Molecular Docking Studies of Botanical Beverage Mix Berries (LIFEGREENTM) against Breast Cancer Cells from Targeted Protein 1QQG, 7B5Q & 7B5O & Uterine Fibroid from Targeted Protein 2AYR, 6T41 & 3GRF

Fibroids, also called leiomyomas or myomas, are communal tumors of the muscle or uterine wall that affect about 20% of females who are of reprodu c- tive age. They can look as if singly or in clusters, and they often cease to grow after menopause. Fibroids can be classified as intramural, sub serosal, p e- dunculated, or submucosal based on where they are positioned in the uterus. Although fibroid s are benign, they can grow quickly and cause a range of symptoms, such as pelvic pressure, heavy menstrual flow, and infertility. As a result, fibroids are a main reason behind hysterectomy surgeries. The majori- ty of cases of breast cancer are ductal and lobular cancers, making it the second utmost common cancer in women international. Gene mutations like those in BRCA1 or BRCA2 knowingly raise the risk of breast and other ca n- cers, typically with an earlier cancer onset. Cancer risk is influenced by a complex interplay of genetic abnormalities, environmental factors, and lifestyle selections. Further research into these relations is domineering. Although they are common in uterine leiomyomas, especially multiple leiomyomas, MED12 mutations do not significan tly correlate with tumor size. These mutations have also been noticed in smooth muscle tumors and leiomyosarcomas, two How to cite this paper: Shahieda Lazaroo Bt Zurrein Shah Lazaroo, U., Sivanananthan, N. and How, C.K. (2024) Molecular Docking Studies of Botanical Bev erage Mix Berries (LIFEGREENTM) against Breast Cancer Cells from Targeted Protein 1QQG, 7B5Q & 7B5O & Uterine Fibroid from Targeted Protein 2AYR, 6T41 & 3GRF. Computational Mole- cular Bioscience, 14, 59-123. https://doi.org/10.4236/cmb.2024.142004 Received: April 24, 2024 Accepted: June 10, 2024 Published: June 13, 2024 Copyright © 2024 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 60 Computational Molecular Bioscience other types of uterine cancer. The identification of MED12 mutations as the sole genetic abnormality originates in leiomyomas raises the opportunity of a role in the genesis of cancer. 10% - 15% of women who are of reproductive age have endometriosis, which grants serious difficulties because of its chronic nature and range of clinical symptoms. Even after effective surgeries, issues reoccur often, adding to the enormous financial burden. The effects of MED12 mutations have been experiential in recent studies examining the molecular causes of endometriosis -associated infertility, which have shown anomalies in cellular connections and signali ng cascades. Computational techniques were used in this study to investigate LifeGreen TM’s potential to prevent uterine fibroids and breast cancer. The efficacy of LifeGreen TM as a preventive measure or a treatment for common gynecological matters was examined and modeled. We investigated the mechanisms underlying Life- GreenTM’s benefits in the treatment of uterine fibroids and breast cancer using computational techniques. Our research contributes to our understanding of its potential therapeutic benefits for women’s health.

Uterine Fibroid, Breast Cancer, Molecular Docking, IRS Protein, BRCA1, BRCA2, MED12-a, Endometriosis 1. Introduction 1.1. LifeGreen™ LifeGreen™ Cactus Powder is known as a highly concentrated cactus extract which made using 22 different types of fruit and vegetable extracts, Italian mixed berries, and TRUEBROC® broccoli seed extract, a US-patented ingredient, Oxxynea® is a popular French health drink. According to studies, cactus polysaccharides having the ability to boost immunity and inhibit abnormal cell developments when con- sumed over time. In addition, Truebroc® broccoli seed extract promotes aberrant cell death, reduces abnormal cell blood supply, and prevents abnormal cell repro- duction and spread. Figure 1 shows the packaging of the LifeGreen™ Beverage [1]. 