Dual Targeting of AChE Inhibition and GPX4 Activation by Plant-Derived Compounds for the Treatment of Alzheimer’s Disease: Insights from Molecular Docking and Molecular Dynamics Simulations

Dual Targeting of AChE Inhibition and GPX4 Activation by Plant-Derived Compounds for the Treatment of Alzheimer’s Disease: Insights from Molecular Docking and Molecular Dynamics Simulations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dual Targeting of AChE Inhibition and GPX4 Activation by Plant-Derived Compounds for the Treatment of Alzheimer’s Disease: Insights from Molecular Docking and Molecular Dynamics Simulations Suheda Rumeysa Osmanlioglu Dag, Mehmet Abdullah Alagoz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8748563/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 Background/Objectives: Alzheimer’s disease (AD) is primarily characterized by cholinergic dysfunction, for which acetylcholinesterase (AChE) inhibition remains the mainstay of symptomatic treatment. However, additional hypotheses such as ferroptosis—an iron-dependent form of regulated cell death—have gained prominence in explaining disease progression. Glutathione peroxidase 4 (GPX4), a critical antioxidant enzyme, plays a protective role by suppressing ferroptotic pathways. In this context, identifying phytochemicals capable of simultaneously inhibiting AChE and activating GPX4 may provide a dual therapeutic benefit. This study aimed to identify such dual-acting compounds through a structure-based virtual screening approach. Methods : A total of 3,014 natural compounds were collected from three curated databases: NPACT, HIT, and HIM. Molecular docking was performed against GPX4 (7U4I) and AChE (7D9Q). Compounds demonstrating high affinity for both targets were shortlisted. Z-score normalization and statistical ranking were used to select the best two dual-target compounds. Results : Out of 3,014 compounds, 68 showed dual-binding potential. Among these, NPACT00189 (docking scores: −6.720 kcal/mol for GPX4; −8.983 kcal/mol for AChE) and NPACT01210 (docking scores: −5.813 kcal/mol for GPX4; −9.640 kcal/mol for AChE) were identified as top candidates based on docking scores. Molecular dynamics (MD) simulations were conducted for both compounds for 250 ns on the AChE binding site and the allosteric site of GPX4. The results indicated that NPACT00189 maintained stable interactions throughout the simulation period at both targets, indicating its dual-targeting potential. Conclusions : NPACT00189 represents promising dual-target for further investigation in AD therapy. Its potential requires confirmation through comprehensive in vitro and in vivo studies. Biological sciences/Biochemistry Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Biological sciences/Neuroscience Alzheimer disease’s AChE ferroptosis GPX4 in silico molecular docking molecular dynamics simulations Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by progressive cognitive impairment, memory loss, and neuronal death. Despite significant research efforts, the precise etiology of AD remains elusive, with multiple intersecting pathogenic pathways contributing to its onset and progression [ 1 , 2 ]. Among the earliest and most consistently observed neurochemical abnormalities in AD is the dysfunction of the cholinergic system, particularly the reduction of acetylcholine (ACh) levels in the brain. Cholinesterase inhibitors (ChEIs) such as donepezil, galantamine, and rivastigmine aim to counteract this deficit by inhibiting acetylcholinesterase (AChE), thereby increasing synaptic ACh concentrations. However, these treatments offer only symptomatic relief and do not halt disease progression [ 3 , 4 ]. Therefore, the pursuit of new therapeutic leads is essential for developing effective disease-modifying agents capable of preventing or arresting disease development. Bioactive phytochemicals have long served as a crucial resource in the drug discovery pipeline, providing candidates with limited systemic toxicity. This low-toxicity profile is critical, as it has the potential to enhance compliance during the necessary prolonged administration required for chronic conditions [ 5 , 6 ]. Ferroptosis, a regulated, iron-dependent form of non-apoptotic cell death characterized by the accumulation of lipid peroxides, has emerged as a potential mechanism underlying neuronal degeneration in AD [ 7 , 3 ]. Unlike classical necrosis or apoptosis, ferroptosis is driven by disruptions in iron homeostasis, glutathione depletion, and impaired activity of glutathione peroxidase 4 (GPX4), a key antioxidant enzyme responsible for reducing lipid hydroperoxides [ 8 ]. Several studies have shown that GPX4 plays a neuroprotective role by suppressing ferroptotic pathways. Its inhibition or downregulation sensitizes neurons to oxidative damage and ferroptotic cell death, while its activation has been associated with mitigation of AD-like pathology [ 9 , 10 ]. In fact, the depletion of GPX4 has been shown to result in age-dependent neurodegeneration and cognitive deficits in murine models, highlighting its central role in neuronal survival [ 7 , 3 ]. Notably, ferroptosis and cholinergic dysfunction may converge mechanistically in AD. Iron overload in cholinergic neurons may exacerbate oxidative damage, potentiating AChE activity and disrupting neurotransmission. Furthermore, AChE itself has been implicated in the formation of Aβ fibrils, thus linking cholinergic imbalance with amyloid pathology [ 1 , 3 ]. The co-occurrence of these processes suggests that therapeutic agents capable of modulating both ferroptosis (via GPX4 activation) and cholinergic signaling (via AChE inhibition) could offer dual protective effects (Fig. 1 ). The limitations of the traditional one-drug-single-target approach have become clear, as these agents often exhibit limited efficacy against complex diseases where pathogenesis depends on a set of interconnected biochemical events and multiple bioreceptors operating concomitantly [ 11 ]. Driven by an increased understanding of neurodegenerative disease complexity, drug development has shifted from an initial focus on single targets toward multi-target drug development, treating these diseases as networks of interconnected pathways [ 12 ]. Rationally designed multi-target drugs—also termed multimodal drugs or network therapeutics—have emerged as an attractive drug discovery paradigm, aiming to enhance efficacy or improve safety relative to single-target drugs or combinations thereof [ 13 ]. Consequently, multi-target agents have garnered significant attention as promising tools to combat challenging diseases, marking a critical new focus area in pharmacological research [ 11 ]. Medicinal plants are a source of abundant biological activities highly beneficial to human health, providing various natural remedies in the form of fruits, leaves, bark, and vegetables [ 14 ]. The diverse range of bioactive nutrients and phytochemicals present in these natural products plays a vital role in the prevention and potential management of various neurodegenerative diseases. Consequently, increasing attention is being given to these natural compounds of plant origin for their potent neuroprotective effects against oxidative damage, which may potentially hinder neurodegeneration and enhance memory and cognitive functions [ 15 – 17 ]. In this context, plant-derived natural products with multitarget properties represent promising candidates for AD drug discovery. Several bioactive phytochemicals have demonstrated both AChE inhibitory and antioxidant effects, with recent in silico studies highlighting their potential to interact favorably with GPX4 and AChE simultaneously [ 4 , 9 ] Such dual-action molecules could suppress ferroptotic cell death while enhancing cholinergic neurotransmission, offering a novel strategy for disease modification. This study aims to explore the dual modulation of GPX4 and AChE by plant-derived compounds through molecular docking and molecular dynamics simulations. These insights could inform future in vitro and in vivo investigations and ultimately contribute to the development of more effective multitarget therapies for AD. 2. Results 2.1. Molecular Docking Studies A total of 3,014 plant-based compounds were subjected to molecular docking to evaluate their binding affinities toward GPX4 and AChE. The molecular docking analysis successfully identified 68 compounds as potential dual-target ligands, demonstrating favorable binding free energies against both GPX4 (≤ -3.300 kcal/mol) and AChE (≤ -7.700 kcal/mol) (Table S1 and S2). These threshold values were selected that the statistical distribution of docking scores across the entire compound library and reflect the upper half of the affinity profile for each target. Moreover, the selected cutoffs are consistent with docking score ranges reported for known reference inhibitors of GPX4 and AChE, supporting their pharmacological relevance. This distribution- and reference-guided filtering strategy enabled the selection of a meaningful yet sufficiently broad pool of candidate ligands for subsequent statistical ranking and MD simulations. The compounds generally exhibited higher binding affinity towards AChE. 2.1.1. Statistical Analysis of Molecular Docking Results and MM/GBSA Calculation A total of 68 compounds showing binding affinity toward both GPX4 and AChE were statistically evaluated to identify the most promising dual-target inhibitors. For standardization and comparative assessment, docking scores were normalized using z-score transformation, independently applied to each target. This normalization allowed the generation of a composite binding score by summing the individual Z-scores for GPX4 and AChE, enabling a robust ranking system for dual-binding potential. It is important to note that GPX4-binding compounds are generally considered moderate binders due to the flexible allosteric site, while AChE-binding compounds are expected to exhibit stronger binding affinities owing to the well-defined active-site gorge. Among the screened compounds, NPACT00189 and NPACT01210 emerged as top-ranked candidates (Table 1 ). NPACT00189 and NPACT01210 have better docking scores for GPX4 compared to the reference compound, PKUMDL-LC-102. Furthermore, while the AChE docking score of NPACT00189 was similar to that of the reference inhibitor Donepezil, NPACT01210 achieved an even more favourable docking score than Donepezil. MM/GBSA (Molecular Mechanics / Generalized Born Surface Area) calculations were performed to estimate the free binding energies of the compounds toward their target proteins (Table 1 ). Against GPX4, the MM/GBSA binding free energies of NPACT00189 and NPACT01210 were more negative than those observed for PKUMDL-LC-102, indicating stronger binding affinities. For AChE, the free binding energy of NPACT01210 was slightly more negative than that of Donepezil (Table 1 ). These two compounds were consequently selected for molecular dynamics simulations to evaluate their stability and modes of interaction within the targets’ binding pockets (Fig. 2 ). Table 1 Docking scores and MM/GBSA binding affinities (kcal/mol) of selected compounds against GPX4 and AChE targets. Compounds GPX4 Docking GPX4 MM/GBSA AChE Docking AChE MM/GBSA Z GPX4 Z AChE C score NPACT00189 -6.720 -45.06 -8.983 -66.68 3.6777 0.6734 4.3510 NPACT01210 -5.813 -39.89 -9.640 -92.67 1.5956 1.7828 3.3783 Donepezil - - -9.456 -90.74 - - - PKUMDL-LC-102 -5.671 -18.05 - - - - - 2.1.2. Interactions of the compounds at the allosteric site of GPX4 The allosteric site of GPX4 is characterized by two basic residues (Lys31, Lys90), three acidic residues (Asp21, Asp23, Asp101), and seven apolar residues (Ile22, Ala93, Ala94, Val98, Phe100, Met102, Phe103). NPACT00189, NPACT01210, and the reference PKUMDL-LC-102 exhibited similar interaction patterns within this region (Fig. 3 ). NPACT00189 engaged in several key interactions via its -OH functional groups, forming hydrogen bonds with the residues Asp23, Phe100, and Asp101. Additionally, its phenyl ring established a pi -cation interaction with Lys90. The ligand also showed extensive hydrophobic interactions with the surrounding apolar residues, including Ala93, Ala94, Val98, Phe100, Met102, and Phe103. Similarly, NPACT01210 formed multiple hydrogen bonds through its -OH groups with Asp21, Asp23, Lys31, Lys90, and Phe100. Hydrophobic contacts were observed with Ile22, Ala93, Ala94, Val98, Phe100, and Met102. The literature-reported GPX4 activator [ 9 ], PKUMDL-LC-102, established hydrogen bonds within the allosteric pocket with Asp21, Asp23, Lys90, and Met102. Hydrophobic interactions were noted with Ile22, Val98, Phe100, Met102, and Phe103. Collectively, the analysis indicates that both NPACT00189 and NPACT01210 interact with the key basic (Lys90), acidic (Asp23, Asp101), and apolar residues that characterize the GPX4 allosteric binding region, exhibiting an interaction profile similar to the known activator PKUMDL-LC-102. 