than one of these parameters were excluded, as such deviations are often linked to poor pharmacokinetic behavior, including limited absorption, distribution, metabolism, and excretion (ADME). This filtering step was essential to increase the chance of identifying bioavailable and pharmacologically active molecules suitable for further development. The shortlisted compounds were then converted into their SMILES (Simplified Molecular Input Line Entry System) representations, which served as input for subsequent computational analyses in the virtual screening workflow.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Molecular Docking\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe N-terminal domain of human Hsp90 in complex with a co-crystallized inhibitor (PDB ID: 6LTI, 1.59 \u0026Aring; resolution) was obtained from the RCSB Protein Data Bank (https://www.rcsb.org) [20]. Protein preparation was carried out using AutoDock Tools (1.5.7) by removing water molecules, ions, and non-essential heteroatoms, and extracting the co-crystallized ligand to define the binding site [21]. Missing hydrogens were added, polar hydrogens included for hydrogen-bond calculations, and Gasteiger charges assigned; non-polar hydrogens were merged with their parent carbons. A grid box with a volume of 10 \u0026Aring;\u0026sup3; and a default spacing of 0.375 \u0026Aring; was generated, centered at coordinates x = 33.00, y = -14.00, and z = -20.00, to define the docking search space. The actual docking simulations of the selected indole-alkaloids were performed using AutoDock Vina (1.2.3) with Sampling exhaustivity of 40. The resulting conformations were analyzed from the AutoDock log files, with emphasis placed on the lowest binding energy (LBE) values to identify the most favorable poses. To enhance reliability, the conformer with the largest cluster size was selected for further analysis. The final chosen conformers were exported and visualized using ChimeraX-1.8 [22].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3 In silico\u003c/em\u003e prediction of physicochemical properties, drug-likeness, and lead-likeness for the top-ranked molecules\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe physicochemical properties, drug-likeness, and lead-likeness profiles of the top-ranked indole alkaloid molecules identified from docking studies were evaluated using SwissADME (http://www.swissadme.ch/) [23]. Compounds were first screened according to Lipinski\u0026rsquo;s Rule of 5. Additional drug- and lead-likeness assessments were performed by applying medicinal chemistry filters, including the Veber (polar surface area \u0026le; 140 \u0026Aring;\u0026sup2; and \u0026le; 10 rotatable bonds), Ghose (molecular weight 160\u0026ndash;480 Da, logP \u0026minus;0.4 to +5.6, molar refractivity 40\u0026ndash;130), and Egan (logP \u0026le; 5.88 and polar surface area \u0026le; 131 \u0026Aring;\u0026sup2;) rules, thereby providing a comprehensive evaluation of the compounds\u0026rsquo; oral bioavailability and suitability as potential leads.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e2.4 In Silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;Cytotoxicity Prediction in Tumor Cell Lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvaluating cytotoxicity in tumor cell lines is crucial in anticancer drug development, as it helps assess both therapeutic effectiveness and safety. Computational methods for predicting cytotoxicity provide a quick and cost-efficient alternative to laboratory testing, reducing resource use during early candidate screening. In this study, the cytotoxic potential of top-ranked indole alkaloid\u0026ndash;based molecules was evaluated using CLC-Pred 2.0 (Cell Line Cytotoxicity Predictor), a web platform for \u003cem\u003ein silico\u003c/em\u003e prediction of activity against cancerous and non-cancerous cell lines (https://www.way2drug.com/Cell-line) [24]. CLC-Pred 2.0 employs QSAR models trained on extensive experimental cytotoxicity datasets, predicting activity by correlating the compound\u0026apos;s structural features with known bioactive molecules in its database. For each indole alkaloid derivative, the SMILES notation was generated and submitted to the CLC-Pred server. The results indicated the probability of cytotoxic activity across various tumor cell lines, aiding in the identification of candidates with the highest predicted anticancer potential for further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Off-Target Prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the potential off-target effects of the top-ranked indole alkaloid compounds, an \u003cem\u003ein silico\u003c/em\u003e screening was conducted using the Way2Drug PASS (Prediction of Activity Spectra for Substances) platform (http://www.way2drug.com/PASS) [25]. This tool predicts a wide range of biological activities, including both desired pharmacological effects and possible adverse or toxicological outcomes, based on structural similarity to known bioactive compounds. Each prediction is given as the probability of activity (Pa) versus inactivity (Pi), with higher Pa values indicating a greater likelihood that the compound will exhibit the predicted activity. For this study, activities with Pa \u0026gt; 0.7 were considered highly probable, while those with 0.5 \u0026lt; Pa \u0026lt; 0.7 were regarded as moderately probable. This method enabled the identification of potential off-target interactions, offering insights into the safety profile and pharmacological significance of the selected indole alkaloids.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eComputer-aided drug discovery (CADD) is a rapidly advancing field that helps identify and analyze molecules with desired biological effects [11, 13, 26\u0026ndash;28]. Recent advances in computational chemistry and machine learning have significantly improved the accuracy of predicting and ranking chemical compounds for specific biological functions. In this study, we used \u003cem\u003ein silico\u003c/em\u003e methods to find indole alkaloid\u0026ndash;based molecules capable of inhibiting Hsp90 ATPase activity. Using bioactive small molecules to influence protein function is a promising approach for developing targeted anticancer therapies. Compared to larger therapeutics like monoclonal antibodies and polypeptides, small molecules offer advantages such as lower manufacturing costs, oral bioavailability, improved patient compliance, and favorable pharmacokinetics [29, 30]. They can also target various proteins, including kinases, proteasomes, and chaperones like Hsp90 [10, 31, 32]. This research employed virtual screening and molecular docking to identify diverse indole alkaloid scaffolds with high binding affinity and firm interaction profiles at the Hsp90 ATP-binding site. These computational findings serve as a valuable initial step in drug discovery, highlighting promising candidates