1.2. Ingredients and Benefits LifeGreen™ contains Italian Premium Mixed Berries (Blueberry, Blackcurrant, Raspberry, Elderberry, Red Grape, Strawberry, Cranberry), Cactus Powder, Oxxy- nea® (Green Tea Extract, Red Grape Extract, White Grape Extract, Bilberry, Car- rot, Grapefruit, Papaya, Pineapple, Strawberry, Apple, Apricot, Cherry, Ora nge, Broccoli, Green Cabbage, Onion, Garlic, Olive, Cucumber, Blackcurrant, Tomato, Asparagus), TRUEBROC ® broccoli seed extract, and immune deficiencies include fatigue and weakness, allergies, and the need to restore immunity. Moreover, Life- Green™’s ingre dients, particularly Cactus Powder, have been extensively re- searched and studied for their anticancer effects. Figure 2 shows the picture of LifeGreen™ Beverage drink [1]. U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 61 Computational Molecular Bioscience Figure 1 . Lifegreen™ Beverage sachet and packaging [1]. Figure 2 . Lifegreen™ Beverage drink [1]. 1.3. Cancer Cell Growth The body creates molecules are known as growth factors, which govern cell divi- sion. Growth factors occur in a number of types and each operate s differently. Some growth factors advise cells on how to specialize and what type of cell they should become. Some cause cell division and proliferation to generate new cells. Cells can be told to cease growing or die. Growth factors operate by binding to cell surface receptors. This sends a signal to the cell’s inside, initiating a sequence of complex chemical reactions. There are multiple different growth factors [2] . These include epidermal growth factor (EGF), vascular endothelial growth factor U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 62 Computational Molecular Bioscience (VEGF), platelet -derived endothelial growth factor (PDGF), and fibroblast growth factor (FGF), which all regulate cell growth. Each growth factor works by attaching to it s corresponding cell surface receptor. For example, epidermal growth factor (EGF) interacts with the EGFR. Tyrosine Kinases are chemical messengers (enzymes) that control cells ’ capacity to divide and grow. Similar to an “ on-off” switch feature , Tyrosine kinase is activated when a growth fac- tor attaches to a cell ’s surface. This will prompt cell division as shown in Figure 3 [2]. 1.4. Uterine Fibroids Fibroids, also known as leiomyoma or myoma, are frequent tumors that develop in the uterine wall or muscle. Fibroids in women of their reproductive years can present as single or multiple growths. Fibroids are uncommon in women who have not yet started menstruation, although they afflict around 20% of women of reproductive age. Usually, growth slows after menopause [3] . Fibroids can be classified into various types based on where they exist. The submucosal fibroid is a fibroid that usually develops inside the uterus while intramural fibroid is a fi- broid that develops within the musculature of the uterine wall. Moreover, su b- serosa fibroid is known as a uterine fibroid that protrudes from the body. Lastly, a fibroid with a stalk that extent from the uterus into the pelvis or else disco v- ered inside the inner uterine cavity and extends through cervix is known as p e- dunculated fibroid. Figure 4 shows the locations of the various types of fibroids [4]. Despite their benign nature, they can grow rapidly and dramatically [5] . They produce heavy and irregular menstrual bleeding (HMB), which leads to severe anemia, dysmenorrhea, pelvic pressure and discomfort, urinary incontinence, dyspareunia, infertility, premature labor, and recurrent early and late pregnancy losses [6]. More than 70% of women have UFs, with only around 30% experiencing Figure 3 . The cancer cells growth factor that affects the body [2]. U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 63 Computational Molecular Bioscience Figure 4 . The common locations for uterine fibroid to occur [4]. symptoms, making UFs the most common clinical cause for hysterectomy, which removes a woman’s capacity to produce early [7]. 1.5. Breast Cancer Cells (IRS-1) Breast cancer is the world ’s second most frequent illness, affecting more women than any other malignancy. The two most common types of breast cancer are ductal and lobular. In situ (localized to a single location), ductal and lobular cancers account for 85 % - 90% and 8% of all breast cancers, respectively. In a d- dition, aggressive inflammatory breast cancers exist, as do invasive ductal and lo- bular tumors. Although chemotherapy has been demonstrated to improve breast cancer patients’ survival rates, a significant minority of individuals only have a brief response to the treatment before succumbing to metastatic disease. IRS1 has been shown to enhance breast cancer cell growth rather than prevent ing metastasis [8]. Figure 5 shows the process of IRS- 1 occurring before metas tasis happen in which responsible for the growth of breast cancer cell [9]. 