2.1.3. Interactions of the compounds at the active site of AChE The deep and narrow active-site gorge of AChE comprises two major ligand-binding regions: the peripheral anionic site (PAS) and the acylation or catalytic site (AS). The PAS, located near the entrance of the gorge, features the key aromatic residue Trp286, while the AS contains the catalytic triad—Ser203, Glu334, and His447—as well as the critical aromatic residue Trp86 positioned near the base of the gorge. NPACT00189 primarily interacted with apolar residues within the active-site region, including Tyr72, Leu76, Trp86, Tyr124, Val294, Trp286, Phe295, Phe297, Tyr337, Val340, Ala343, and Pro344 via hydrophobic interactions. It also established a pi–pi stacking interaction with Phe338 and formed hydrogen bonds with Tyr75, Tyr341, Gly345, and Phe346. In its initial binding pose, NPACT00189 showed limited interaction with the key residues of the PAS and AS; however, MD simulations indicated that the ligand migrated closer to the active site over time, increasing its interaction with crucial catalytic residues (Fig. 4 ). NPACT01210 formed hydrogen bonds with several residues in the catalytic site, including Asp74, Trp286, Ser293, Phe295, Arg296, and Tyr341. Additionally, the ligand engaged in hydrophobic interactions with Tyr72, Leu76, Trp86, Tyr124, Val288, Leu289, Val294, Tyr337, and Phe338 (Fig. 4 ). As a positive control, donepezil interacted with Trp286 via pi–pi stacking and formed pi–cation interactions with Tyr341 and Tyr337, in addition to hydrophobic interactions with residues Tyr72, Trp86, Tyr124, Trp286, Leu289, Val294, Phe295, Phe297, and Phe338 (Fig. 4 ). 2.2. Molecular Dynamics Simulations (MD) According to molecular docking results, NPACT00189 and NPACT01210 have high binding scores against both GPX4 and AChE. Based on these results, MD studies were conducted with NPACT00189, NPACT01210, PKUMDL-LC102, and Donepezil for 250 ns. 2.2.1. NPACT00189-GPX4 Complex MD simulations were conducted to evaluate the binding stability and interaction profile of NPACT00189 within the allosteric site of GPX4. The ligand exhibited RMSD fluctuations between 3.5 Å and 7.5 Å during the initial 0–150 ns followed by increased fluctuations ranging from 6.0 Å to 9.5 Å after 150 ns. The average RMSD value between 160 and 250 ns after the ligand became stable was calculated to be approximately 3.3 Å. The Cα RMSD of GPX4 remained relatively stable, fluctuating between 0.9 Å and 2.2 Å throughout the simulation, indicating a well-maintained protein structure. The RMSF analysis revealed minor fluctuations (< 3.2 Å) primarily in the 110–125 residue region, with all other fluctuations remaining below 2.4 Å (Fig. 5 ). NPACT00189 maintained persistent interactions with key residues, including Lys90, Phe100, and Asp101, primarily through hydrogen bonding and water-mediated contacts. Approximately 99% of the simulation time showed consistent interactions with Lys99, contributing to ligand stability (Fig. 5 ). Up to 150 ns, the compound sustained interactions with allosteric site residues, especially the acidic residue Asp101; the basic residue Lys90; and hydrophobic residues Ala94, Phe100, and Met102. However, the compound showed limited interaction with acidic residues Asp21 and Asp23. Following this initial 150 ns period, a conformational rearrangement occurred, leading to a reduction in interactions with Asp21, Asp23, Lys90, and Met102, and the emergence of new interactions with Lys31, Ala64, Leu68, Arg69, and Val98. Throughout the simulation, the ligand maintained critical contacts with Asp101 and Phe100, showing that interactions with Lys90, Phe100, and Asp101 may play a key role in anchoring the molecule within the GPX4 allosteric site (Fig. 6 ). 2.2.2. NPACT01210-GPX4 Complex NPACT01210 initially exhibited stable binding within the GPX4 allosteric site during the first 47 ns of the MD simulation, maintaining an average ligand RMSD of approximately 1.0 Å. However, following this period, a significant conformational shift was observed, with the ligand RMSD increasing and fluctuating between 17.5 Å and 21.5 Å, indicating displacement from the original binding site. Throughout the entire simulation, the RMSD of the protein's Cα atoms remained within the range of 1.0 Å to 2.0 Å, reflecting the overall structural integrity of the protein. RMSF analysis demonstrated minimal residue fluctuations, with all values below 3.0 Å, suggesting a relatively stable protein backbone (Fig. 5 ). The compound interacted with Glu65 (99%), His60 (80%), and Phe170 (75%) by forming hydrogen bonds and a water bridge during a significant portion of the simulation (Fig. 5 ). From 0 to 47 ns of the simulation, it interacted with residues located in the allosteric region, including Asp21, Asp23, Lys31, Lys90, Lys99, Phe100, Met102, and Phe103. However, due to a significant conformational change, the ligand’s interactions with residues such as Asp21, Asp23, Lys31, Lys90, Val98, Phe100, Met102, and Phe103 were disrupted. After 47th ns, the ligand moved away from the allosteric site and began to interact with Glu34, Val36, Ala64, Glu65, Cys66, Leu68, Ile70, Lys145, and Phe170. Although the ligand remained stable from 50 ns onwards, it was observed to move away from the allosteric pocket (Fig. 6 ). MD simulation results for the NPACT01210–GPX4 complex indicated that the ligand dissociated from the active site after 47 ns, accompanied by a marked reduction in interactions with key residues. Although molecular docking initially suggested NPACT01210 as a candidate compound targeting both GPX4 and AChE, the MD analyses ruled out its potential to act as a GPX4 inhibitor. 2.2.3. PKUMDL-LC-102 -GPX4 Complex The compound PKUMDL-LC-102, known as a GPX4 activator, was used as a reference molecule in our study. Examination of the protein–ligand RMSD plot shows marked fluctuations in the ligand’s RMSD values (Fig. 7 ). Although the compound remains relatively stable during certain segments of the simulation (specifically between 60–110 ns and 160–215 ns), it does not display an overall stable binding profile. The RMSD values of the protein Cα atoms range between 0.9 and 2.4 Å, which is within an acceptable range. In the protein RMSF analysis, fluctuations remain below 3.6 Å. Throughout the simulation, the compound interacts with multiple residues; however, during significant portions of the trajectory, it predominantly forms hydrophobic contacts and water-bridge interactions with Lys20 and Met120 (Fig. 7 ). Overall, the compound is able to maintain its interactions with these residues intermittently throughout the simulation. 2.2 .4. NPACT00189-AChE Complex The MD simulation of the NPACT00189–AChE complex throughout the 250 ns revealed an average ligand RMSD of approximately 3.5 Å, while the RMSD of the protein Cα atoms remained stable around 1.7 Å. The RMSF profile of the protein showed minimal fluctuations, all remaining below 2.8 Å, indicating overall structural stability (Fig. 8 ). Throughout the simulation, NPACT00189 maintained persistent interactions—hydrogen bonding, hydrophobic contacts, and water bridges—with the key residue Trp286 for approximately 70% of the total simulation time. Additionally, the ligand engaged consistently with residues located near the base of the peripheral anionic site (PAS), including Asp74 (hydrogen bond, water bridge), Tyr124 (hydrophobic, water bridge), Ser125 (hydrogen bond, water bridge), and Tyr341 (hydrophobic, water bridge) (Fig. 8 ). Continuous interactions with Trp286, Asp74, Thr75, Tyr124, and Tyr341 throughout the trajectory further support the notion that NPACT00189 remained stably accommodated within the AChE active-site gorge (Fig. 9 ). These findings suggest a robust and sustained binding affinity of NPACT00189 to the AChE binding cavity during the MD simulation. 2.2 .5. NPACT01210-AChE Complex Throughout the 250 ns MD simulation, the average RMSD value of NPACT01210 within the AChE binding pocket was approximately 3.0 Å, while the average Cα RMSD of the protein remained around 0.75 Å, indicating overall structural stability (Fig. 8 ). During a significant portion of the simulation, the ligand formed hydrogen bonds and water bridges with residues such as Asp74 (93%), Tyr124 (68%), and Phe293 (75%). Persistent hydrophobic interactions with Phe295 and Tyr341 were also detected, lasting for more than 95% of the simulation (Fig. 8 ). In addition, stable contacts were observed with Ser293, Arg296, and Tyr337 (Fig. 9 ). These findings indicate that NPACT01210 remained structurally stable within the AChE binding site. However, limited interactions with key residues in the peripheral anionic site (PAS) and acylation site (AS) reduce its potential to act as an effective AChE inhibitor. 2.2 .6. Donepezil-AChE Complex In the molecular dynamics (MD) simulations performed for AChE, Donepezil was employed as the reference inhibitor. During the initial phase of the simulation (0–140 ns), the ligand exhibited RMSD values ranging from 1.5 to 4.5 Å. In the later phase (160–245 ns), the RMSD increased and fluctuated between 6 and 9 Å, indicating periods of reduced conformational stability. Throughout the simulation, the RMSD of the protein Cα atoms remained consistently within the 0.9–2.4 Å interval, and the protein RMSF values were below 2.8 Å, reflecting a stable protein backbone (Fig. 10 ). Donepezil interacted predominantly with Asp74 (ionic interactions and hydrogen bonding), Trp341 (hydrophobic contacts and water-bridge interactions), and Tyr337 (hydrophobic and water-bridge interactions). Additionally, the ligand maintained intermittent interactions with Asp74, Tyr337, Phe338, and Tyr341 across various segments of the simulation trajectory (Fig. 10 ), suggesting that these residues contribute to the stabilization of the ligand within the AChE active-site gorge. For the validation of the molecular docking protocol, the donepezil molecule present in the crystal structure was removed, energy-minimized, and subsequently redocked. The resulting RMSD between the crystallographic and redocked poses was calculated as 0.9008 Å. 3. Discussion Recent investigations have established the pivotal involvement of ferroptosis in neuronal demise across various neurological disorders [ 18 ]. Key attributes of ferroptosis, such as iron dysregulation and reactive oxygen species (ROS) accumulation, are directly relevant to Alzheimer's disease (AD) pathology [ 19 ]. This compelling evidence strongly implicates ferroptosis as a primary etiological factor underlying AD, closely correlating with the observed neuronal loss and cognitive decline [ 20 ]. Glutathione Peroxidase 4 (GPX4) acts as a key mediator in the cellular processes leading to ferroptosis-induced death. Furthermore, activation of GPX4 has been reported to increase intracellular protein levels of the enzyme in AD-specific pathological conditions [ 9 ]. Activation of GPX4 can reduce oxidative damage to neuronal membranes and enhance cellular resilience against oxidative stress, which is a hallmark of AD pathology. Conversely, inhibition of AChE increases acetylcholine availability in synaptic clefts, thereby ameliorating cognitive deficits associated with cholinergic neurotransmission impairment [ 22 ]. The simultaneous modulation of GPX4 and AChE represents a promising polypharmacological strategy for neurodegenerative diseases, particularly AD, where oxidative stress and cholinergic dysfunction are both prominent pathological features [ 23 , 24 ]. Therefore, targeting both the allosteric site of GPX4 and the AChE active site is an important strategy for AD treatment. In this study molecular docking, statistical analysis, and MD simulations were used to identify plant-based dual-target compounds capable of simultaneously modulating GPX4 and AChE in Alzheimer's disease. Out of an initial library of 3,014 plant-derived compounds, 68 compounds were initially selected after demonstrating dual-binding potential, with binding energies of ≤ -3.300 kcal/mol for GPX4 and ≤ -7.700 kcal/mol for AChE. Docking score thresholds were defined based on the score distribution of the screened library, selecting compounds within the favorable tail of the affinity profile rather than applying fixed universal cutoffs. For AChE, the selected threshold is consistent with reported docking score ranges of known reference inhibitors, while GPX4 candidates were prioritized using a percentile-based ranking approach. Based on subsequent molecular docking and statistical ranking analysis, NPACT00189 and NPACT01210 emerged as the most promising dual-target modulators. The use of Z-score normalisation for composite scoring provided a robust statistical framework to fairly compare binding affinities across targets with inherently different energy scales. This approach enabled the prioritisation of compounds with balanced dual-target activity rather than those with disproportionate affinity toward a single target. NPACT00189 and NPACT01210 demonstrated superior or comparable binding scores to the reference compounds PKUMDL-LC-102 (GPX4 activator) and Donepezil (AChE inhibitor), suggesting their potential as viable therapeutic candidates. The interactions played a particularly important role in maintaining NPACT00189's stability in the allosteric pocket of GPX4. These interactions are consistent with the binding properties of known GPX4 activators, which generally exploit the electrostatic and hydrophobic nature of the allosteric site [ 9 ]. Molecular docking and molecular dynamics (MD) simulation studies are important tools in screening potential drug candidates for Alzheimer's disease (AD). AChE is a well-defined target in AD treatment; structure-based virtual screenings are frequently used to discover new inhibitors with increased binding affinity [ 25 ]. Additionally, GPX4 has recently gained attention as a target for AD [ 26 ]. Using docking studies and MD simulations, it is possible to more confidently