for biological activity. However, confirming the therapeutic potential of these compounds will require further validation through in vitro biochemical tests, cell-based cytotoxicity assessments, and detailed pharmacokinetic and pharmacodynamic studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Ligand Library Preparation and Shortlisting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA diverse library of indole alkaloid\u0026ndash;based molecules was compiled from public databases such as ZINC, Mcule, LOTUS, and PubChem, along with relevant literature. The selection prioritized maintaining the indole core while adding substituents capable of hydrogen bonding, hydrophobic interactions, and ion\u0026ndash;\u0026pi; stacking with Hsp90. The dataset was curated to eliminate duplicates, inorganic compounds, and molecules larger than 500 Da, ensuring drug-like properties. After initial structural optimization, a filtering process based on Lipinski\u0026rsquo;s Ro5 was applied to favor bioavailable, pharmacologically relevant Hsp90 inhibitors. Compounds were evaluated for molecular weight (\u0026le;500 Da), cLogP (\u0026le;5), hydrogen bond donors (\u0026le;5), and acceptors (\u0026le;10). Those exceeding these thresholds were excluded, mainly due to high lipophilicity or molecular weight, reducing the initial 1,240 compounds to 812 (a 34.5% reduction). This improved dataset suited molecular docking, focusing on scaffolds with balanced physicochemical properties to enhance binding potential and ADME profiles. The filtered compounds were converted into SMILES format for subsequent computational analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Evaluation of Indole Alkaloid\u0026ndash;Based Molecules as Potential Hsp90 Inhibitors via Molecular Docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter assembling the indole alkaloid library, compounds that did not meet Lipinski\u0026rsquo;s Rule of Five were removed, and the remaining molecules underwent molecular docking against the Hsp90 N-terminal domain (PDB ID: 6LTI). Binding affinities were estimated as docking scores (\u0026Delta;G, kcal/mol), and compounds were ranked based on these scores, with more negative values indicating stronger predicted binding. As shown in Table 1 and depicted in Figure 1, a subset of ligands had docking scores of \u0026le; \u0026ndash;10.0 kcal/mol, indicating strong binding potential and good complementarity to the Hsp90 active site. Notably, the top ten compounds showed a narrow energy range (\u0026ndash;10.691 to \u0026ndash;10.004 kcal/mol), reflecting consistently high predicted affinity for the ATP-binding pocket. The LBE value derived from molecular docking is used to gauge binding strength: values more negative than \u0026ndash;10.0 kcal/mol are considered strong binders, those between \u0026ndash;9.0 and \u0026ndash;10.0 kcal/mol are moderate binders, while values less negative than \u0026ndash;9.0 kcal/mol typically indicate weak binding affinity [33].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Top 10 ranked indole alkaloid\u0026ndash;based molecules docked into the Hsp90 ATP-binding site, with their predicted docking scores, molecular weights, and SMILES strings.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.59509%;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3497%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePubChem CID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3374%;\"\u003e\n \u003cp\u003eDocking Score (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7178%;\"\u003e\n \u003cp\u003eSMILES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.59509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3497%;\"\u003e\n \u003cp\u003e4200841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3374%;\"\u003e\n \u003cp\u003e-10.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7178%;\"\u003e\n \u003cp\u003eC1OC2=C(O1)C=C(C=C2)C(C3=CNC4=CC=CC=C43)C5=CNC6=CC=CC=C65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.59509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3497%;\"\u003e\n \u003cp\u003e2940578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3374%;\"\u003e\n \u003cp\u003e-10.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7178%;\"\u003e\n \u003cp\u003eC1(C2=CC=CC=C2NC=1)C(C1C=CC(=C(OC)C=1)OCC(O)=O)C1C2C=CC=CC=2NC=1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.59509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3497%;\"\u003e\n \u003cp\u003e24718647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3374%;\"\u003e\n \u003cp\u003e-10.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7178%;\"\u003e\n \u003cp\u003eC12=CC(CNC(CC(C3C=CC4OCOC=4C=3)C3C4C=CC=CC=4NC=3)=O)=CC=C1OCO2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.59509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3497%;\"\u003e\n \u003cp\u003e2924030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3374%;\"\u003e\n \u003cp\u003e-10.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66.7178%;\"\u003e\n \u003cp\u003eC1(C2=CC=CC=C2NC=1)C(C1C=CC=C(C)C=1O)C1C2C=CC=CC=2NC=1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.59509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3497%;\"\u003e\n \u003cp\u003e2940798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3374%;\"\u003e\n \u003cp\u003e-10.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66.7178%;\"\u003e\n \u003cp\u003eC1(C2=CC=CC=C2NC=1)C(C1C=CC=C(C=1)N(=O)=O)C1C2C=CC=CC=2NC=1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.59509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3497%;\"\u003e\n \u003cp\u003e2923908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3374%;\"\u003e\n \u003cp\u003e-10.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66.7178%;\"\u003e\n \u003cp\u003eC1(C2=CC=CC=C2NC=1)C(C1C=C(OC)C=CC=1O)C1C2C=CC=CC=2NC=1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.59509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.3497%;\"\u003e\n \u003cp\u003e4292821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3374%;\"\u003e\n \u003cp\u003e-10.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66.7178%;\"\u003e\n \u003cp\u003eC1(C2=CC=CC=C2NC=1)C(C1C=CC(=CC=1)C(O)=O)C1C2C=CC=CC=2NC=1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.59509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.3497%;\"\u003e\n \u003cp\u003e2949399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3374%;\"\u003e\n \u003cp\u003e-10.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66.7178%;\"\u003e\n \u003cp\u003eC1(C2=CC=CC=C2N(C)C=1)C(C1C=CC(=CC=1)O)C1C2C=CC=CC=2N(C)C=1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.59509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.3497%;\"\u003e\n \u003cp\u003e2923935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3374%;\"\u003e\n \u003cp\u003e-10.