1.6. BRCA 1 & BRCA 2 People protecting injurious variants in BRCA1 or BRCA2 genes aspect signifi- cantly higher risks of emerging various cancers, together with breast, ovarian, fallopian tube, and primary peritoneal cancers [10] . The in cidence of these m u- tations significantly rises the lifetime risk of cancer onset, with pretentious people often facing earlier age of cancer diagnosis equated to the general popula- tion. While BRCA mutations are sturdily associated with increased cancer risk, the extent of risk variability amongst carriers is inclined by various factors [10] . These issues include environmental contacts, lifestyle choices, hormonal effects, and genetic modifiers that may interrelate with BRCA mutations to modulate cancer susceptibility. In spite of extensive research, some of these factors remain somewhat characterized, highlighting the need for further investigation into the complex in terplay between genetic and environmental determinants of cancer risk [11]. U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 64 Computational Molecular Bioscience Figure 5 . The process of IRS -1 occurring before metastasis happens causes breast cancer cell growth. 1.7. MED12-a Tumors did not exhibit more than one mutation, and previous research did not address the MED12 mutation status of multiple leiomyomas in one patient. Multiple uterine leiomyomas appeared to have a higher incidence of MED12 mutations compared to single uterine leiomyomas (72.73% versus 59.26%), a l- though this difference was not statistically significant [12]. Moreover, patients with multiple leiomyomas exhibited smaller mean sizes of leiomyomas signifi- cantly, consistent with previous research. Nearly twofold difference in MED12 mutation frequency between multiple and single uterine leiomyomas from a c o- hort of 122 patients. However, they could not establish a significant association between MED12 mutation and tumor size [12] . Therefore, larger sample sizes are required to evaluate the relationship between MED12 mutation frequency and the number or size of uterine leiomyomas. MED12 mutation has also been detected in other uterine tumors such as leiomyosarcoma s and smooth muscle tumors of uncertain malignant potential, but not in tumors of other organs. I n- terestingly, breast fibroadenoma also harbored highly frequent MED12 mut a- tions. Whole exome sequencing revealed no genes other than MED12 mutation in MED12 mutation-positive and -negative leiomyomas, suggesting that MED12 mutation alone may be sufficient for leiomyoma tumorigenesis [12]. 1.8. Endometriosis Endometriosis is a multifaceted gynecological state affecting 10% - 15% of r e- productive-age females and around 70% of women facing persistent pelvic pain. While the ovaries and pelvic peritoneum are the primary sites of endometriotic U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 65 Computational Molecular Bioscience lesions, they can also patent in various other locations inside the body [13]. The etiologic of endometriosis- associated pain remains poorly unspoken, with i n- flammation broadly believed to play a significant role. Despite fruitful surgical interventions, recurrence of symptoms is communal, underscoring the chronic nature of the condition. Endometriosis poses a substantial economic burden, particularly in countries like India, with costs estimated a t approximately 1 to 2 lakhs per affected woman. The participation of structures such as the uterosacral ligaments, posterior vaginal wall, rectovaginal space, intestines, and urinary sy s- tem is frequently experiential in endometriosis cases. Various studie s have e x- plored the molecular mechanisms underlying endometriosis -associated infertili- ty, highlighting factors such as apoptosis, cell cycle alterations, and oxidative stress in granulosa cells. Moreover, recent research endeavors have sought to elucidate the role of MED12 mutations in endometriosis pathogenesis, revealing disruptions in cellular interactions and signaling pathways [13]. 