prioritize candidate compounds suitable for experimental validation [ 27 ]. The critical role of ferroptosis in neurological pathology is increasingly recognized, driving innovative drug discovery efforts. Recent studies highlight diverse strategies to modulate this pathway: Thonningianin A, a novel ferroptosis inhibitor, was found to improve functional recovery and reduce neuronal damage following spinal cord injury (SCI), demonstrating the therapeutic potential of targeting ferroptosis beyond chronic neurodegenerative diseases [ 28 ]. Furthermore, the development of hybrid molecules, such as those combining Donepezil and a ferroptosis inhibitor, has been shown to synergistically mitigate ferroptosis and cholinergic dysfunction in Alzheimer's Disease (AD), validating the multi-target strategy adopted by our work [ 29 ]. This multi-target approach is further supported by the evidence that specific inhibitors, like Compound 1 developed by Liu et al. (2025), offer significant neuroprotection by attenuating oxidative stress and mitochondrial injury, thereby strongly confirming the need for sustained ferroptosis inhibition in mitigating neuronal injury [ 30 ]. Collectively, these reports underscore that effective intervention in complex conditions like AD must involve simultaneous modulation of both established pathological hallmarks (e.g., cholinergic deficit) and emerging cell death mechanisms (ferroptosis), which our dual-target compound, NPACT00189, successfully addresses. The successful identification of potent GPX4 inhibitors derived from mercaptosuccinic acid, as reported by Fatonah et al. (2020), underscores the therapeutic viability of GPX4 inhibition for inducing ferroptosis [ 31 ]. Consistent with this finding, our study utilizes natural plant-derived compounds to target GPX4, validating the ferroptosis pathway as a generalizable strategy against neurodegeneration and demonstrating the potential for plant-based therapeutics to achieve this goal. In another investigation exploring the effect of plant-derived compounds on ferroptosis, Silibinin was found to attenuate ferroptosis in acute kidney injury by targeting FTH1. This finding highlights that the therapeutic regulation of ferroptosis by phytochemicals can occur through diverse molecular targets beyond GPX4, reinforcing the vast potential of natural products in modulating multiple facets of this cell death pathway across various diseases [ 32 ]. The GPX4 allosteric site presents a more open and flexible binding region, which may result in comparatively weaker binding energies, whereas AChE possesses a deep, narrow active-site gorge that facilitates strong ligand interactions through multiple aromatic residues [ 33 , 34 ]. The active-site gorge of AChE comprises two functionally distinct regions: the peripheral anionic site (PAS) at the entrance, characterised by the aromatic residue Trp286, and the acylation site (AS), which contains the catalytic triad (Ser203, Glu334, His447) as well as the critical Trp86 residue [ 35 ]. Effective AChE inhibitors typically interact with both regions, thereby blocking substrate access and preventing catalytic activity. Alameen et al. [ 36 ] conducted molecular docking and MD simulation studies on PKUMDL-LC-102, a known GPX4 activator, and elucidated its binding mechanism to the protein. Their docking results indicated that the compound interacts predominantly with Asp23 and Met102. The MD simulations further revealed pronounced fluctuations in the ligand RMSD plot. The docking and MD simulation findings obtained in our study for PKUMDL-LC-102 are consistent with those reported in the literature. In the literature [ 37 ], screening of 1,768 PubChem compounds was performed to evaluate their binding energies against AChE, and a strong candidate (PubChem CID 54414454) was identified. The compound formed key interactions with Asp74, Tyr124, and Tyr341 within the active site. To further characterize its binding behavior, MD simulations were performed, and the RMSD profile reached equilibrium, supporting its potential as a promising AChE inhibitor. Following a similar rationale, we first performed molecular docking studies against AChE using compounds retrieved from the NPACT, HIT, and HIM databases. Among these, NPACT01210 and NPACT00189 exhibited the highest binding scores. The interaction patterns observed in our docking analyses were consistent with previously reported literature. Subsequent MD simulations conducted for these two compounds demonstrated favorable stability and binding features, suggesting that both NPACT01210 and NPACT00189 may serve as potential AChE inhibitors. NPACT00189, with its demonstrated stability and favourable interaction profiles at both targets, represents a promising lead compound for further development. Its plant-derived origin also offers potential advantages in terms of bioavailability, reduced toxicity, and multi-target activity inherent to many phytochemicals [ 6 ]. However, experimental validation through in vitro enzymatic assays, cell-based models, and ultimately in vivo studies will be essential to confirm its therapeutic potential. Multi-target ligand approaches offer substantial promise for managing AD, though they introduce inherent complexities in obtaining new compounds with the requisite balanced in vitro and in vivo activities and favorable pharmacokinetic/toxicity profiles. Despite these hurdles, ongoing discoveries regarding AD pathogenesis and continued innovation in multi-target drug discovery reinforce the idea that these ligands are poised to become a core potential pharmacotherapy for this multifactorial neurodegenerative condition [ 38 ]. Based on molecular docking results, both NPACT01210 and NPACT00189 were initially proposed as dual-target candidates against AChE and GPX4. However, MD simulations revealed that NPACT01210 dissociated from the GPX4 active site with a pronounced loss of key interactions, thereby ruling out its potential as a GPX4 modulator, whereas NPACT00189 retained binding features. Collectively, these findings position NPACT00189 as a robust dual-target modulator capable of maintaining stable and functionally relevant interactions with both GPX4 and AChE over extended simulation periods. Limitations While this study provides promising insights into the dual-target potential of plant-based compounds against GPX4 and AChE, it is subject to several limitations. First, all findings are based on in silico predictions; therefore, experimental validation through enzymatic and cellular assays is essential to confirm binding and biological activity. Second, although NPACT00189 showed favorable binding properties, further structure-activity relationship (SAR) analyses and chemical modifications may enhance its pharmacological profile. Additionally, while 250 ns MD simulations offer valuable conformational insights, extended simulations or alternative sampling techniques may reveal additional dynamics. Finally, force field limitations may affect simulation accuracy. Future Directions Building on the promising in silico findings, several key directions are recommended to advance the translational potential of NPACT00189: Experimental Validation Confirm the dual-target interaction of NPACT00189 through biochemical assays, including AChE inhibition and GPX4 activation tests, alongside cell-based functional studies, noting that the current findings indicate only potential AChE inhibitory and potential GPX4 activator-like effects based on in silico binding evidence. ADMET Profiling and Optimization Conduct absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses to assess pharmacokinetic properties and optimize drug-likeness. Neuroprotective Efficacy Testing Investigate the compound's ability to mitigate oxidative stress and cholinergic dysfunction in relevant neuronal or cellular models. Medicinal Chemistry Optimization Perform structure-activity relationship (SAR) studies and targeted chemical modifications to enhance binding affinity, metabolic stability, and bioavailability. In Vivo Evaluation Assess therapeutic efficacy and safety in animal models of neurodegeneration to establish preclinical relevance. These steps are crucial to translating computational predictions into viable therapeutic strategies for neurodegenerative disorders such as Alzheimer's disease. 4. Materials and Methods 4.1. Dataset Acquisition and Preprocessing In this study, a total of 3,014 plant-derived compounds were collected by downloading the NPACT (1,541 compounds), HIT (486 compounds), and HIM (987 compounds) collections from the COCONUT (COlleCtion of Open Natural prodUcTs) database ( https://coconut.naturalproducts.net/ Accessed: 1.11.2025). These molecules were computationally evaluated against two therapeutically relevant targets GPX4 and AChE, to identify potential dual-target inhibitors involved in oxidative stress and neurodegeneration pathways. 4.2. Molecular Docking Studies All molecular docking studies were performed using Maestro 14.5 (Schrödinger, New York) with the aim of identifying dual-target compounds potentially useful against Alzheimer’s disease, acting as both Acetylcholinesterase (AChE) inhibitors and Glutathione Peroxidase 4 (GPX4) modulators. For this purpose, the crystal structures of AChE (PDB ID: 7D9Q) [ 39 ] and GPX4 (PDB ID: 7U4I) [ 40 ] were downloaded from the Protein Data Bank ( www.rcsb.org ) and prepared using the Protein Preparation module integrated within the Maestro software suite. Water molecules in the crystal structures were removed, hydrogen atoms were added, bond orders were adjusted, and energy minimization was performed using the OPLS4 force field. A grid map of 20 Å on each side was generated in the active regions of the proteins. Donepezil (for AChE) and PKUMDL-LC-102 (for GPX4) were selected as reference compounds. The ligands and reference compounds were prepared with the LigPrep software for molecular docking studies. The ligands were docked into the active sites of the 7D9Q and 7U4I PDB-encoded proteins 50 times at standard precision (SP) using Glide. 4.3. Statistical Analysis To enable an objective comparison of compounds exhibiting dual affinity toward GPX4 and AChE, docking scores (X) were standardized using Z-score normalization, a method widely applied in inverse docking and target ranking to minimize target-specific scoring bias [ 41 ]. The raw docking scores (kcal/mol) for each target were independently standardized using Z-score transformation. The Z-score ( Z ) for each raw docking score ( X ) was calculated as: where µ and σ represent the mean and standard deviation of the docking scores, respectively (Table S2.). Crucially, these parameters ( µ and σ ) were calculated exclusively across the pool of the 68 initial dual-target compounds for the respective protein, ensuring the Z-score reflects the relative affinity within the highly selective candidate subset. Subsequently, a Composite Binding Score ( C score ) was derived for each molecule by summing the individual normalized Z -scores for GPX4 (Z GPX4 ) and AChE (Z AChE ): This composite scoring system, a robust approach known as consensus scoring in multi-target drug design [ 42 ], provided a statistically robust framework for ranking the dual-active compounds based on their overall combined affinity, which guided the selection of the two highest-ranked candidates for subsequent molecular dynamics simulations. 4.4. Molecular Dynamics Simulations (MD) The two most promising dual-target compounds identified by the molecular docking results, NPACT00189 and NPACT01210, were subjected to 250 ns MD simulations. These simulations were carried out to thoroughly investigate the stability and dynamic trajectory of the ligand–protein interactions within the GPX4 and AChE complexes. The complexes were analysed for parameters, including Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF). The Desmond program (Maestro 14.5) was employed for all MD simulations. Simulations were initiated with the best pose obtained from the molecular docking studies. The 250 ns MD simulations were conducted using the NPT ensemble for compounds NPACT00189 and NPACT01210 against 7D9Q and 7U4I. In these simulations, RMSD values of the ligands and proteins, as well as ligand–protein interactions, were evaluated. The protein-ligand complexes were immersed in a solvent by placing them in an octahedral box containing TIP3P water molecules, ensuring a minimum distance of 10 Å between the protein-ligand complexes and the box edges. The systems were rendered chemically neutral by adding Na⁺ and Cl⁻ ions, and the ionic concentration was adjusted using a 0.15 M NaCl solution. Desmond's standard relaxation protocol was employed. The Nose–Hoover chain algorithm was used to maintain the temperature at 300 K, and the Martyna–Tobias–Klein algorithm was applied to regulate the pressure at 1.01325 bar [ 42 – 44 ]. 4.5. MM/GBSA Calculations The MM/GBSA method was employed to estimate the binding free energies of the protein–ligand complexes. All calculations were performed using Schrödinger’s Prime MM-GBSA module. Complexes exhibiting the most favorable binding poses obtained from the molecular docking studies were selected for subsequent analysis. The binding free energy (ΔG bind ) was calculated according to the following equation: $$\:{\Delta\:}{G}_{\text{bind}}={\Delta\:}{E}_{\text{MM}}+{\Delta\:}{G}_{\text{solv}}+{\Delta\:}{G}_{\text{SA}}$$ Where ΔE MM represents the molecular mechanics energy, ΔG solv denotes the solvation free energy, and ΔG SA corresponds to the surface area–dependent free energy contribution. All binding free energies are reported in kcal/mol [ 45 ]. 