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66.7178%;\"\u003e\n \u003cp\u003eC1(C2=CC=CC=C2NC=1)C(C1C=CC=C(OCC)C=1O)C1C2C=CC=CC=2NC=1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.59509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.3497%;\"\u003e\n \u003cp\u003e2949436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3374%;\"\u003e\n \u003cp\u003e-10.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7178%;\"\u003e\n \u003cp\u003eC1(C2=CC=CC=C2N(C)C=1)C(C1C=CC=NC=1)C1C2C=CC=CC=2N(C)C=1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAll top-ranking hits shared a 3,3\u0026prime;-di(indolyl)methane-like (DIM) scaffold, composed of two indole- or carbazole-type aromatic systems connected by a central carbon atom (Figure 2A). This conjugated \u0026pi;-surface enabled extensive hydrophobic and cation-\u0026pi; interactions, supported by limited hydrogen-bonding capability from the indole NH group (absent in N-methylated analogues). In control docking, ATP adopted the expected binding pose\u0026mdash;anchored within the adenine sub-pocket via backbone hydrogen bonds, with its triphosphate group extending toward the solvent-exposed phosphate-binding region (Figure 2B). Similarly, the DIM core aligned within the canonical ATP-binding pocket, overlapping with the adenine-binding region (Figure 2B\u0026ndash;D) [34, 35]. The central linking carbon positioned the fused aromatic systems parallel to the pocket\u0026rsquo;s hydrophobic wall, facilitating multiple interactions with non-polar residues (Figure 2E). Typically, one indole ring occupied the adenine sub-pocket, while the second extended toward the pocket entrance, where substituents such as phenol, methoxy, benzodioxole, or carboxylate groups interacted with polar residues and solvent-exposed features at the rim.\u003c/p\u003e\n\u003cp\u003eDespite the structural diversity at these peripheral positions, docking scores for DIM derivatives remained closely grouped, emphasizing the dominant role of the scaffold\u0026rsquo;s hydrophobic and cation-\u0026pi; interactions in driving binding. Substituents such as phenol, methoxy, nitro, carboxylic acid, and benzodioxole were all well tolerated, with minimal loss of predicted affinity. Significantly, N-methylation of the indole nitrogen (CIDs 2949399 and 2949436) did not reduce docking scores, indicating that the indole NH is not essential for target engagement. The top-ranked compound (CID 4200841, \u0026Delta;G = \u0026ndash;10.691 kcal/mol) featured a benzodioxole substituent, likely improving shape complementarity and providing additional hydrogen-bond acceptor sites at the binding pocket edge. Similarly, derivatives with polar acidic groups, such as CID 4292821 with a benzoic acid group, maintained strong affinities, possibly through salt-bridge formation\u0026mdash;though such groups might affect membrane permeability. Nitro-substituted analogues (e.g., CID 2940798) kept docking performance but raised concerns about mutagenicity and metabolic stability. Flexible substituents, such as the phenoxyethyl linker in CID 2923935, offered no advantage over more rigid analogues, suggesting that the Hsp90 binding pocket prefers compact, rigid aromatic systems. Overall, the SAR from these docking studies highlights the DIM scaffold as a key core for Hsp90 binding, with peripheral modifications acting as modulators of solubility, permeability, and selectivity. The tolerance for both hydrogen-bond donors (phenol) and acceptors (methoxy, pyridine) at the pocket entrance shows that polar interactions can be strategically used to adjust ligand orientation and pharmacokinetics without disrupting core binding. Future optimization should focus on rigid aromatic substituents to enhance shape fit, replace metabolically unstable or toxic groups (e.g., nitro), and incorporate bioisosteres for acidic functionalities\u0026mdash;aiming to improve drug-like properties while maintaining high affinity.\u003c/p\u003e\n\u003cp\u003eDocking analysis of Hsp90 NTD showed that indole-alkaloids bind in ways similar to typical ATP-site inhibitors. Most compounds formed consistent hydrogen bonds with Asp93, Gly97, Asn51, and Thr109 (Table 2), which are crucial for anchoring nucleotides in the adenine-binding pocket [36]. Additional stabilizing interactions involved Ser52, Ser50, Thr184, and sometimes backbone contacts with Gly137, while Phe138 was mainly stabilized through aromatic stacking. A preserved hydrophobic cluster\u0026mdash;including Leu107, Met98, Ala55, Gly108 (backbone), and Thr109\u0026mdash;helped support the indole core and its groups, further strengthening binding stability.\u003c/p\u003e\n\u003cp\u003eTable 2: Key Residues of Hsp90 Involved in Indole-Alkaloid Interactions\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePubChem CID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 510px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteractions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eH-Bonds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eHydrophobic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eion-\u0026pi;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eIonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4200841\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eThr109, Asn51, Gly97, Asp93, Ser52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eGly97, Gly108, Asn51, Lys58, Ala55, Thr184, Leu107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eLys58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2940578\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAsp93, Ser52, Gly97, Asn51, Phe138, Gly137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eGly137, Phe138, Asn51, Gly97, Asp93, Se52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24718647\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eThr109, Ser50, Lys58, Thr109, Asn51, Asp93, Thr184, Gly135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eAsn51, Met98, Gly108, Gly97, Leu107, Ser50, Thr108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2924030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eGly97, Leu107, Thr109, Ser50, Asp93, Gly108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eLeu107, Asn51, Asp54, Gly97, Lys58, Thr109, Met98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eLys58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2940798\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAsn51, Phe138, Thr109, Gly97, Asp93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eMet98, Leu107, Thr109, Asn51, Gly97, Asp54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eLys58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2923908\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eThr109, Asp54, Asn51, Asp93, Gly97, Ala55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eLeu107, Gly97, Ala55, Thr184, Gly108, Asp54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eLys58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4292821\