2. Methodology LifeGreen™ uses proteomic computing to identify compounds that can interact with IRS-1 (breast cancer cells) as well as BRCA 1 and 2 - 1QQG, 7B5Q & 7B5O and endometriosis and mutated Med -a (uterine fibroid) -2AYR, 3GRF & 6T41 proteins [14]. Furthermore, additional research and investigations on each effec- tive molecule with the highest binding energy and its advantages to human health. Figure 6 shows the general methodology of this research while Figure 7 Figure 6 . Overview of the whole methodology U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 66 Computational Molecular Bioscience until Figure 1 0 shows the in-depth methodology. 2.1. Phase 1: Metabolomics Analysis In Figure 7 , in chromatography process, separation was performed using The r- mo Scientific C18 column (Acclaim TM Polar Advantage II, 3 × 150 mm, 3 um particle size) on an UltiMate 3000 UHPLC system (Dionex) [15] . Gradient elu- tion was performed at flow rate of 0.4 ml/min and 40˚C column temperature using H2O + 0.1% Formic Acid (A) and 100% ACN (B) with 22 minutes total run time. The injection volume o f sample was 5 ul. The gradient started at 5% B (0 - 3 min); 80% B (3 - 10 min); 80% B (10 - 15 min) and 5% B (15 - 22 min). For mass-spectrophotometry, sample is analyzed with positive ionization parameter [15], Table 1 . In data processing steps, the accurate mass data of the molecular ions, pr o- vided by the TOF analyzer, were processed by Compass Data Analysis software Figure 7 . Phase 1: Metabolomics Analysis. Table 1 . Positive mode ionization. Acquisition Parameter Source Type ESI Ion Polarity Positive Set Nebulizer 2.0 Focus Active Set Capillary 4500 V Set Dry Heater 300˚C Scan Begin 50 m/z Set End Place Offset −500 V Set Dry Gas 8.0 l/min Scan End 1500 m/z Set Collision Cell RF 200.0 Vpp Set Divert Valve Waste U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 67 Computational Molecular Bioscience (Bruker Daltonik GmbH). Further process by using metfrag (In silico fragmen- tation for computer assisted identification of metabolite mass spectra) in order to pull out the list of compound present in each peak presented by LC -MS/TOF [16] gi-bin/portal.py#welcome. 2.2. Phase 2: Compound Analysis Before proceeding with the analysis, each compound underwent meticulous quality control procedures employing ZoBio by NMR, ensuring the integrity and reliability of subsequent results. Following this initial step, the compounds were subjected to an array of sophisticated analyses aimed at elucidating their properties and functions. Significance testing was employed to discern me a- ningful patterns and deviations within the dataset, shedding light on potential biological implications. Quantitation- pattern recognition techniques were a p- plied to discern quantitative relationships and trends within the data, facilitat- ing a deeper understandin g of compound behavior. Compound assignment methodologies were utilized to accurately identify and classify each co m- pound, ensuring precise cataloguing and characterization. Finally, functional assessment protocols were implemented to assess the biological activities and potential applications of the compounds, providing valuable insights for further research, Figure 8 . 2.3. Phase 3: Protein Analysis During this third phase of the study Figure 9 , an extensive protein analysis was undertaken utilizing resources available on the Protein Data Bank (PDB) https://www.rcsb.org/ website [17]. This involved a meticulous examination of protein structures and relevant data to glean insights into the molecular Figure 8 . Phase 2: Compound analysis. U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 68 Computational Molecular Bioscience Figure 9 . Phase 3: Protein analysis of 1QQG, 2AYR, 3GRF, 6T41, 7B5Q and 7B5O. Table 2 . Breast cancer and uterine fibroid flow. Breast Cancer Uterine Fibroid IRS-1 BRCA1 & BRCA 2 Endometriosis MED a (mutated) IRS1 was found to promote breast cancer cell proliferation XPB & XPD Tissue lining grows outside the uterus CDK 8/Cyclin C. TFIIH 1QQG 7B5Q & 7B5O 2AYR 3RGF & 6T41 underpinnings of the investigated conditions. To augment our understanding, a thorough literature review was conducted. This involved delving into published studies and scientific literature to identify and elucidate the roles of specific pro- teins implicated in breast cancer and uterine fibroids. By synthesizing inform a- tion from various sources, we aimed to pinpoint key proteins associated with these diseases, providing a comprehensive foundation for our research. Table 2 serves as a comprehensive repository of the gathered results pertaining to both breast cancer and uterine fibroids. This tabulated data offers a detailed overview of the proteins identified and their respective implications in the pathogenesis of these conditions. Through meticulous documentation and analysis, we aim to uncover potential biomarkers and therapeutic targets, contributing to the a d- vancement of diagnostics and treatment strategies for breast cancer and uterine fibroids. 