5. Conclusions In this molecular modeling and dynamics study, a comprehensive virtual screening of plant-based compounds identified NPACT00189 as promising dual-target hit compounds against Alzheimer's disease by simultaneously targeting GPX4 and AChE. Docking analysis indicated high binding affinity towards both target proteins, exceeding that of the reference GPX4 activator PKUMDL-LC-102. Molecular Dynamics simulations confirmed that NPACT00189 exhibited stable binding in both the AChE active gorge and the GPX4 allosteric site, maintaining key interactions throughout the 250 ns trajectory, thus supporting its potential as a balanced dual-target modulator. Conversely, while NPACT01210 showed good stability within the AChE complex, its significant displacement from the GPX4 allosteric pocket after 47 ns suggests that its activity may be predominantly restricted to AChE inhibition. These results provide computational evidence that NPACT00189 is a superior lead candidate for the development of novel plant-derived therapies addressing the complex multi-factorial pathology of Alzheimer's disease. However, the promising dual-target profile of NPACT00189 must be rigorously validated through subsequent in vitro enzyme inhibition assays and in vivo studies to confirm its efficacy, bioavailability, and therapeutic potential. Abbreviations The following abbreviations are used in this manuscript: AD Alzheimer’s disease ACh Acetylcholine AChE Acetylcholine Esterase ChEIs Cholinesterase inhibitors COCONUT COlleCtion of Open Natural prodUcTs GPX4 Glutathione peroxidase 4 HIM Herbal Ingredients In-Vivo Metabolism Database HIT Herbal Ingredients' Targets Database MD Molecular Dynamics MM/GBSA Molecular Mechanics / Generalized Born Surface Area NPACT Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target Database. RMSD Root-Mean-Square Deviation RMSF Root-Mean-Square Fluctuation SAR Structure-activity relationship Declarations Supplementary Materials: Table S1 and S2 Author Contributions: Conceptualization, S.R.O.D.; methodology, S.R.O.D, M.A.A.; software, M.A.A.; validation, M.A.A.; formal analysis, S.R.O.D.; investigation, S.R.O.D.; resources, M.A.A.; data curation, M.A.A.; writing—original draft preparation, S.R.O.D.; writing—review and editing, M.A.A.; visualization, S.R.O.D.; supervision, M.A.A.; project administration, S.R.O.D.; funding acquisition, S.R.O.D. All authors have read and agreed to the published version of the manuscript. Funding: This study, including the article processing charge (APC), was funded by the Scientific Research Projects Unit (BAP) of Inonu University under the grant number TSA-2025-4366. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data used in this study were obtained by downloading the NPACT , HIT , and HIM collections available within the COCONUT (Collection Of Open Natural Products) database (https://coconut.naturalproducts.net/ Accessed: 1.Sep.2025). All compound sets were retrieved in standardized formats and locally processed for virtual screening. Acknowledgments: During the preparation of this manuscript, the author(s) used ChatGPT-5.1 (OpenAI) and QuillBot for the purposes of grammar refinement, language editing, and sentence structure improvements. The authors have reviewed and edited the output and take full responsibility for the content of this publication. The authors have reviewed and edited the output and take full responsibility for the content of this publication. Conflicts of Interest: The authors declare no conflicts of interest. References Huang, L. K., Kuan, Y. C., Lin, H. W. & Hu, C. J. 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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-8748563","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":592039772,"identity":"9c8509e0-a285-47c6-827e-5151f0516ed3","order_by":0,"name":"Suheda Rumeysa Osmanlioglu 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interactions of NPACT00189, NPACT01210, and PKUMDL-LC-102 within the allosteric site of GPX4. \u003cem\u003eThe 2D interaction diagrams were generated using Maestro 14.5 (Schrödinger, New York, NY, USA).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8748563/v1/64ba8946b6c401958d6c8eb6.jpg"},{"id":102963077,"identity":"0ff84556-8087-4681-bbc1-191ea00b550f","added_by":"auto","created_at":"2026-02-19 04:13:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":383919,"visible":true,"origin":"","legend":"\u003cp\u003e2D binding interactions of NPACT00189, NPACT01210, and Donepezil within the active site of AChE.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8748563/v1/39f149509588604df4f3fe34.jpg"},{"id":102963382,"identity":"3ebd2d38-942a-45b7-80a1-b5e0701ad3e7","added_by":"auto","created_at":"2026-02-19 04:16:28","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":857321,"visible":true,"origin":"","legend":"\u003cp\u003e250 ns MD simulation analysis of NPACT00189 and NPACT01210 within the allosteric site of GPX4, illustrating the protein–ligand RMSD, protein RMSF, and protein–ligand contact profiles.\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8748563/v1/f20edb168002e14f13c20088.jpg"},{"id":102845225,"identity":"a4da08f8-031a-4b14-b2ff-aa20e8ebf8bc","added_by":"auto","created_at":"2026-02-17 13:04:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":260803,"visible":true,"origin":"","legend":"\u003cp\u003eTimeline of interactions of NPACT00189 and NPACT01210 in GPX4 during 250ns MD simulation\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8748563/v1/dbe15e59c79f275935a197cb.png"},{"id":102963010,"identity":"0da0beff-fa23-474f-b8c7-58bff492db7a","added_by":"auto","created_at":"2026-02-19 04:12:47","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":398064,"visible":true,"origin":"","legend":"\u003cp\u003e250 ns MD simulation analysis of PKUMDL-LC within the allosteric site of GPX4, illustrating the protein–ligand RMSD, protein RMSF, protein–ligand contact and timeline of interactions profiles\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8748563/v1/88e1dc870d7fb6394df347da.jpg"},{"id":102845232,"identity":"bc2182b6-73f3-41b6-b521-156b45e6b078","added_by":"auto","created_at":"2026-02-17 13:04:18","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":676139,"visible":true,"origin":"","legend":"\u003cp\u003e250 ns MD simulation analysis of NPACT00189 and NPACT01210 within the active gorge of AChE, illustrating the protein–ligand RMSD, protein RMSF, and protein–ligand contact profiles.\u003c/p\u003e","description":"","filename":"figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8748563/v1/dec09c8c94f784f426a9cc5e.jpg"},{"id":102962850,"identity":"facdaa98-ae20-4380-8d04-d7663347ff2f","added_by":"auto","created_at":"2026-02-19 04:11:41","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":354574,"visible":true,"origin":"","legend":"\u003cp\u003eTimeline of interactions of NPACT00189 and NPACT01210 in AChE during 250ns MD simulation\u003c/p\u003e","description":"","filename":"figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8748563/v1/4b0654c7b7ea2e28df4f66b4.png"},{"id":102845231,"identity":"b26e4bb1-7cad-46b7-9899-80bba4ee1c23","added_by":"auto","created_at":"2026-02-17 13:04:18","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":544337,"visible":true,"origin":"","legend":"\u003cp\u003e250 ns MD simulation analysis of Donepezil within the active gorge of AChE, illustrating the protein–ligand RMSD, protein RMSF, protein–ligand contact and timeline of interactions profiles\u003c/p\u003e","description":"","filename":"figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8748563/v1/c994aee3203c54619980f3a7.jpg"},{"id":108156290,"identity":"dd12d129-5b43-45cc-8b03-8a50ba8f1404","added_by":"auto","created_at":"2026-04-30 02:41:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4540606,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8748563/v1/5a8bbb6e-1d54-4d93-80d8-80b9cd200245.pdf"},{"id":102845223,"identity":"169885f4-0ebe-4149-a74b-c7f7d7d5e06e","added_by":"auto","created_at":"2026-02-17 13:04:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32981,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8748563/v1/e6c2bfb268022831aa777dd1.docx"},{"id":102845229,"identity":"5ee5c5a0-b5b6-4975-a617-7f1f9be76221","added_by":"auto","created_at":"2026-02-17 13:04:18","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1781523,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-8748563/v1/11cc7e8baf53ced4e6367b61.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual Targeting of AChE Inhibition and GPX4 Activation by Plant-Derived Compounds for the Treatment of Alzheimer’s Disease: Insights from Molecular Docking and Molecular Dynamics Simulations","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a multifactorial neurodegenerative disorder characterized by progressive cognitive impairment, memory loss, and neuronal death. Despite significant research efforts, the precise etiology of AD remains elusive, with multiple intersecting pathogenic pathways contributing to its onset and progression [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among the earliest and most consistently observed neurochemical abnormalities in AD is the dysfunction of the cholinergic system, particularly the reduction of acetylcholine (ACh) levels in the brain. Cholinesterase inhibitors (ChEIs) such as donepezil, galantamine, and rivastigmine aim to counteract this deficit by inhibiting acetylcholinesterase (AChE), thereby increasing synaptic ACh concentrations. However, these treatments offer only symptomatic relief and do not halt disease progression [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, the pursuit of new therapeutic leads is essential for developing effective disease-modifying agents capable of preventing or arresting disease development. Bioactive phytochemicals have long served as a crucial resource in the drug discovery pipeline, providing candidates with limited systemic toxicity. This low-toxicity profile is critical, as it has the potential to enhance compliance during the necessary prolonged administration required for chronic conditions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFerroptosis, a regulated, iron-dependent form of non-apoptotic cell death characterized by the accumulation of lipid peroxides, has emerged as a potential mechanism underlying neuronal degeneration in AD [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Unlike classical necrosis or apoptosis, ferroptosis is driven by disruptions in iron homeostasis, glutathione depletion, and impaired activity of glutathione peroxidase 4 (GPX4), a key antioxidant enzyme responsible for reducing lipid hydroperoxides [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Several studies have shown that GPX4 plays a neuroprotective role by suppressing ferroptotic pathways. Its inhibition or downregulation sensitizes neurons to oxidative damage and ferroptotic cell death, while its activation has been associated with mitigation of AD-like pathology [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In fact, the depletion of GPX4 has been shown to result in age-dependent neurodegeneration and cognitive deficits in murine models, highlighting its central role in neuronal survival [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, ferroptosis and cholinergic dysfunction may converge mechanistically in AD. Iron overload in cholinergic neurons may exacerbate oxidative damage, potentiating AChE activity and disrupting neurotransmission. Furthermore, AChE itself has been implicated in the formation of Aβ fibrils, thus linking cholinergic imbalance with amyloid pathology [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The co-occurrence of these processes suggests that therapeutic agents capable of modulating both ferroptosis (via GPX4 activation) and cholinergic signaling (via AChE inhibition) could offer dual protective effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe limitations of the traditional one-drug-single-target approach have become clear, as these agents often exhibit limited efficacy against complex diseases where pathogenesis depends on a set of interconnected biochemical events and multiple bioreceptors operating concomitantly [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Driven by an increased understanding of neurodegenerative disease complexity, drug development has shifted from an initial focus on single targets toward multi-target drug development, treating these diseases as networks of interconnected pathways [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Rationally designed multi-target drugs\u0026mdash;also termed multimodal drugs or network therapeutics\u0026mdash;have emerged as an attractive drug discovery paradigm, aiming to enhance efficacy or improve safety relative to single-target drugs or combinations thereof [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Consequently, multi-target agents have garnered significant attention as promising tools to combat challenging diseases, marking a critical new focus area in pharmacological research [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMedicinal plants are a source of abundant biological activities highly beneficial to human health, providing various natural remedies in the form of fruits, leaves, bark, and vegetables [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The diverse range of bioactive nutrients and phytochemicals present in these natural products plays a vital role in the prevention and potential management of various neurodegenerative diseases. Consequently, increasing attention is being given to these natural compounds of plant origin for their potent neuroprotective effects against oxidative damage, which may potentially hinder neurodegeneration and enhance memory and cognitive functions [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, plant-derived natural products with multitarget properties represent promising candidates for AD drug discovery. Several bioactive phytochemicals have demonstrated both AChE inhibitory and antioxidant effects, with recent \u003cem\u003ein silico\u003c/em\u003e studies highlighting their potential to interact favorably with GPX4 and AChE simultaneously [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Such dual-action molecules could suppress ferroptotic cell death while enhancing cholinergic neurotransmission, offering a novel strategy for disease modification.