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eThr109, Ser50, Asp54, Ser52, Asp93, Gly97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eAsp93, Ser52, Gly97, Thr109, Ala55, Asp54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eLys58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eHis154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2949399\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eGly97, Lys58, Ile96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eGly109, Leu107, Asn51, Lys58, Met98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eLys58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2923935\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eGly108, Thr109, Asp93, Gly97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eAsp54, Leu107, Gly97, Thr109, Phe138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eLys58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2949436\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eLys58, Thr109, Gly108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eAsp54, Phe138, Leu107, Gly108, Asn51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA prominent feature across most ligands was the engagement of Lys58 in ion\u0026ndash;\u0026pi; interactions with the indole ring, observed in eight of the ten top compounds (Figure 3D\u0026ndash;E). This emphasizes the central role of the indole scaffold in stabilizing binding. Two ligands (CIDs 2940578 and 2949436) lacked Lys58-mediated contacts but compensated through extensive hydrogen-bond networks involving Asp93, Gly97, Asn51, and Ser52, highlighting alternative strategies for high-affinity binding. Unique binding features were also observed. CID 4292821 (Rank 7) not only formed typical hydrogen bonds with Thr109, Asp93, Gly97, and Ser52, and hydrophobic contacts with Ala55, Leu107, and Asp93, but also engaged His154 via its carboxylate group (Figure 3F). Since His154 is not commonly involved in ATP-site recognition, this suggests that CID 4292821 adopts a deeper or altered orientation, exploiting an underutilized region of the pocket. This interaction underscores the chemical flexibility of indole-alkaloids and presents opportunities to design derivatives with increased selectivity. Comparisons among ligands revealed distinct binding preferences. CID 24718647 exhibited the broadest network of interactions, engaging eight residues through hydrogen bonds and hydrophobic contacts, indicating a very stable fit. In contrast, CID 2949399 achieved strong affinity despite fewer hydrogen bonds, relying mainly on compact hydrophobic interactions with Leu107, Met98, and Gly109. The top-ranked compound, CID 4200841, combined extensive hydrogen bonding (Asp93, Gly97, Ser52, dioxole oxygens) with hydrophobic contacts (Leu107, Thr184, Gly108, Ala55, Asn51), along with a stabilizing Lys58 ion\u0026ndash;\u0026pi; interaction. Similarly, CID 2940578 achieved strong binding through multiple hydrogen bonds (Asp93, Gly97, Ser52, Asn51, Gly137, Phe138) and complementary hydrophobic stabilization. CID 24718647 further demonstrated a rich interaction profile, with its amide group engaging Asn51, Thr109, and Gly135, while its dioxole substituents contacted Lys58, Ser50, and Met98, enhancing stability through both polar and hydrophobic contributions. These findings align with established Hsp90 inhibitors. Classical inhibitors such as geldanamycin and ansamycin derivatives depend on Asp93 and Gly97, while purine-based scaffolds like PU-H71 mimic the adenine ring through interactions with Asp93 and Asn51. Indole-alkaloids not only replicate these canonical interactions but also introduce new features\u0026mdash;particularly Lys58-mediated ion\u0026ndash;\u0026pi; contacts absent in most purine analogs, and in the case of CID 4292821, a novel ionic interaction with His154. In summary, indole-alkaloids serve as potent ATP-competitive inhibitors of Hsp90 NTD. Their high predicted affinities come from conserved hydrogen bonds (Asp93, Gly97, Asn51, Thr109), hydrophobic stabilization within the Leu107\u0026ndash;Met98\u0026ndash;Ala55 cluster, and frequent ion\u0026ndash;\u0026pi; interactions with Lys58. The unique His154 interaction observed for CID 4292821 broadens the pharmacophoric landscape of the ATP pocket, suggesting that rationally designed indole derivatives could target underexplored residues to achieve enhanced potency and selectivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Physicochemical and Drug-Likeness Properties of Indole-Alkaloids Predicted by SwissADME\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhysicochemical profiling of the selected indole-alkaloid compounds was conducted using various drug-likeness and lead-likeness criteria (Table 3). The molecular weights (MW) of the compounds ranged from 351.44 to 442.46 Da, all below the 500 Da threshold specified by Lipinski\u0026apos;s Ro5 for oral bioavailability [19]. The number of rotatable bonds varied from 3 to 7, within the Veber guideline limit (\u0026le;10), which favors molecular flexibility and absorption [37]. HBA ranged from 1 to 5, and HBD from 0 to 3, both within recommended ranges for permeability and oral activity [19, 37]. The topological polar surface area (TPSA) values ranged from 22.75 to 87.87.34 \u0026Aring;\u0026sup2;, well below the 140 \u0026Aring;\u0026sup2; limit, indicating potential for good membrane permeability and CNS penetration for the lower TPSA compounds (e.g., CID 2949436 with TPSA = 22.75 \u0026Aring;\u0026sup2;) [38]. Lipophilicity, measured by consensus Log P, ranged from 3.88 to 4.80, indicating moderate to high hydrophobicity, which may enhance membrane crossing but could affect solubility [39]. The ESOL-predicted solubility (Log S) values (-5.19 to \u0026minus;5.99) showed moderate solubility for all compounds, a common trait of hydrophobic scaffolds [39]. Drug-likeness evaluation confirmed that all compounds adhered to Lipinski\u0026apos;s, Ghose\u0026apos;s, Veber\u0026apos;s, and Egan\u0026apos;s rules, with only isolated violations in Ghose\u0026apos;s or Egan\u0026apos;s parameters for a few (e.g., CID 2940798 and CID 2923935). The Muegge filter flagged most compounds (except CID 24718647 and CID 2949436) with one violation, often related to hydrophobicity [23]. The bioavailability scores were generally high (0.55), with one compound (CID 4292821) scoring 0.85, indicating excellent oral bioavailability prospects [40]. No PAINS (Pan Assay Interference Compounds) or Brenk alerts were detected, suggesting low risk of assay interference or chemical reactivity issues. However, all compounds exhibited two lead-likeness violations related to molecular weight, many above 350, and increased hydrophobicity, which could hinder further optimization for smaller fragment-like leads [37]. Overall, these ADME and drug-likeness profiles suggest the investigated indole-alkaloids have favorable physicochemical properties for