2.4. Phase 4 and Phase 5: Protein-Ligand Interactions and Molecular Docking In Figure 1 0, proteomic molecular docking represents a sophisticated computa- tional approach used in the field of proteomics to predict and analyses the U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 69 Computational Molecular Bioscience Figure 1 0. Phase 4: Protein-Ligand Interaction (RPBS) and Phase 5: Molecular Docking. interactions between proteins and other molecules, Achilles Blind Docking Server, https://bio-hpc.ucam.edu/achilles/. This method integrates principles from both proteomics and molecular docking, leveraging computational alg o- rithms to simulate and predict the binding affinity and spatial or ientation of proteins with various ligands, substrates, or inhibitors. The process typically b e- gins with the identification of target proteins of interest through proteomic techniques such as mass spectrometry or protein microarrays. Once the target proteins are identified, molecular docking algorithms are employed to simulate the binding interactions between these proteins and small molecules, peptides, or other proteins. These docking algorithms use complex scoring functions and search algorithms to explo re the conformational space and predict the most energetically favorable binding poses between the proteins and their ligands. By analyzing these predicted binding conformations, researchers can gain valuable insights into the molecular mechanisms underlyi ng protein-ligand interactions, including the identification of key binding residues and structural determinants. Proteomic molecular docking holds significant promise for various applications in drug discovery, structural biology, and systems pharmacology . It enables the screening of large compound libraries to identify potential drug candidates or lead compounds that modulate the activity of target proteins implicated in di s- eases. Additionally, it facilitates the elucidation of protein -protein interaction networks and signaling pathways, providing valuable insights into complex bio- logical processes and disease mechanisms. 3. Results 3.1. LC-TOF-MS In Figure 1 1, the chemical composition of LifeGreen TM samples, LC- TOF MS analysis emerged as a pivotal investigative tool, yielding a comprehensive co m- pound spectrum graph delineating the peaks indicative of molecular entities U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 70 Computational Molecular Bioscience Figure 1 1. Shows the compound spectrum graph and its peaks. present within the samples. Notably, the analysis revealed approximately 140 distinct compounds, with varying degrees of detectability attributable to the i n- herent limitations posed by sample composition and instrumental sensitivity. Within this array, certain compounds wer e readily discernible, while others r e- mained undetected, a phenomenon attributed to the relatively sparse population of compounds within the samples. In our examination of the compound spectrum graph, each peak was charac- U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 71 Computational Molecular Bioscience terized by its corresponding retent ion time (RT) in minutes, intensity, si g- nal-to-noise ratio, maximum mass -to-charge ratio (m/z), and area under the peak. These parameters collectively provided insights into the abundance, purity, and spectral characteristics of the identified compounds. F urthermore, to au g- ment our understanding of the molecular identities associated with the observed peaks, collision energy (eV) values were assigned to selected peaks, facilitating subsequent tandem mass spectrometry (MS/MS) analyses. To unveil the chemical identities corresponding to the observed peaks, we employed the computational tools afforded by MetFrag. Leveraging