\u003c/p\u003e \u003cp\u003eThis study aims to explore the dual modulation of GPX4 and AChE by plant-derived compounds through molecular docking and molecular dynamics simulations. These insights could inform future \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e investigations and ultimately contribute to the development of more effective multitarget therapies for AD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Molecular Docking Studies\u003c/h2\u003e \u003cp\u003eA total of 3,014 plant-based compounds were subjected to molecular docking to evaluate their binding affinities toward GPX4 and AChE. The molecular docking analysis successfully identified 68 compounds as potential dual-target ligands, demonstrating favorable binding free energies against both GPX4 (\u0026le; -3.300 kcal/mol) and AChE (\u0026le; -7.700 kcal/mol) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). These threshold values were selected that the statistical distribution of docking scores across the entire compound library and reflect the upper half of the affinity profile for each target. Moreover, the selected cutoffs are consistent with docking score ranges reported for known reference inhibitors of GPX4 and AChE, supporting their pharmacological relevance. This distribution- and reference-guided filtering strategy enabled the selection of a meaningful yet sufficiently broad pool of candidate ligands for subsequent statistical ranking and MD simulations. The compounds generally exhibited higher binding affinity towards AChE.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Statistical Analysis of Molecular Docking Results and MM/GBSA Calculation\u003c/h2\u003e \u003cp\u003eA total of 68 compounds showing binding affinity toward both GPX4 and AChE were statistically evaluated to identify the most promising dual-target inhibitors. For standardization and comparative assessment, docking scores were normalized using z-score transformation, independently applied to each target. This normalization allowed the generation of a composite binding score by summing the individual Z-scores for GPX4 and AChE, enabling a robust ranking system for dual-binding potential. It is important to note that GPX4-binding compounds are generally considered moderate binders due to the flexible allosteric site, while AChE-binding compounds are expected to exhibit stronger binding affinities owing to the well-defined active-site gorge. Among the screened compounds, NPACT00189 and NPACT01210 emerged as top-ranked candidates (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). NPACT00189 and NPACT01210 have better docking scores for GPX4 compared to the reference compound, PKUMDL-LC-102. Furthermore, while the AChE docking score of NPACT00189 was similar to that of the reference inhibitor Donepezil, NPACT01210 achieved an even more favourable docking score than Donepezil. MM/GBSA (Molecular Mechanics / Generalized Born Surface Area) calculations were performed to estimate the free binding energies of the compounds toward their target proteins (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Against GPX4, the MM/GBSA binding free energies of NPACT00189 and NPACT01210 were more negative than those observed for PKUMDL-LC-102, indicating stronger binding affinities. For AChE, the free binding energy of NPACT01210 was slightly more negative than that of Donepezil (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These two compounds were consequently selected for molecular dynamics simulations to evaluate their stability and modes of interaction within the targets\u0026rsquo; binding pockets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDocking scores and MM/GBSA binding affinities (kcal/mol) of selected compounds against GPX4 and AChE targets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompounds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPX4\u003c/p\u003e \u003cp\u003eDocking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPX4\u003c/p\u003e \u003cp\u003eMM/GBSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAChE\u003c/p\u003e \u003cp\u003eDocking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAChE\u003c/p\u003e \u003cp\u003eMM/GBSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZ\u003csub\u003eGPX4\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZ\u003csub\u003eAChE\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC\u003csub\u003escore\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPACT00189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-45.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-66.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.3510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPACT01210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-39.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-92.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.7828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.3783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonepezil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-90.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePKUMDL-LC-102\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-18.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. Interactions of the compounds at the allosteric site of GPX4\u003c/h2\u003e \u003cp\u003eThe allosteric site of GPX4 is characterized by two basic residues (Lys31, Lys90), three acidic residues (Asp21, Asp23, Asp101), and seven apolar residues (Ile22, Ala93, Ala94, Val98, Phe100, Met102, Phe103). NPACT00189, NPACT01210, and the reference PKUMDL-LC-102 exhibited similar interaction patterns within this region (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNPACT00189 engaged in several key interactions via its -OH functional groups, forming hydrogen bonds with the residues Asp23, Phe100, and Asp101. Additionally, its phenyl ring established a pi -cation interaction with Lys90. The ligand also showed extensive hydrophobic interactions with the surrounding apolar residues, including Ala93, Ala94, Val98, Phe100, Met102, and Phe103. Similarly, NPACT01210 formed multiple hydrogen bonds through its -OH groups with Asp21, Asp23, Lys31, Lys90, and Phe100. Hydrophobic contacts were observed with Ile22, Ala93, Ala94, Val98, Phe100, and Met102. The literature-reported GPX4 activator [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], PKUMDL-LC-102, established hydrogen bonds within the allosteric pocket with Asp21, Asp23, Lys90, and Met102. Hydrophobic interactions were noted with Ile22, Val98, Phe100, Met102, and Phe103.\u003c/p\u003e \u003cp\u003eCollectively, the analysis indicates that both NPACT00189 and NPACT01210 interact with the key basic (Lys90), acidic (Asp23, Asp101), and apolar residues that characterize the GPX4 allosteric binding region, exhibiting an interaction profile similar to the known activator PKUMDL-LC-102.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3. Interactions of the compounds at the active site of AChE\u003c/h2\u003e \u003cp\u003eThe deep and narrow active-site gorge of AChE comprises two major ligand-binding regions: the peripheral anionic site (PAS) and the acylation or catalytic site (AS). The PAS, located near the entrance of the gorge, features the key aromatic residue Trp286, while the AS contains the catalytic triad\u0026mdash;Ser203, Glu334, and His447\u0026mdash;as well as the critical aromatic residue Trp86 positioned near the base of the gorge.\u003c/p\u003e \u003cp\u003eNPACT00189 primarily interacted with apolar residues within the active-site region, including Tyr72, Leu76, Trp86, Tyr124, Val294, Trp286, Phe295, Phe297, Tyr337, Val340, Ala343, and Pro344 via hydrophobic interactions. It also established a pi\u0026ndash;pi stacking interaction with Phe338 and formed hydrogen bonds with Tyr75, Tyr341, Gly345, and Phe346. In its initial binding pose, NPACT00189 showed limited interaction with the key residues of the PAS and AS; however, MD simulations indicated that the ligand migrated closer to the active site over time, increasing its interaction with crucial catalytic residues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNPACT01210 formed hydrogen bonds with several residues in the catalytic site, including Asp74, Trp286, Ser293, Phe295, Arg296, and Tyr341. Additionally, the ligand engaged in hydrophobic interactions with Tyr72, Leu76, Trp86, Tyr124, Val288, Leu289, Val294, Tyr337, and Phe338 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As a positive control, donepezil interacted with Trp286 via pi\u0026ndash;pi stacking and formed pi\u0026ndash;cation interactions with Tyr341 and Tyr337, in addition to hydrophobic interactions with residues Tyr72, Trp86, Tyr124, Trp286, Leu289, Val294, Phe295, Phe297, and Phe338 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Molecular Dynamics Simulations (MD)\u003c/h2\u003e \u003cp\u003eAccording to molecular docking results, NPACT00189 and NPACT01210 have high binding scores against both GPX4 and AChE. Based on these results, MD studies were conducted with NPACT00189, NPACT01210, PKUMDL-LC102, and Donepezil for 250 ns.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. NPACT00189-GPX4 Complex\u003c/h2\u003e \u003cp\u003eMD simulations were conducted to evaluate the binding stability and interaction profile of NPACT00189 within the allosteric site of GPX4. The ligand exhibited RMSD fluctuations between 3.5 \u0026Aring; and 7.5 \u0026Aring; during the initial 0\u0026ndash;150 ns followed by increased fluctuations ranging from 6.0 \u0026Aring; to 9.5 \u0026Aring; after 150 ns. The average RMSD value between 160 and 250 ns after the ligand became stable was calculated to be approximately 3.3 \u0026Aring;. The Cα RMSD of GPX4 remained relatively stable, fluctuating between 0.9 \u0026Aring; and 2.2 \u0026Aring; throughout the simulation, indicating a well-maintained protein structure. The RMSF analysis revealed minor fluctuations (\u0026lt;\u0026thinsp;3.2 \u0026Aring;) primarily in the 110\u0026ndash;125 residue region, with all other fluctuations remaining below 2.4 \u0026Aring; (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNPACT00189 maintained persistent interactions with key residues, including Lys90, Phe100, and Asp101, primarily through hydrogen bonding and water-mediated contacts. Approximately 99% of the simulation time showed consistent interactions with Lys99, contributing to ligand stability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUp to 150 ns, the compound sustained interactions with allosteric site residues, especially the acidic residue Asp101; the basic residue Lys90; and hydrophobic residues Ala94, Phe100, and Met102. However, the compound showed limited interaction with acidic residues Asp21 and Asp23. Following this initial 150 ns period, a conformational rearrangement occurred, leading to a reduction in interactions with Asp21, Asp23, Lys90, and Met102, and the emergence of new interactions with Lys31, Ala64, Leu68, Arg69, and Val98. Throughout the simulation, the ligand maintained critical contacts with Asp101 and Phe100, showing that interactions with Lys90, Phe100, and Asp101 may play a key role in anchoring the molecule within the GPX4 allosteric site (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. NPACT01210-GPX4 Complex\u003c/h2\u003e \u003cp\u003eNPACT01210 initially exhibited stable binding within the GPX4 allosteric site during the first 47 ns of the MD simulation, maintaining an average ligand RMSD of approximately 1.0 \u0026Aring;. However, following this period, a significant conformational shift was observed, with the ligand RMSD increasing and fluctuating between 17.5 \u0026Aring; and 21.5 \u0026Aring;, indicating displacement from the original binding site. Throughout the entire simulation, the RMSD of the protein's Cα atoms remained within the range of 1.0 \u0026Aring; to 2.0 \u0026Aring;, reflecting the overall structural integrity of the protein. RMSF analysis demonstrated minimal residue fluctuations, with all values below 3.0 \u0026Aring;, suggesting a relatively stable protein backbone (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe compound interacted with Glu65 (99%), His60 (80%), and Phe170 (75%) by forming hydrogen bonds and a water bridge during a significant portion of the simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). From 0 to 47 ns of the simulation, it interacted with residues located in the allosteric region, including Asp21, Asp23, Lys31, Lys90, Lys99, Phe100, Met102, and Phe103. However, due to a significant conformational change, the ligand\u0026rsquo;s interactions with residues such as Asp21, Asp23, Lys31, Lys90, Val98, Phe100, Met102, and Phe103 were disrupted. After 47th ns, the ligand moved away from the allosteric site and began to interact with Glu34, Val36, Ala64, Glu65, Cys66, Leu68, Ile70, Lys145, and Phe170. Although the ligand remained stable from 50 ns onwards, it was observed to move away from the allosteric pocket (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMD simulation results for the NPACT01210\u0026ndash;GPX4 complex indicated that the ligand dissociated from the active site after 47 ns, accompanied by a marked reduction in interactions with key residues. Although molecular docking initially suggested NPACT01210 as a candidate compound targeting both GPX4 and AChE, the MD analyses ruled out its potential to act as a GPX4 inhibitor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. PKUMDL-LC-102 -GPX4 Complex\u003c/h2\u003e \u003cp\u003eThe compound PKUMDL-LC-102, known as a GPX4 activator, was used as a reference molecule in our study. Examination of the protein\u0026ndash;ligand RMSD plot shows marked fluctuations in the ligand\u0026rsquo;s RMSD values (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Although the compound remains relatively stable during certain segments of the simulation (specifically between 60\u0026ndash;110 ns and 160\u0026ndash;215 ns), it does not display an overall stable binding profile. The RMSD values of the protein Cα atoms range between 0.9 and 2.4 \u0026Aring;, which is within an acceptable range. In the protein RMSF analysis, fluctuations remain below 3.6 \u0026Aring;. Throughout the simulation, the compound interacts with multiple residues; however, during significant portions of the trajectory, it predominantly forms hydrophobic contacts and water-bridge interactions with Lys20 and Met120 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Overall, the compound is able to maintain its interactions with these residues intermittently throughout the simulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e2.2\u003c/em\u003e.4. NPACT00189-AChE Complex\u003c/h2\u003e \u003cp\u003eThe MD simulation of the NPACT00189\u0026ndash;AChE complex throughout the 250 ns revealed an average ligand RMSD of approximately 3.5 \u0026Aring;, while the RMSD of the protein Cα atoms remained stable around 1.7 \u0026Aring;. The RMSF profile of the protein showed minimal fluctuations, all remaining below 2.8 \u0026Aring;, indicating overall structural stability (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThroughout the simulation, NPACT00189 maintained persistent interactions\u0026mdash;hydrogen bonding, hydrophobic contacts, and water bridges\u0026mdash;with the key residue Trp286 for approximately 70% of the total simulation time. Additionally, the ligand engaged consistently with residues located near the base of the peripheral anionic site (PAS), including Asp74 (hydrogen bond, water bridge), Tyr124 (hydrophobic, water bridge), Ser125 (hydrogen bond, water bridge), and Tyr341 (hydrophobic, water bridge) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Continuous interactions with Trp286, Asp74, Thr75, Tyr124, and Tyr341 throughout the trajectory further support the notion that NPACT00189 remained stably accommodated within the AChE active-site gorge (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings suggest a robust and sustained binding affinity of NPACT00189 to the AChE binding cavity during the MD simulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e2.2\u003c/em\u003e.5. NPACT01210-AChE Complex\u003c/h2\u003e \u003cp\u003eThroughout the 250 ns MD simulation, the average RMSD value of NPACT01210 within the AChE binding pocket was approximately 3.0 \u0026Aring;, while the average Cα RMSD of the protein remained around 0.75 \u0026Aring;, indicating overall structural stability (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). During a significant portion of the simulation, the ligand formed hydrogen bonds and water bridges with residues such as Asp74 (93%), Tyr124 (68%), and Phe293 (75%). Persistent hydrophobic interactions with Phe295 and Tyr341 were also detected, lasting for more than 95% of the simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). In addition, stable contacts were observed with Ser293, Arg296, and Tyr337 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings indicate that NPACT01210 remained structurally stable within the AChE binding site. However, limited interactions with key residues in the peripheral anionic site (PAS) and acylation site (AS) reduce its potential to act as an effective AChE inhibitor.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e2.2\u003c/em\u003e.6. Donepezil-AChE Complex\u003c/h2\u003e \u003cp\u003eIn the molecular dynamics (MD) simulations performed for AChE, Donepezil was employed as the reference inhibitor. During the initial phase of the simulation (0\u0026ndash;140 ns), the ligand exhibited RMSD values ranging from 1.5 to 4.5 \u0026Aring;. In the later phase (160\u0026ndash;245 ns), the RMSD increased and fluctuated between 6 and 9 \u0026Aring;, indicating periods of reduced conformational stability. Throughout the simulation, the RMSD of the protein Cα atoms remained consistently within the 0.9\u0026ndash;2.4 \u0026Aring; interval, and the protein RMSF values were below 2.8 \u0026Aring;, reflecting a stable protein backbone (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDonepezil interacted predominantly with Asp74 (ionic interactions and hydrogen bonding), Trp341 (hydrophobic contacts and water-bridge interactions), and Tyr337 (hydrophobic and water-bridge interactions). Additionally, the ligand maintained intermittent interactions with Asp74, Tyr337, Phe338, and Tyr341 across various segments of the simulation trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e), suggesting that these residues contribute to the stabilization of the ligand within the AChE active-site gorge. For the validation of the molecular docking protocol, the donepezil molecule present in the crystal structure was removed, energy-minimized, and subsequently redocked. The resulting RMSD between the crystallographic and redocked poses was calculated as 0.9008 \u0026Aring;.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRecent investigations have established the pivotal involvement of ferroptosis in neuronal demise across various neurological disorders [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Key attributes of ferroptosis, such as iron dysregulation and reactive oxygen species (ROS) accumulation, are directly relevant to Alzheimer's disease (AD) pathology [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This compelling evidence strongly implicates ferroptosis as a primary etiological factor underlying AD, closely correlating with the observed neuronal loss and cognitive decline [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Glutathione Peroxidase 4 (GPX4) acts as a key mediator in the cellular processes leading to ferroptosis-induced death.\u003c/p\u003e \u003cp\u003eFurthermore, activation of GPX4 has been reported to increase intracellular protein levels of the enzyme in AD-specific pathological conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Activation of GPX4 can reduce oxidative damage to neuronal membranes and enhance cellular resilience against oxidative stress, which is a hallmark of AD pathology. Conversely, inhibition of AChE increases acetylcholine availability in synaptic clefts, thereby ameliorating cognitive deficits associated with cholinergic neurotransmission impairment [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The simultaneous modulation of GPX4 and AChE represents a promising polypharmacological strategy for neurodegenerative diseases, particularly AD, where oxidative stress and cholinergic dysfunction are both prominent pathological features [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, targeting both the allosteric site of GPX4 and the AChE active site is an important strategy for AD treatment.\u003c/p\u003e \u003cp\u003eIn this study molecular docking, statistical analysis, and MD simulations were used to identify plant-based dual-target compounds capable of simultaneously modulating GPX4 and AChE in Alzheimer's disease. Out of an initial library of 3,014 plant-derived compounds, 68 compounds were initially selected after demonstrating dual-binding potential, with binding energies of \u003cb\u003e\u0026le;\u003c/b\u003e-3.300 kcal/mol for GPX4 and \u003cb\u003e\u0026le;\u003c/b\u003e-7.700 kcal/mol for AChE. Docking score thresholds were defined based on the score distribution of the screened library, selecting compounds within the favorable tail of the affinity profile rather than applying fixed universal cutoffs. For AChE, the selected threshold is consistent with reported docking score ranges of known reference inhibitors, while GPX4 candidates were prioritized using a percentile-based ranking approach. Based on subsequent molecular docking and statistical ranking analysis, NPACT00189 and NPACT01210 emerged as the most promising dual-target modulators.\u003c/p\u003e \u003cp\u003eThe use of Z-score normalisation for composite scoring provided a robust statistical framework to fairly compare binding affinities across targets with inherently different energy scales. This approach enabled the prioritisation of compounds with balanced dual-target activity rather than those with disproportionate affinity toward a single target. NPACT00189 and NPACT01210 demonstrated superior or comparable binding scores to the reference compounds PKUMDL-LC-102 (GPX4 activator) and Donepezil (AChE inhibitor), suggesting their potential as viable therapeutic candidates.\u003c/p\u003e \u003cp\u003eThe interactions played a particularly important role in maintaining NPACT00189's stability in the allosteric pocket of GPX4. These interactions are consistent with the binding properties of known GPX4 activators, which generally exploit the electrostatic and hydrophobic nature of the allosteric site [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMolecular docking and molecular dynamics (MD) simulation studies are important tools in screening potential drug candidates for Alzheimer's disease (AD). AChE is a well-defined target in AD treatment; structure-based virtual screenings are frequently used to discover new inhibitors with increased binding affinity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, GPX4 has recently gained attention as a target for AD [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Using docking studies and MD simulations, it is possible to more confidently prioritize candidate compounds suitable for experimental validation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe critical role of ferroptosis in neurological pathology is increasingly recognized, driving innovative drug discovery efforts. Recent studies highlight diverse strategies to modulate this pathway: Thonningianin A, a novel ferroptosis inhibitor, was found to improve functional recovery and reduce neuronal damage following spinal cord injury (SCI), demonstrating the therapeutic potential of targeting ferroptosis beyond chronic neurodegenerative diseases [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, the development of hybrid molecules, such as those combining Donepezil and a ferroptosis inhibitor, has been shown to synergistically mitigate ferroptosis and cholinergic dysfunction in Alzheimer's Disease (AD), validating the multi-target strategy adopted by our work [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This multi-target approach is further supported by the evidence that specific inhibitors, like Compound 1 developed by Liu et al. (2025), offer significant neuroprotection by attenuating oxidative stress and mitochondrial injury, thereby strongly confirming the need for sustained ferroptosis inhibition in mitigating neuronal injury [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Collectively, these reports underscore that effective intervention in complex conditions like AD must involve simultaneous modulation of both established pathological hallmarks (e.g., cholinergic deficit) and emerging cell death mechanisms (ferroptosis), which our dual-target compound, NPACT00189, successfully addresses.\u003c/p\u003e \u003cp\u003eThe successful identification of potent GPX4 inhibitors derived from mercaptosuccinic acid, as reported by Fatonah et al. (2020), underscores the therapeutic viability of GPX4 inhibition for inducing ferroptosis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Consistent with this finding, our study utilizes natural plant-derived compounds to target GPX4, validating the ferroptosis pathway as a generalizable strategy against neurodegeneration and demonstrating the potential for plant-based therapeutics to achieve this goal.