oral drug development [41, 42]. They show moderate lipophilicity, acceptable solubility, high compliance with medicinal chemistry filters, and no major structural alerts. Along with their predicted activity against Hsp90 NTD and promising cytotoxicity profiles, these features make them strong candidates for further refinement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Physicochemical and Drug-Likeness Properties of Indole-Alkaloids Predicted by SwissADME\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"960\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePubChem CID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMW (g/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#RB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTPSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eSolubility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLipinski Violations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGhose Violations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVeber Violations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEgan Violations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuegge Violations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBioavailability Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAINS Alerts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLead likeness Violations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4200841\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e366.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e50.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2940578\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e426.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e87.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24718647\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e442.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e81.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-5.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2924030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e352.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e51.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-5.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2940798\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e367.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e77.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2923908\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e368.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e61.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-5.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4292821\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e366.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e68.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2949399\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e366.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e30.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-5.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2923935\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e382.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e61.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2949436\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e351.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e22.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Cytotoxicity Prediction Analysis of Screened Indole-Alkaloids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCLC-Pred 2.0 was used to predict the cytotoxic potential of selected indole-alkaloid compounds against various human cancer cell lines [24]. The analysis revealed a range of predicted activities (Pa) and probabilities of inactivity (Pi), highlighting several compounds with strong potential anticancer effects (Table 4 and Figure 4). A compound was considered potentially active when Pa \u0026gt; 0.5 and higher than the corresponding Pi value. Compound CID 4200841 showed the highest predicted activity toward Hs 683 oligodendroglioma cells (Pa = 0.721, Pi = 0.008), suggesting strong anti-brain cancer potential. It also showed high predicted cytotoxicity against lung carcinoma cell lines DMS-114 (Pa = 0.570) and NCI-H460 (Pa = 0.545), alongside moderate activity for breast carcinoma MCF-7 (Pa = 0.544) and ovarian adenocarcinoma OVCAR-5 (Pa = 0.506). CID 2940578 showed a relatively selective effect on MCF-7 breast carcinoma (Pa = 0.582), while CID 24718647 exhibited moderate activity against NCI-H460 (Pa = 0.415). CID 2924030 demonstrated vigorous predicted activity on Hs 683 oligodendroglioma (Pa = 0.608) and moderate potential for NCI-H460 (Pa = 0.513). CID 2940798 showed moderate cytotoxicity toward NCI-H460 (Pa = 0.501). CID 2923908 had activity against both Hs 683 (Pa = 0.583) and NCI-H460 (Pa = 0.563), with additional potential against MCF-7 (Pa = 0.536). CID 4292821 also favored NCI-H460 (Pa = 0.543) and showed moderate activity for MCF-7 (Pa = 0.507). CID 2949399 emerged as a strong candidate for lung cancer cytotoxicity with the highest activity against NCI-H460 (Pa = 0.722, Pi = 0.005) and moderate activity for ovarian adenocarcinoma OVCAR-5 (Pa = 0.523) and MCF-7 (Pa = 0.509). CID 2923935 showed moderate potential against Hs 683 (Pa = 0.503). Notably, CID 2949436 was the most potent compound in the dataset, showing exceptional predicted activity toward NCI-H460 non-small cell lung carcinoma (Pa = 0.906, Pi = 0.004), PANC-1 pancreatic carcinoma (Pa = 0.710), ACHN papillary renal carcinoma (Pa = 0.704), and HCT-116 colon carcinoma (Pa = 0.616). Overall, the predicted profiles suggest that several indole-alkaloids, especially CID 2949436, 2949399, and 4200841, have significant anticancer potential with multiple targets across different cancer types. The frequent high activity against lung carcinoma cell lines (NCI-H460, DMS-114, A549) indicates possible structural features within these compounds that confer selectivity for pathways essential to lung cancer cell survival. The broad-spectrum activity of CID 2949436, covering lung, pancreas, kidney, and colon cancers, points to its potential as a lead scaffold for developing multi-cancer therapeutic agents. However, this widespread activity also requires careful assessment of off-target effects. These \u003cem\u003ein silico\u003c/em\u003e results provide valuable leads for experimental validation, highlighting CID 2949436 for its remarkable potency, along with CID 2949399 and CID 4200841 as promising candidates for further in vitro and in vivo studies targeting Hsp90 NTD-related anticancer mechanisms.