the spectral information encapsulated within the LC -TOF MS data, MetFrag facilitated the deconvolution of complex spectra, enabling the retrieval and annotation of puta- tive compounds associated with each peak, Table 3 . Table 3 . Shows the compound spectrum list of LifeGreenTM. # RT [min] Area Int. Type I S/N Chromatogram Max. m/z 1 0.3 14319 MolFeatur 2290 25.4 158.9642 2 1.1 7552 MolFeatur 364 8.1 892.302 3 1.1 24660 MolFeatur 858 4.9 1297.4271 4 1.1 16541 MolFeatur 814 3.9 1135.3787 5 1.1 25047 MolFeatur 2021 7.7 325.1159 6 1.1 13410 MolFeatur 364 3.9 1378.9585 7 1.1 21376 MolFeatur 1240 5.8 973.3256 8 1.1 11438 MolFeatur 889 9.9 487.165 9 1.1 17711 MolFeatur 1216 13.5 649.221 10 1.3 46188 MolFeatur 2300 3.2 125.9873 11 1.3 11554 MolFeatur 595 3.3 153.0319 12 1.4 41690 MolFeatur 2138 7.8 143.998 13 1.4 78569 AutoMS(n) 5811 66.7 AutoMSn (242.0010) 218.9853 14 1.8 50134 AutoMS(n) 9113 116 AutoMSn (147.0776) 147.0777 15 1.9 49668 AutoMS(n) 5860 73 AutoMSn (309.1324) 325.1181 16 1.9 222361 AutoMS(n) 14,913 72.4 AutoMSn (163.0613) 325.118 17 1.9 462886 AutoMS(n) 63,448 664.5 AutoMSn (325.1166) 325.1181 18 1.9 59405 AutoMS(n) 5810 63 AutoMSn (203.0533) 325.118 19 1.9 3860 MolFeatur 2800 49.8 265.0968 20 1.9 781734 MolFeatur 41,601 425.2 325.118 21 2 79661 AutoMS(n) 8555 88.6 AutoMSn (365.1092) 163.0609 22 2 11871 MolFeatur 8484 96.9 365.1103 23 2.1 66073 MolFeatur 1004 10.8 145.0516 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 72 Computational Molecular Bioscience Continued 24 2.1 170832 MolFeatur 1939 21.2 163.0615 25 2.2 188364 MolFeatur 1939 21.2 163.0614 26 2.2 35326 MolFeatur 459 3.7 973.3298 27 2.2 41514 MolFeatur 489 4.5 1054.3589 28 2.2 50877 MolFeatur 684 7.6 1135.3881 29 2.3 14168 MolFeatur 283 3.1 1236.9162 30 2.3 46769 MolFeatur 563 6.3 1216.9174 31 2.4 7917 MolFeatur 984 9.3 497.1519 32 2.4 6183 MolFeatur 612 6.6 198.0778 33 2.4 49982 MolFeatur 534 3 1297.4404 34 2.5 23467 MolFeatur 383 3.3 1378.9711 35 2.5 18898 MolFeatur 345 3.8 1387.9728 36 2.5 11851 MolFeatur 318 3.5 1460 37 2.5 8619 MolFeatur 319 3.8 1469.0016 38 2.6 12885 MolFeatur 1764 13.7 307.0905 39 2.6 29720 AutoMS(n) 7065 70.1 AutoMSn (268.1092) 136.064 40 2.6 4844 MolFeatur 356 4 117.0568 41 2.6 17930 MolFeatur 954 10.4 163.062 42 2.7 24200 MolFeatur 1679 8.2 294.1604 43 2.8 24504 MolFeatur 749 3.3 261.0418 44 2.8 7757 MolFeatur 343 3.9 1423.7855 45 3 44717 AutoMS(n) 6350 70.9 AutoMSn (130.0523) 215.0195 46 3.1 19762 MolFeatur 8840 62.6 230.994 47 3.1 131922 AutoMS(n) 8910 93 AutoMSn (230.9921) 215.0192 48 3.1 316934 AutoMS(n) 24,784 315.4 AutoMSn (407.0478) 215.0193 49 3.1 749494 AutoMS(n) 54,493 349.9 AutoMSn (215.0178) 215.0192 50 3.1 102957 AutoMS(n) 7504 67.5 AutoMSn (193.0361) 215.0192 51 3.1 103373 AutoMS(n) 6945 60.7 AutoMSn (308.0244) 215.0191 52 3.1 95222 MolFeatur 4404 48.9 193.0371 53 3.2 301781 MolFeatur 12,438 138.2 215.0194 54 3.3 697424 MolFeatur 20,160 149.4 215.0191 55 3.7 10392 MolFeatur 1057 8.2 302.0911 56 3.7 8383 MolFeatur 909 7.5 166.0876 57 4.1 8535 MolFeatur 321 3.6 328.1426 58 4.4 11022 MolFeatur 710 4.9 277.1583 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 73 Computational Molecular Bioscience Continued 59 5.4 12378 MolFeatur 548 4.7 460.0383 60 5.4 20326 MolFeatur 925 10.3 358.1004 61 5.4 14422 MolFeatur 737 8.2 196.0475 62 6.4 11266 MolFeatur 873 9.5 311.1273 63 6.5 6705 MolFeatur 361 4 361.1123 64 7 3669 MolFeatur 315 3.5 389.1815 65 7.3 247093 AutoMS(n) 60,926 887.7 AutoMSn (310.1649) 251.0943 66 7.4 3888 MolFeatur 306 3.4 896.4008 67 7.5 5669 MolFeatur 416 4.6 476.2284 68 7.6 1454 MolFeatur 396 6.8 703.351 69 7.6 297019 AutoMS(n) 60,619 549 AutoMSn (589.2497) 295.1332 70 7.6 1613847 AutoMS(n) 294,260 3251.2 AutoMSn (295.1298) 295.1332 71 7.6 27313 AutoMS(n) 5368 44.5 AutoMSn (611.2289) 295.1332 72 7.6 36156 MolFeatur 17,093 379.8 590.255 73 7.6 1860691 MolFeatur 99,937 41.2 295.1331 74 7.7 8045 MolFeatur 5178 44.1 287.0581 75 7.7 94945 AutoMS(n) 9837 82.1 AutoMSn (279.0470) 295.1315 76 7.7 39453 AutoMS(n) 5249 51.3 AutoMSn (449.1033) 295.1315 77 7.7 143129 MolFeatur 6626 22.2 279.0498 78 7.8 39909 MolFeatur 1495 7.3 211.06 79 7.9 5869 MolFeatur 451 10 1156.4916 80 8 6146 MolFeatur 844 6.9 453.2078 81 8 8300 MolFeatur 459 5.1 247.0831 82 8 7534 MolFeatur 1513 16.8 195.0889 83 8.2 11008 MolFeatur 1359 30.2 765.2588 84 8.2 4727 MolFeatur 631 6.7 635.2532 85 8.3 49149 