\u003c/p\u003e \u003cp\u003eIn another investigation exploring the effect of plant-derived compounds on ferroptosis, Silibinin was found to attenuate ferroptosis in acute kidney injury by targeting FTH1. This finding highlights that the therapeutic regulation of ferroptosis by phytochemicals can occur through diverse molecular targets beyond GPX4, reinforcing the vast potential of natural products in modulating multiple facets of this cell death pathway across various diseases [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe GPX4 allosteric site presents a more open and flexible binding region, which may result in comparatively weaker binding energies, whereas AChE possesses a deep, narrow active-site gorge that facilitates strong ligand interactions through multiple aromatic residues [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The active-site gorge of AChE comprises two functionally distinct regions: the peripheral anionic site (PAS) at the entrance, characterised by the aromatic residue Trp286, and the acylation site (AS), which contains the catalytic triad (Ser203, Glu334, His447) as well as the critical Trp86 residue [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Effective AChE inhibitors typically interact with both regions, thereby blocking substrate access and preventing catalytic activity.\u003c/p\u003e \u003cp\u003eAlameen et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] conducted molecular docking and MD simulation studies on PKUMDL-LC-102, a known GPX4 activator, and elucidated its binding mechanism to the protein. Their docking results indicated that the compound interacts predominantly with Asp23 and Met102. The MD simulations further revealed pronounced fluctuations in the ligand RMSD plot. The docking and MD simulation findings obtained in our study for PKUMDL-LC-102 are consistent with those reported in the literature.\u003c/p\u003e \u003cp\u003eIn the literature [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], screening of 1,768 PubChem compounds was performed to evaluate their binding energies against AChE, and a strong candidate (PubChem CID 54414454) was identified. The compound formed key interactions with Asp74, Tyr124, and Tyr341 within the active site. To further characterize its binding behavior, MD simulations were performed, and the RMSD profile reached equilibrium, supporting its potential as a promising AChE inhibitor.\u003c/p\u003e \u003cp\u003eFollowing a similar rationale, we first performed molecular docking studies against AChE using compounds retrieved from the NPACT, HIT, and HIM databases. Among these, NPACT01210 and NPACT00189 exhibited the highest binding scores. The interaction patterns observed in our docking analyses were consistent with previously reported literature. Subsequent MD simulations conducted for these two compounds demonstrated favorable stability and binding features, suggesting that both NPACT01210 and NPACT00189 may serve as potential AChE inhibitors.\u003c/p\u003e \u003cp\u003eNPACT00189, with its demonstrated stability and favourable interaction profiles at both targets, represents a promising lead compound for further development. Its plant-derived origin also offers potential advantages in terms of bioavailability, reduced toxicity, and multi-target activity inherent to many phytochemicals [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, experimental validation through \u003cem\u003ein vitro\u003c/em\u003e enzymatic assays, cell-based models, and ultimately in vivo studies will be essential to confirm its therapeutic potential.\u003c/p\u003e \u003cp\u003eMulti-target ligand approaches offer substantial promise for managing AD, though they introduce inherent complexities in obtaining new compounds with the requisite balanced \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e activities and favorable pharmacokinetic/toxicity profiles. Despite these hurdles, ongoing discoveries regarding AD pathogenesis and continued innovation in multi-target drug discovery reinforce the idea that these ligands are poised to become a core potential pharmacotherapy for this multifactorial neurodegenerative condition [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Based on molecular docking results, both NPACT01210 and NPACT00189 were initially proposed as dual-target candidates against AChE and GPX4. However, MD simulations revealed that NPACT01210 dissociated from the GPX4 active site with a pronounced loss of key interactions, thereby ruling out its potential as a GPX4 modulator, whereas NPACT00189 retained binding features. Collectively, these findings position NPACT00189 as a robust dual-target modulator capable of maintaining stable and functionally relevant interactions with both GPX4 and AChE over extended simulation periods.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhile this study provides promising insights into the dual-target potential of plant-based compounds against GPX4 and AChE, it is subject to several limitations. First, all findings are based on in silico predictions; therefore, experimental validation through enzymatic and cellular assays is essential to confirm binding and biological activity. Second, although NPACT00189 showed favorable binding properties, further structure-activity relationship (SAR) analyses and chemical modifications may enhance its pharmacological profile. Additionally, while 250 ns MD simulations offer valuable conformational insights, extended simulations or alternative sampling techniques may reveal additional dynamics. Finally, force field limitations may affect simulation accuracy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFuture Directions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBuilding on the promising in silico findings, several key directions are recommended to advance the translational potential of NPACT00189:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExperimental Validation\u003c/strong\u003e \u003cp\u003eConfirm the dual-target interaction of NPACT00189 through biochemical assays, including AChE inhibition and GPX4 activation tests, alongside cell-based functional studies, noting that the current findings indicate only potential AChE inhibitory and potential GPX4 activator-like effects based on \u003cem\u003ein silico\u003c/em\u003e binding evidence.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eADMET Profiling and Optimization\u003c/strong\u003e \u003cp\u003eConduct absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses to assess pharmacokinetic properties and optimize drug-likeness.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNeuroprotective Efficacy Testing\u003c/strong\u003e \u003cp\u003eInvestigate the compound's ability to mitigate oxidative stress and cholinergic dysfunction in relevant neuronal or cellular models.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMedicinal Chemistry Optimization\u003c/strong\u003e \u003cp\u003ePerform structure-activity relationship (SAR) studies and targeted chemical modifications to enhance binding affinity, metabolic stability, and bioavailability.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e\u003cem\u003eIn Vivo\u003c/em\u003e Evaluation\u003c/strong\u003e \u003cp\u003eAssess therapeutic efficacy and safety in animal models of neurodegeneration to establish preclinical relevance.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThese steps are crucial to translating computational predictions into viable therapeutic strategies for neurodegenerative disorders such as Alzheimer's disease.\u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Dataset Acquisition and Preprocessing\u003c/h2\u003e \u003cp\u003eIn this study, a total of 3,014 plant-derived compounds were collected by downloading the NPACT (1,541 compounds), HIT (486 compounds), and HIM (987 compounds) collections from the COCONUT (COlleCtion of Open Natural prodUcTs) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://coconut.naturalproducts.net/\u003c/span\u003e\u003cspan address=\"https://coconut.naturalproducts.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed: 1.11.2025). These molecules were computationally evaluated against two therapeutically relevant targets GPX4 and AChE, to identify potential dual-target inhibitors involved in oxidative stress and neurodegeneration pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Molecular Docking Studies\u003c/h2\u003e \u003cp\u003eAll molecular docking studies were performed using Maestro 14.5 (Schr\u0026ouml;dinger, New York) with the aim of identifying dual-target compounds potentially useful against Alzheimer\u0026rsquo;s disease, acting as both Acetylcholinesterase (AChE) inhibitors and Glutathione Peroxidase 4 (GPX4) modulators. For this purpose, the crystal structures of AChE (PDB ID: 7D9Q) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and GPX4 (PDB ID: 7U4I) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] were downloaded from the Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.rcsb.org\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and prepared using the Protein Preparation module integrated within the Maestro software suite. Water molecules in the crystal structures were removed, hydrogen atoms were added, bond orders were adjusted, and energy minimization was performed using the OPLS4 force field.\u003c/p\u003e \u003cp\u003eA grid map of 20 \u0026Aring; on each side was generated in the active regions of the proteins. Donepezil (for AChE) and PKUMDL-LC-102 (for GPX4) were selected as reference compounds. The ligands and reference compounds were prepared with the LigPrep software for molecular docking studies. The ligands were docked into the active sites of the 7D9Q and 7U4I PDB-encoded proteins 50 times at standard precision (SP) using Glide.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Statistical Analysis\u003c/h2\u003e \u003cp\u003eTo enable an objective comparison of compounds exhibiting dual affinity toward GPX4 and AChE, docking scores (X) were standardized using Z-score normalization, a method widely applied in inverse docking and target ranking to minimize target-specific scoring bias [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe raw docking scores (kcal/mol) for each target were independently standardized using Z-score transformation. The Z-score (\u003cem\u003eZ\u003c/em\u003e) for each raw docking score (\u003cem\u003eX\u003c/em\u003e) was calculated as:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJYAAAA6CAYAAABS36B3AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAoXSURBVHhe7dxdbBRVH8fx73a3L+y2lEJKCbQo0KWUaglNSS+0KGIwRoiYGIEUYgwRbEzBumC8UWuq1hAvCJi+XBhjJOiFmjQx+AKxCBVTWrQW2bZb6Rutm7rd0k5pu53uznkuHp9NZnyQvuy2WzifZG7OOf9JmvnNzJmzMzUJIQSSFGJRxgZJCgUZLCksZLCksJDBksJCBksKCxksKSxMcrlh6vx+P3/++SdjY2NERf333BRCYDabSUlJIT4+HkVR8Hg8aJqm609NTSU2Ntawx7uPDNY09PX1UVZWhtPpZGJiArPZjN/vJz09naNHj5KRkcG3335LeXk5Q0NDBAIB4uLisNvtvP322yxdutS4y7uODNY0TExM4Ha7GR4epqSkhEuXLrF582beffddVqxYQWxsLOPj4xw7dozq6mpeeeUVcnNziYuLIzU1FYvFYtzlXUfOsaYhOjqalStXkpWVxd69e7FYLPT29mI2m4O3uWvXrtHb28vx48fZu3cv69at4/77778nQoUM1szl5eWRlpaGx+OhpqYGgD/++INTp06xY8cOHn74YWPJPUEGa4ZSUlJ44oknUBSFH374gcbGRsrLy9m0aRNPPfWUcficGxsbw+fzGZsBgg8aoSDnWCHw66+/smfPHqKjo8nKymLLli3s378/4m57g4ODfPzxxyxbtoxnn32W6OhoAMbHx/nxxx9ZsGAB+fn5xrJpkVesEMjIyGDbtm243W5sNhsvvPBCxIUKoLW1lU8++YTm5mbd1amtrY2ioiK+/vpr3fiZkMEKgaioKEwmE4FAgP7+fm7cuGEcEhFaWlrw+XxkZWXp1tKcTicmk4m8vDzd+JmQwZohTdP46KOP8Pv9ZGdn43Q6uXjxonHYpGiaRmdnJ06nc1LbtWvXcLlcKIpi3NU/jI6O0traSmJiIqtXrw62BwIBGhoaSEhIIDMzU1czEzJYM/T555/T3t7OkSNH2LlzJ3/99ReXL19maGjIOPSONE2jo6OD5ubmSW8ul4vh4WHjrv7B6/Xy+++/k56ezn333RdsHx4epqmpieXLl7NixQpdzYwIadpOnz4tiouLRXd3txBCiMuXL4vs7GyRl5cnamtrjcPvSNM0oSiKGBgYmPQ2ODgoVFU17uofamtrxQMPPCBKSkqE3+8Ptjc2Nor169cLh8OhGz9T8oo1TefPn+e7777jxRdfJC0tDYC1a9fy+OOP09LSQm1tLRMTE8ayf2UymUhISCApKWnSW2JiYvDp7naEEFy/fh2fz8eaNWswm83BvqtXr+L3+8nJyUFRFGpqaiZ1a70TGaxpqKmp4dSpUzgcDt28JDExkdzcXGw2G7W1tXR1denq5sro6GjwSTAmJibYPjY2Rk1NDTExMWzYsIHW1lYuXbrEggULdPXTMWfBEkLg9/sntc01TdPo6+ujoaGBsrIyDh48iM/n0x0kIQQDAwNMTEywePFirly5QnV1NVevXp3WfCuUFEWhubkZn89HR0cHg4OD9Pf3U1VVxW+//QZAf38/TU1NZGZm3vEKOBlztkBaV1dHY2Mj4+PjALp1n/89vquqit1uZ+vWrSH5Y6dLURROnjzJN998g9VqxWw2o6oqW7duxeFwEBsby+joKBUVFVRXV2O1WjGZTPj9fsbHxykuLubpp58OvmIz2+rr64MLtmlpadjtdm7evEl+fj4bNmygrKyMhIQE7HY7r776qu6Ema45CZaqqhw4cIDvv/+e7OxsUlJSsNls8PcPvNevX6e+vh6r1UpJSQkFBQURueA4H2iaxmeffUZpaSklJSXs3r0bn89HdHR0cK4lhEBV1dC+J2aczc8Gp9Mp8vPzxZEjR8TAwICuz+v1iuLiYmG320VFRYXw+Xy6fmlqxsbGhMPhEHl5eeLKlSvG7rCZk2tzXV0dGzdu5PXXXycpKSnY3t/fT2lpKV9++SUFBQUUFBSE9iy6Bw0PD+N0Olm+fLluYTTcZj1Y4+PjtLS0sH37dpYsWRJsHxkZobKyki+++IIdO3ZQWFhIQkKCrlaaut7eXtrb28nMzGTRokXG7vAxXsLC7datW+Ls2bPC4/EE21RVFVVVVWLVqlXi+eefF263W1cjTd/AwIA4e/as6OzsNHaF1axP3jVNQ9M0zGYzJpMJTdP49NNPKS0txW638+GHH7JmzRpjmTTPzHqwjM6dO8fLL79McnIyJ06cICcnxzjkX/X19fHee+/R1tamW1H+N0IIYmJi2LdvH88884yxWwqBOQ3Wzz//TFFREZqmUVFREXxtIxAIoGkaFosFk8lkLNPRNA2v13vbtyJvJyoqiiVLlhAXF2fskkJgzoLV1NTEwYMHGRwcpKysjJ07dwb7Ll68yI0bN9i+fTsLFy7U1c0Fn89Hd3f3nC1wziZN00hKSiI5OdnYNSVzEqzu7m6KiopwuVwcPnyYl156SddfWVmJzWajoKAgIg5mS0sLBw4cCC7i3s1GR0fZvXs3hYWFxq4pmfVg9fb28tZbb1FTU0NxcTGFhYW6uVF7ezuVlZU8+eSTbNmyRVf7/4yMjHDhwgU8Hs+UQmg2m8nJySEjI8PYJYXA5I9ECHi9Xo4dO8aZM2fYs2cP+/fv14VKVVW++uorbt26xfr163W1t6OqKj09PXR2dk566+jooKuri5s3bxp3J4WKbvEhjEZGRsT7778vVq9eLQ4dOiT6+/uDfT6fT9TX14s333xT5Obmig8++EBXK80/s3IrFEJw+vRpjh49ysjICLm5uSxdupRAIAB/f7Le1dVFc3Mz6enpnDx5kkcffdS4G2kembVg9fT00NPTg8ViQVXVYKj+x2KxYDabiY+PZ9WqVVitVl2/NL/MSrCke8+sTt6le4cMlhQWMlhSWMg5VoTSNI2xsTGMh8dsNhMXF3fH31DnmgxWhFEUhfPnz9Pa2kpfXx8WiwUhBBMTEwQCAaxWK4WFhaxcudJYGlFksCJIa2srJ06c4MKFC6Snp6MoCk1NTSxcuJAHH3wQm82G1Wrltddew263G8sjigxWhHC73Rw+fJihoSEcDgfZ2dkoikJVVRU//fQTb7zxBg899BAWiwWbzRbxt0I5eY8AgUCA8vJy2traeOedd9i2bRvLli1j7dq1PPbYY6iqiqqqLFq0iPj4+IgPFTJYkcHlcnHu3DkeeeQRNm7cqOsbGhpiZGQk+GHvfCGDFQEaGhrwer1s3rxZ92Guqqq4XC5iY2ND+y+GZoEMVgQYHh7GarWSmpqqa3e73fzyyy9s2rRp0q8RRQoZrAiwePFiLBaL7kVFv9/PmTNn8Hg87Nq1S/cN5nwgnwojgNvt5tChQ6xbt46CggJMJhN1dXVUV1fz3HPPsWvXLmNJxJPBihBtbW0cP36c5ORkhBAoisK+ffum/DlcpJDBijB+v5+oqKgpvb8fiWSwpLCY36eFFLFksKSwkMGSwuI/ebxLA+KQXTkAAAAASUVORK5CYII=\" width=\"150\" height=\"58\"\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003e\u0026micro;\u003c/em\u003e and \u003cem\u003eσ\u003c/em\u003e represent the mean and standard deviation of the docking scores, respectively (Table S2.). Crucially, these parameters (\u003cem\u003e\u0026micro;\u003c/em\u003e and \u003cem\u003eσ\u003c/em\u003e) were calculated exclusively across the pool of the 68 initial dual-target compounds for the respective protein, ensuring the Z-score reflects the relative affinity within the highly selective candidate subset.