\u003c/p\u003e\n\u003cp\u003eTable 4. Predicted cytotoxicity of Indole-alkaloids by CLC-Pred 2.0\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompound CID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell-line\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell-line name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTissue/organ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4200841\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eHs 683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eOligodendroglioma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eBrain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eDMS-114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eLung carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNCI-H460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eNon-small cell lung carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMCF\u0026nbsp;7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eBreast carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eA549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eLung carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eOVCAR-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eOvarian adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eOvarium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2940578\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMCF\u0026nbsp;7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eBreast carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24718647\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNCI-H460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eNon-small cell lung carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2924030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eHs 683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eOligodendroglioma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eBrain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNCI-H460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eNon-small cell lung carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2940798\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNCI-H460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eNon-small cell lung carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2923908\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eHs 683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eOligodendroglioma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eBrain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNCI-H460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eNon-small cell lung carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMCF\u0026nbsp;7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eBreast carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4292821\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNCI-H460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eNon-small cell lung carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMCF\u0026nbsp;7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eBreast carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2949399\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNCI-H460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eNon-small cell lung carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eOVCAR-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eOvarian adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eOvarium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eDMS-114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eLung carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMCF\u0026nbsp;7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eBreast carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2923935\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eHs 683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eOligodendroglioma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eBrain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2949436\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNCI-H460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eNon-small cell lung carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003ePANC-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003ePancreatic carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003ePancreas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eACHN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003ePapillary renal carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eKidney\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eHCT-116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eColon carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eColon\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Predicted Protein Targets and Biological Relevance of Indole Alkaloids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to primary target prediction, \u003cem\u003ein silico\u003c/em\u003e analysis was performed using PASS Targets to evaluate potential off-target interactions of the screened indole-alkaloid derivatives [25]. The predicted probabilities of activity (Pa) were used to assess the likelihood of compound\u0026ndash;target associations, with Pa values of \u0026ge; 0.70 considered high confidence, 0.50\u0026ndash;0.69 as moderate confidence, and \u0026lt; 0.50 as low confidence [25]. Overall, the off-target prediction profile revealed a predominant association with serine/threonine kinase families, suggesting that structural features of the tested compounds may favor interactions with ATP-binding sites common to multiple kinases (Table 5 and Figure 5). Several compounds also showed predicted activity toward phosphoinositide kinases, GPCRs, and metabolic enzymes. Among the tested molecules, CID 4200841 exhibited a high-confidence off-target prediction for CaM kinase IV (Pa = 0.7070), along with moderate-confidence associations for dual specificity protein kinase CLK2 (Pa = 0.5677), endothelin receptor ET-A (Pa = 0.5642), serine/threonine-protein kinase PLK3 (Pa = 0.5608), and phosphatidylinositol-4-phosphate 3-kinase C2 domain-containing subunit gamma (Pa = 0.5287). The ET-A receptor interaction, in particular, could have cardiovascular implications if confirmed experimentally. CID 2949436 demonstrated the most extensive high-confidence kinase off-target profile, including Nek3 kinase (Pa = 0.8471) and PFTAIRE-2 kinase (Pa = 0.7630), as well as moderate-confidence predictions for phosphatidylinositol-4-phosphate 5-kinase type-1 gamma (Pa = 0.6357), PFTAIRE-1 kinase (Pa = 0.6200), and CaM kinase IV (Pa = 0.5117). This profile suggests a potential for multi-kinase activity that warrants in vitro selectivity evaluation. CID 2949399 also ranked highly for phosphatidylinositol-4-phosphate 5-kinase type-1 gamma (Pa = 0.7177) and Nek3 kinase (Pa = 0.6143), with additional predicted interactions involving rhodopsin kinase and homeodomain-interacting protein kinase 3. Meanwhile, CID 2940798 showed moderate predictions for MAP kinase ERK1 (Pa = 0.6042), microtubule-associated protein tau (Pa = 0.5441), cytochrome P450 2J2 (Pa = 0.5255), and CaM kinase IV (Pa = 0.5183), indicating possible effects on