AutoMS(n) 7874 68.1 AutoMSn (362.2406) 362.2436 86 8.3 28410 AutoMS(n) 6319 66.5 AutoMSn (340.2600) 322.2509 87 8.4 16121 MolFeatur 1257 12 481.1332 88 8.6 2674 MolFeatur 317 3.5 787.2362 89 8.7 16551 MolFeatur 846 9.4 141.0545 90 8.8 3726 MolFeatur 1266 4.9 429.1734 91 8.8 103448 AutoMS(n) 12,920 116.5 AutoMSn (475.3227) 475.3273 92 8.8 52265 AutoMS(n) 6608 60.4 AutoMSn (453.3422) 453.3441 93 8.9 11754 MolFeatur 723 8 625.1779 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 74 Computational Molecular Bioscience Continued 94 8.9 76886 AutoMS(n) 11,827 104.2 AutoMSn (566.4229) 566.4304 95 8.9 115283 AutoMS(n) 14,400 107.5 AutoMSn (588.4059) 588.4125 96 8.9 57648 AutoMS(n) 9152 93.9 AutoMSn (283.7180) 588.4104 97 9.1 410677 AutoMS(n) 72,826 766.7 AutoMSn (340.2600) 114.0925 98 9.1 212347 AutoMS(n) 39,912 260.3 AutoMSn (679.5096) 679.5153 99 9.1 175923 AutoMS(n) 28,600 176.1 AutoMSn (701.4907) 701.4972 100 9.1 855268 MolFeatur 32,414 37.6 114.0925 101 9.2 257033 MolFeatur 9413 209.2 701.4972 102 9.2 549469 AutoMS(n) 69,035 633.7 AutoMSn (396.8013) 396.8049 103 9.2 137634 AutoMS(n) 17,220 114.8 AutoMSn (814.5751) 814.5812 104 9.2 122930 AutoMS(n) 15,509 123.3 AutoMSn (792.5921) 396.8049 105 9.3 AutoMS(n) 5379 AutoMS (n): TIC + MS2 (340.2587) 106 9.3 207392 AutoMS(n) 28,786 404.8 AutoMSn (274.2738) 274.2773 107 9.3 50477 AutoMS(n) 6733 71.2 AutoMSn (318.2989) 274.277 108 9.5 7283 MolFeatur 638 6.2 155.047 109 9.5 26104 MolFeatur 12,699 61.1 183.0786 110 9.5 75436 AutoMS(n) 12,781 152.9 AutoMSn (183.0768) 183.0786 111 9.5 34410 AutoMS(n) 7875 97.2 AutoMSn (290.2694) 183.0787 112 9.5 13638 MolFeatur 7844 151.7 290.2727 113 10 18573 MolFeatur 2371 3.6 389.2515 114 10.1 8421 MolFeatur 1406 15.6 304.3024 115 10.1 3945 MolFeatur 618 4.9 318.3028 116 10.4 8374 MolFeatur 998 7.1 188.0479 117 10.4 21705 MolFeatur 2542 3.8 447.2916 118 10.9 12476 MolFeatur 1335 5.2 505.3321 119 11.3 2349 MolFeatur 273 3 235.1688 120 11.5 8109 MolFeatur 621 6 343.2974 121 11.6 25151 MolFeatur 3668 40.8 288.2552 122 11.8 12750 MolFeatur 2517 36.2 225.1943 123 12.3 37695 MolFeatur 1329 14.8 239.1618 124 12.3 15554 MolFeatur 2306 25.6 199.1688 125 12.3 12451 MolFeatur 1074 8.8 209.1528 126 13 4050 MolFeatur 693 3.8 421.2309 127 13.2 25382 MolFeatur 3875 43.1 383.2033 128 13.2 115665 MolFeatur 544 6 506.3295 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 75 Computational Molecular Bioscience Continued 129 13.9 31914 MolFeatur 4401 48.9 425.2118 130 13.9 6244 MolFeatur 835 9.3 441.1855 131 14.2 5321 MolFeatur 726 8.1 370.2006 132 15 277893 MolFeatur 832 6.1 907.7663 133 15 138295 MolFeatur 484 3.9 404.3161 134 15.4 9501 MolFeatur 870 8 398.2321 135 16.2 324760 MolFeatur 933 5.5 758.5641 136 16.3 398562 MolFeatur 1304 6.2 603.5311 137 16.6 54641 MolFeatur 379 3.8 782.5625 138 16.7 257755 MolFeatur 1157 9 756.5476 139 17.3 43661 MolFeatur 317 3.4 897.7209 140 17.3 432817 MolFeatur 1504 10.7 923.74 3.2. MetFrag In Table A1, through the utilization of MetFrag, a comprehensive compilation of compounds was extracted from each designated peak within the compound spec- trum graph generated via LC-TOF MS analysis of the LifeGreenTM samples. These compounds were meticulously identified, characterized, and annotated, facilitating the elucidation of their chemical nature and potential functional attributes [18]. Each identified compound was assigned a canoni cal SMILES (Simplified M o- lecular Input Line Entry System) representation, serving as a concise yet co m- prehensive descriptor of its molecular structure. Additionally, molecular form u- las were determined, encapsulating the precise arrangement of atoms constit ut- ing each compound, thus providing crucial insights into their elemental comp o- sition and stoichiometry [18]. The benefits and potential applications associated with each identified compound were elucidated, leveraging existing knowledge and literature resources. These benefits encompassed a diverse array of domains, including pharmaceuticals, agriculture, food science, cosmetics, and enviro n- mental remediation, among others [18]. By integrating the structural information derived from canonical SMILES no- tation and molecular formulas with the contextual understanding of their fun c- tional properties, a holistic perspective on the chemical constituents present within the LifeGreen TM samples was attained, Table 4. 