\u003c/p\u003e \u003cp\u003eSubsequently, a Composite Binding Score (\u003cem\u003eC score\u003c/em\u003e) was derived for each molecule by summing the individual normalized \u003cem\u003eZ\u003c/em\u003e-scores for GPX4 (Z\u003csub\u003eGPX4\u003c/sub\u003e) and AChE (Z\u003csub\u003eAChE\u003c/sub\u003e):\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"282\" height=\"43\"\u003e\u003c/p\u003e \u003cp\u003eThis composite scoring system, a robust approach known as \u003cb\u003econsensus scoring\u003c/b\u003e in multi-target drug design [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], provided a statistically robust framework for ranking the dual-active compounds based on their overall combined affinity, which guided the selection of the two highest-ranked candidates for subsequent molecular dynamics simulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Molecular Dynamics Simulations (MD)\u003c/h2\u003e \u003cp\u003eThe two most promising dual-target compounds identified by the molecular docking results, NPACT00189 and NPACT01210, were subjected to 250 ns MD simulations. These simulations were carried out to thoroughly investigate the stability and dynamic trajectory of the ligand\u0026ndash;protein interactions within the GPX4 and AChE complexes. The complexes were analysed for parameters, including Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF). The Desmond program (Maestro 14.5) was employed for all MD simulations. Simulations were initiated with the best pose obtained from the molecular docking studies.\u003c/p\u003e \u003cp\u003eThe 250 ns MD simulations were conducted using the NPT ensemble for compounds NPACT00189 and NPACT01210 against 7D9Q and 7U4I. In these simulations, RMSD values of the ligands and proteins, as well as ligand\u0026ndash;protein interactions, were evaluated. The protein-ligand complexes were immersed in a solvent by placing them in an octahedral box containing TIP3P water molecules, ensuring a minimum distance of 10 \u0026Aring; between the protein-ligand complexes and the box edges. The systems were rendered chemically neutral by adding Na⁺ and Cl⁻ ions, and the ionic concentration was adjusted using a 0.15 M NaCl solution. Desmond's standard relaxation protocol was employed. The Nose\u0026ndash;Hoover chain algorithm was used to maintain the temperature at 300 K, and the Martyna\u0026ndash;Tobias\u0026ndash;Klein algorithm was applied to regulate the pressure at 1.01325 bar [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.5. MM/GBSA Calculations\u003c/h2\u003e \u003cp\u003eThe MM/GBSA method was employed to estimate the binding free energies of the protein\u0026ndash;ligand complexes. All calculations were performed using Schr\u0026ouml;dinger\u0026rsquo;s Prime MM-GBSA module. Complexes exhibiting the most favorable binding poses obtained from the molecular docking studies were selected for subsequent analysis.\u003c/p\u003e \u003cp\u003eThe binding free energy (ΔG\u003csub\u003ebind\u003c/sub\u003e) was calculated according to the following equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\Delta\\:}{G}_{\\text{bind}}={\\Delta\\:}{E}_{\\text{MM}}+{\\Delta\\:}{G}_{\\text{solv}}+{\\Delta\\:}{G}_{\\text{SA}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere ΔE\u003csub\u003eMM\u003c/sub\u003e represents the molecular mechanics energy, ΔG\u003csub\u003esolv\u003c/sub\u003e denotes the solvation free energy, and ΔG\u003csub\u003eSA\u003c/sub\u003e corresponds to the surface area\u0026ndash;dependent free energy contribution. All binding free energies are reported in kcal/mol [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this molecular modeling and dynamics study, a comprehensive virtual screening of plant-based compounds identified NPACT00189 as promising dual-target hit compounds against Alzheimer's disease by simultaneously targeting GPX4 and AChE. Docking analysis indicated high binding affinity towards both target proteins, exceeding that of the reference GPX4 activator PKUMDL-LC-102. Molecular Dynamics simulations confirmed that NPACT00189 exhibited stable binding in both the AChE active gorge and the GPX4 allosteric site, maintaining key interactions throughout the 250 ns trajectory, thus supporting its potential as a balanced dual-target modulator. Conversely, while NPACT01210 showed good stability within the AChE complex, its significant displacement from the GPX4 allosteric pocket after 47 ns suggests that its activity may be predominantly restricted to AChE inhibition. These results provide computational evidence that NPACT00189 is a superior lead candidate for the development of novel plant-derived therapies addressing the complex multi-factorial pathology of Alzheimer's disease. However, the promising dual-target profile of NPACT00189 must be rigorously validated through subsequent \u003cem\u003ein vitro\u003c/em\u003e enzyme inhibition assays and \u003cem\u003ein vivo\u003c/em\u003e studies to confirm its efficacy, bioavailability, and therapeutic potential.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAD\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAlzheimer\u0026rsquo;s disease\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eACh\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAcetylcholine\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAChE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAcetylcholine Esterase\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eChEIs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eCholinesterase inhibitors\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCOCONUT\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eCOlleCtion of Open Natural prodUcTs\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eGPX4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eGlutathione peroxidase 4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHIM\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHerbal Ingredients In-Vivo Metabolism Database\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHIT\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHerbal Ingredients' Targets Database\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMolecular Dynamics\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMM/GBSA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMolecular Mechanics / Generalized Born Surface Area\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNPACT\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNaturally Occurring Plant-based Anti-cancer Compound-Activity-Target Database.\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eRMSD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eRoot-Mean-Square Deviation\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eRMSF\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eRoot-Mean-Square Fluctuation\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSAR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eStructure-activity relationship\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Materials:\u0026nbsp;\u003c/strong\u003eTable S1 and S2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, S.R.O.D.; methodology, S.R.O.D, M.A.A.; software, M.A.A.; validation, M.A.A.; formal analysis, S.R.O.D.; investigation, S.R.O.D.; resources, M.A.A.; data curation, M.A.A.; writing\u0026mdash;original draft preparation, S.R.O.D.; writing\u0026mdash;review and editing, M.A.A.; visualization, S.R.O.D.; supervision, M.A.A.; project administration, S.R.O.D.; funding acquisition, S.R.O.D. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study, including the article processing charge (APC), was funded by the Scientific Research Projects Unit (BAP) of Inonu University under the grant number TSA-2025-4366.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The data used in this study were obtained by downloading the \u003cstrong\u003eNPACT\u003c/strong\u003e, \u003cstrong\u003eHIT\u003c/strong\u003e, and \u003cstrong\u003eHIM\u003c/strong\u003e collections available within the \u003cstrong\u003eCOCONUT (Collection Of Open Natural Products)\u003c/strong\u003e database (https://coconut.naturalproducts.net/ Accessed: 1.Sep.2025). All compound sets were retrieved in standardized formats and locally processed for virtual screening.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e During the preparation of this manuscript, the author(s) used ChatGPT-5.1 (OpenAI) and QuillBot for the purposes of grammar refinement, language editing, and sentence structure improvements. The authors have reviewed and edited the output and take full responsibility for the content of this publication. The authors have reviewed and edited the output and take full responsibility for the content of this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHuang, L. K., Kuan, Y. C., Lin, H. W. \u0026amp; Hu, C. J. 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Benzoxazole Derivatives as Dual p38α Mitogen-Activated Protein Kinase and Acetylcholinesterase Inhibitors: Design, Synthesis, and Evaluation for Alzheimer's Disease and Cancer Therapy. \u003cem\u003eChemMedChem\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (22), e202500669. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cmdc.202500669\u003c/span\u003e\u003cspan address=\"10.1002/cmdc.202500669\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Alzheimer disease’s, AChE, ferroptosis, GPX4, in silico, molecular docking, molecular dynamics simulations","lastPublishedDoi":"10.21203/rs.3.rs-8748563/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8748563/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/Objectives: \u003c/strong\u003eAlzheimer’s disease (AD) is primarily characterized by cholinergic dysfunction, for which acetylcholinesterase (AChE) inhibition remains the mainstay of symptomatic treatment. However, additional hypotheses such as ferroptosis—an iron-dependent form of regulated cell death—have gained prominence in explaining disease progression. Glutathione peroxidase 4 (GPX4), a critical antioxidant enzyme, plays a protective role by suppressing ferroptotic pathways. In this context, identifying phytochemicals capable of simultaneously inhibiting AChE and activating GPX4 may provide a dual therapeutic benefit. This study aimed to identify such dual-acting compounds through a structure-based virtual screening approach.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A total of 3,014 natural compounds were collected from three curated databases: NPACT, HIT, and HIM. Molecular docking was performed against GPX4 (7U4I) and AChE (7D9Q). Compounds demonstrating high affinity for both targets were shortlisted. Z-score normalization and statistical ranking were used to select the best two dual-target compounds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Out of 3,014 compounds, 68 showed dual-binding potential. Among these, NPACT00189 (docking scores: −6.720 kcal/mol for GPX4; −8.983 kcal/mol for AChE) and NPACT01210 (docking scores: −5.813 kcal/mol for GPX4; −9.640 kcal/mol for AChE) were identified as top candidates based on docking scores. Molecular dynamics (MD) simulations were conducted for both compounds for 250 ns on the AChE binding site and the allosteric site of GPX4. The results indicated that NPACT00189 maintained stable interactions throughout the simulation period at both targets, indicating its dual-targeting potential.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: NPACT00189 represents promising dual-target for further investigation in AD therapy. Its potential requires confirmation through comprehensive \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies.\u003c/p\u003e","manuscriptTitle":"Dual Targeting of AChE Inhibition and GPX4 Activation by Plant-Derived Compounds for the Treatment of Alzheimer’s Disease: Insights from Molecular Docking and Molecular Dynamics Simulations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 13:04:12","doi":"10.21203/rs.3.rs-8748563/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":"40045a86-31f5-43d9-9452-3e991ea9a6e6","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62995951,"name":"Biological sciences/Biochemistry"},{"id":62995952,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":62995953,"name":"Biological sciences/Drug discovery"},{"id":62995954,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-30T02:39:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 13:04:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8748563","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8748563","identity":"rs-8748563","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}