both cancer-related signaling and metabolic pathways. Several other compounds, including CIDs 2924030, 4292821, and 2923935, demonstrated moderate-confidence hits for CaM kinase IV, ERK1, and various mitotic kinases (e.g., NEK6, STK38-like, and BRK). In contrast, CIDs 2940578 and 2923908 exhibited only low-confidence predictions (Pa ~0.34\u0026ndash;0.50), suggesting minimal off-target liabilities in the PASS model. The recurrence of CaM kinase IV as a predicted off-target across multiple scaffolds suggests specific structural motifs within the indole-alkaloid derivatives may mimic known ligands of this calcium/calmodulin-dependent kinase. Additionally, the frequent prediction of phosphoinositide kinases and NEK family members reflects a possible overlap in binding site compatibility with the ATP-binding region of Hsp90 NTD. While this could indicate polypharmacological potential, it also raises the need for kinase selectivity profiling during lead optimization. From a drug development perspective, the high Pa predictions for cancer-relevant kinases (e.g., ERK1, PLK3, Nek3) may offer opportunities for dual-targeting strategies, potentially enhancing anticancer efficacy. However, off-targets such as ET-A and CYP2J2 highlight the importance of early safety assessments to mitigate potential cardiovascular or metabolic adverse effects. Taken together, these findings underscore the necessity of integrating off-target prediction into the early stages of Hsp90 inhibitor development. The PASS-derived predictions provide a prioritized list of targets for follow-up validation using in vitro kinase assays, binding studies, and docking simulations. In particular, compounds such as CIDs 2949436, 2949399, and 4200841 merit focused investigation to confirm their selectivity profiles while harnessing their anticancer potential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Off-Target Predictions for Indole-Alkaloids Using PASS Targets Prediction Software\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eCompound CID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eTarget name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eConfidence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4200841\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eCaM kinase IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.7070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eDual specificity protein kinase CLK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eEndothelin receptor ET-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase PLK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003ePhosphatidylinositol-4-phosphate 3-kinase C2 domain-containing subunit gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2940578\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase OSR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.3435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eHematopoietic cell protein-tyrosine phosphatase 70Z-PEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.3432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24718647\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eEukaryotic translation initiation factor 4H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003ePolyadenylate-binding protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2924030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eCaM kinase IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eMAP kinase ERK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2940798\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eMAP kinase ERK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.6042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eMicrotubule-associated protein tau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eCytochrome P450 2J2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eCaM kinase IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2923908\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase OSR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4292821\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eCaM kinase IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.6184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2949399\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003ePhosphatidylinositol-4-phosphate 5-kinase type-1 gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.7177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase Nek3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.6143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eRhodopsin kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eHomeodomain-interacting protein kinase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003ecAMP-dependent protein kinase beta-1 catalytic subunit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase PFTAIRE-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2923935\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase NEK6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase 38-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eTyrosine-protein kinase BRK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\" valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2949436\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase Nek3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.8471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase PFTAIRE-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.7630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003ePhosphatidylinositol-4-phosphate 5-kinase type-1 gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.6357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase PFTAIRE-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.6200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eTestis-specific serine/threonine-protein kinase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase tousled-like 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eHomeodomain-interacting protein kinase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003ecAMP-dependent protein kinase beta-1 catalytic subunit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003eCaM kinase IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.5117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the important role of computer-aided drug discovery in identifying indole alkaloids as promising Hsp90 ATPase inhibitors. Using molecular docking and \u003cem\u003ein silico\u003c/em\u003e profiling, these compounds showed strong binding affinities (\u0026ndash;10.004 to \u0026ndash;10.691 kcal/mol) and key interactions with catalytic residues Asp93, Lys58, Gly97, and Thr184. Computational ADME and toxicity predictions further confirmed their favorable drug-like properties, including compliance with medicinal chemistry filters, good solubility, moderate lipophilicity, high oral bioavailability, and no structural alerts. Notably, off-target interactions with kinases such as CaM kinase IV and Nek kinases indicate potential polypharmacological anticancer activity, though they also suggest a need to improve selectivity. Overall, this research demonstrates how computational methods can speed up the discovery of new chemotypes and help identify promising drug candidates. Supported by these \u003cem\u003ein silico\u003c/em\u003e findings, indole alkaloids emerge as strong leads for developing Hsp90-targeted anticancer therapies, emphasizing the need for further mechanistic research and preclinical testing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJackson SE (2012) Hsp90: Structure and Function. pp 155\u0026ndash;240\u003c/li\u003e\n\u003cli\u003eWei H, Zhang Y, Jia Y, Chen X, Niu T, Chatterjee A, He P, Hou G (2024) Heat shock protein 90: biological functions, diseases, and therapeutic targets. MedComm (Beijing). https://doi.org/10.1002/mco2.470\u003c/li\u003e\n\u003cli\u003eCiocca DR, Calderwood SK (2005) Heat shock proteins in cancer: diagnostic, prognostic, predictive, and treatment implications. Cell Stress Chaperones 10:86\u003c/li\u003e\n\u003cli\u003eBarrott JJ, Haystead TAJ (2013) Hsp90, an unlikely ally in the war on cancer. FEBS J 280:1381\u0026ndash;1396\u003c/li\u003e\n\u003cli\u003eKeramisanou D, Aboalroub A, Zhang Z, Liu W, Marshall D, Diviney A, Larsen RW, Landgraf R, Gelis I (2016) Molecular Mechanism of Protein Kinase Recognition and Sorting by the Hsp90 Kinome-Specific Cochaperone Cdc37. Mol Cell 62:260\u0026ndash;271\u003c/li\u003e\n\u003cli\u003eAboalroub AA (2025) Pathogenic Proteins Through the Lens of NMR Spectroscopy: Structural and Functional Insights into Disease. Cell Biochem Biophys. https://doi.org/10.1007/s12013-025-01869-1\u003c/li\u003e\n\u003cli\u003eBohush A, Bieganowski P, Filipek A (2019) Hsp90 and Its Co-Chaperones in Neurodegenerative Diseases. Int J Mol Sci 20:4976\u003c/li\u003e\n\u003cli\u003eRobbins N, Cowen LE (2023) Roles of Hsp90 in Candida albicans morphogenesis and virulence. Curr Opin Microbiol 75:102351\u003c/li\u003e\n\u003cli\u003ePoyya J, Joshi CG (2024) Inhibition of the HSP90 homodimerization and HSP90-HIF1\u0026alpha; interactions by employing small molecules at C-terminal ATP binding site of HSP90. https://doi.org/10.1101/2024.06.02.595921\u003c/li\u003e\n\u003cli\u003eKeramisanou D, Vasantha Kumar MV, Boose N, Abzalimov RR, Gelis I (2022) Assembly mechanism of early Hsp90-Cdc37-kinase complexes. Sci Adv. https://doi.org/10.1126/sciadv.abm9294\u003c/li\u003e\n\u003cli\u003eAboalroub A (2025) In Silico Identification of Spirodioxynaphthalenes as Promising Hsp90 Inhibitors. https://doi.org/10.21203/rs.3.rs-6199117/v1\u003c/li\u003e\n\u003cli\u003eAboalroub AA (2025) Virtual Screening and Molecular Docking Characterization of Isoxazole-based Small Molecules as Potential Hsp90 Inhibitors: An in Silico Insight. Medinformatics. https://doi.org/10.47852/bonviewMEDIN52025019\u003c/li\u003e\n\u003cli\u003eAboalroub AA, Al-Najjar BO (2024) In-silico identification of 3,4-Diarylpyrazoles-based small molecules as potential Hsp90 inhibitors. Results Chem 101757\u003c/li\u003e\n\u003cli\u003eKitson RRA, Kitsonov\u0026aacute; D, Siegel D, Ross D, Moody CJ (2024) Geldanamycin, a Naturally Occurring Inhibitor of Hsp90 and a Lead Compound for Medicinal Chemistry. J Med Chem 67:17946\u0026ndash;17963\u003c/li\u003e\n\u003cli\u003eRosales PF, Bordin GS, Gower AE, Moura S (2020) Indole alkaloids: 2012 until now, highlighting the new chemical structures and biological activities. 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Int J Comput Biol Drug Des 6:146\u003c/li\u003e\n\u003cli\u003eMorak-Mlodawska B, Jelen M, Martula E, Korlacki R (2023) Study of Lipophilicity and ADME Properties of 1,9-Diazaphenothiazines with Anticancer Action. Int J Mol Sci 24:6970\u003c/li\u003e\n\u003cli\u003eMartin YC (2005) A Bioavailability Score. J Med Chem 48:3164\u0026ndash;3170\u003c/li\u003e\n\u003cli\u003eShin HK, Kang Y-M, No KT (2016) Predicting ADME Properties of Chemicals. In: Handbook of Computational Chemistry. Springer Netherlands, Dordrecht, pp 1\u0026ndash;37\u003c/li\u003e\n\u003cli\u003eShen J, Cheng F, Xu Y, Li W, Tang Y (2010) Estimation of ADME Properties with Substructure Pattern Recognition. J Chem Inf Model 50:1034\u0026ndash;1041\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hsp90 inhibitors, Indole alkaloids, Molecular docking, Anticancer drug discovery, ATPase activity","lastPublishedDoi":"10.21203/rs.3.rs-7419782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7419782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The ATPase activity of heat shock protein 90 (Hsp90) is crucial for stabilizing and regulating many oncogenic client proteins, thereby supporting cancer progression and tumor cell survival. Although several small-molecule inhibitors have demonstrated preclinical promise, their clinical use remains limited due to toxicity and moderate effectiveness, highlighting the need for new chemotypes with better therapeutic profiles. Indole alkaloids, a diverse group of natural compounds with wide-ranging biological activities—including anticancer, antimicrobial, and enzyme-inhibition effects—were explored here as potential Hsp90 ATPase inhibitors through an extensive computer-based approach. Molecular docking of natural-product derivatives showed strong binding affinities (–10.004 to –10.691 kcal/mol), favorable pharmacokinetic and toxicity predictions, and key interactions with catalytic residues Asp93, Lys58, Gly97, and Thr184. Physicochemical and ADME profiling further validated favorable drug-like properties, including adherence to key medicinal chemistry filters, acceptable solubility, moderate lipophilicity, high oral bioavailability, and no structural alerts. Several indole-alkaloid derivatives also exhibited off-target interactions with several kinases, indicating potential for polypharmacological anticancer effects but emphasizing the importance of selectivity profiling. Overall, this research presents indole alkaloids as promising Hsp90-targeted anticancer candidates. Additional mechanistic studies and preclinical validation are necessary to advance these compounds toward clinical development.","manuscriptTitle":"Computational Identification of Indole Alkaloids as Novel Hsp90 ATPase Inhibitors with Anticancer Potential","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-07 08:59:29","doi":"10.21203/rs.3.rs-7419782/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff659624-5087-4063-9e36-2ed113dd4079","owner":[],"postedDate":"September 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-07T08:59:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-07 08:59:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7419782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7419782","identity":"rs-7419782","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}