3.3. LifeGreen™ Compounds 3.3.1. Swiss-ADME The compiled list of 117 compounds extracted from the LC -TOF MS analysis of LifeGreenTM samples underwent comprehensive evaluation via SwissADME, a robust computational platform designed to assess various pharmacokinetic and pharmacodynamic parameters crucial for drug discovery and development [19] . U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 76 Computational Molecular Bioscience Table 4. Shows list of compounds in LifeGreenTM. COMPOUND NAME FORMULA SMILES Phenethyl anthranilate C15H15NO2 C1=CC=C(C=C1)CCOC(=O)C2=CC=CC=C2N D-glutamine C5H10N2O3 C(CC(=O)N)C(C(=O)O)N Citrus red 2 C18H16N2O3 COC1=CC(=C(C=C1)OC)N=NC2=C(C=CC3=CC=CC=C32 )O 1,5-Anhydro-D-fructose C6H10O5 C1C(=O)C(C(C(O1)CO)O)O Bis-D-fructose 2’,1:2,1’-dianhydride C12H20O10 C1C2(C(C(C(O2)CO)O)O)OCC3(O1)C(C(C(O3)CO)O)O Bergaptol C11H6O4 C1=CC(=O)OC2=CC3=C(C=CO3)C(=C21)O Bergapten C12H8O4 COC1=C2C=CC(=O)OC2=CC3=C1C=CO3 Isobergaptol C11H6O4 C1=CC(=O)OC2=C1C(=CC3=C2C=CO3)O Abscisic acid C15H20O4 CC1=CC(=O)CC(C1(C=CC(=CC(=O)O)C)O)(C)C (-)-Abscisic acid CC1=CC(=O)CC(C1(C=CC(=CC(=O)O)C)O)(C)C (+)-8’-Hydroxyabscisic acid C15H20O5 CC1=CC(=O)CC(C1(C=CC(=CC(=O)O)C)O)(C)CO (+)-abscisic acid beta-D-glucopyranosyl ester C21H30O9 CC1=CC(=O)CC(C1(C=CC(=CC(=O)OC2C(C(C(C(O2)CO )O)O)O)C)O)(C)C D-Fructofuranose 1,2’’:2,3’’-dianhydride C12H20O10 C1C2(C(C(C(O2)CO)O)O)OC3C(C(OC3(O1)CO)CO)O 10-Hydroxycamptothecin C20H16N2O5 CCC1(C2=C(COC1=O)C(=O)N3CC4=C(C3=C2)N=C5C=C C(=CC5=C4)O)O 2-(Hydroxymethyl)pentanedioic acid C6H10O5 C(CC(=O)O)C(CO)C(=O)O Adenosin C1=NC(=C2C(=N1)N(C=N2)C3C(C(C(O3)CO)O)O)N 4-hydroxycoumarin C1=CC=C2C(=C1)C(=CC(=O)O2)O Alpha-beta-Dihydroresveratrol C14H14O3 C1=CC(=CC=C1CCC2=CC(=CC(=C2)O)O)O Casticin C19H18O8 COC1=C(C=C(C=C1)C2=C(C(=O)C3=C(C(=C(C=C3O2)O C)OC)O)OC)O 4-nitrophenylalanine C9H10N2O4 C1=CC(=CC=C1CC(C(=O)O)N)[N+](=O)[O-] citrate C6H8O7 C(C(=O)O)C(CC(=O)O)(C(=O)O)O 3-Hydroxy-3-Carboxy-Adipic Acid C7H10O7 C(CC(CC(=O)O)(C(=O)O)O)C(=O)O (1R,2S)-1-hydroxybutane-1,2,4-tricarboxylic acid C7H10O7 C(CC(=O)O)C(C(C(=O)O)O)C(=O)O (2R)-dihomocitric acid C8H12O7 C(CC(=O)O)CC(CC(=O)O)(C(=O)O)O (-)-Threo-isodihomocitric acid C8H12O7 C(CC(C(C(=O)O)O)C(=O)O)CC(=O)O (2R)-trihomocitric acid C9H14O7 C(CCC(CC(=O)O)(C(=O)O)O)CC(=O)O 1-Hydroxyhexane-1,2,6-tricarboxylate C9H14O7 C(CCC(=O)O)CC(C(C(=O)O)O)C(=O)O Riccionidin A C15H9O6+ C1=C2C=C3C(=C4C(=CC(=CC4=[OH+])O)O3)OC2=CC(= C1O)O Scopoletin C10H8O4 COC1=C(C=C2C(=C1)C=CC(=O)O2)O 2-Deoxy-D-ribose 5-phosphate C5H11O7P C1C(C(OC1O)COP(=O)(O)O)O U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 77 Computational Molecular Bioscience Continued cyanidin C15H11O6+ C1=CC(=C(C=C1C2=[O+]C3=CC(=CC(=C3C=C2O)O)O) O)O Dinoflagellate luciferin C33H40N4O6 CCC1=C(NC(=C1C)CC2C(=C(C(=O)N2)C)C=C)CC3=C(C 4=C(N3)C(=C5C(C(C(N5)C(=O)O)C)CCC(=O)O)CC4=O) C (3S,4S,5E)-4-(2-carboxyethyl)-5-[2-({5-[(3-ethenyl -4-methyl-5-oxo-2,5-dihydro-1H-pyrrol-2-yl)met hyl]-3-ethyl-4-methyl-1H-pyrrol-2-yl}methyl)-3- me- thyl-4,5-dioxo-4,5-dihydrocyclopenta[b]pyrrol-6( 1H)-ylidene]-3-methyl-L-proline C33H38N4O7 CCC1=C(NC(=C1C)CC2C(=C(C(=O)N2)C)C=C)CC3=C(C 4=C(N3)C(=C5C(C(C(N5)C(=O)O)C)CCC(=O)O)C(=O)C4 =O)C N-Glycosyl-L-asparagine C10H18N2O8 C(C1C(C(C(C(O1)NC(=O)CC(C(=O)O)N)O)O)O)O Hesperidin C28H34O15 CC1C(C(C(C(O1)OCC2C(C(C(C(O2)OC3=CC(=C4C(=O)C C(OC4=C3)C5=CC(=C(C=C5)OC)O)O)O)O)O)O)O)O Methyl hesperidin C29H36O15 CC1C(C(C(C(O1)OCC2C(C(C(C(O2)OC3=CC(=C4C(=O)C C(OC4=C3)C5=CC(=C(C=C5)OC)OC)O)O)O)O)O)O)O Arbutin C12H16O7 C1=CC(=CC=C1O)OC2C(C(C(C(O2)CO)O)O)O alpha-Arbutin C12H16O7 C1=CC(=CC=C1O)OC2C(C(C(C(O2)CO)O)O)O Methylarbutin C13H18O7 COC1=CC=C(C=C1)OC2C(C(C(C(O2)CO)O)O)O Quercitrin C21H20O11 CC1C(C(C(C(O1)OC2=C(OC3=CC(=CC(=C3C2=O)O)O)C 4=CC(=C(C=C4)O)O)O)O)O 3’’,5’’-Dihydroxyflavanone C15H12O4 C1C(OC2=CC=CC=C2C1=O)C3=CC(=CC(=C3)O)O (2S)-2’,7-Dimethoxy-3’,5-dihydroxyflavanone C17H16O6 COC1=CC(=C2C(=O)CC(OC2=C1)C3=C(C(=CC=C3)O)O C)O Orientin C21H20O11 C1=CC(=C(C=C1C2=CC(=O)C3=C(O2)C(=C(C=C3O)O)C 4C(C(C(C(O4)CO)O)O)O)O)O Evocarpine C23H33NO CCCCC=CCCCCCCCC1=CC(=O)C2=CC=CC=C2N1C (Z)-1-Methyl-2-(tridec-8-en-1-yl)quinolin-4(1H)- one C23H33NO CCCCC=CCCCCCCCC1=CC(=O)C2=CC=CC=C2N1C Aspalathin C21H24O11 C1=CC(=C(C=C1CCC(=O)C2=C(C=C(C(=C2O)C3C(C(C( C(O3)CO)O)O)O)O)O)O)O 1-Stearoylglycerophosphocholine C26H55NO7P+ CCCCCCCCCCCCCCCCCC(=O)OCC(COP(=O)(O)OCC[ N+](C)(C)C)O 1-Octadecanoyl-sn-glycero-3-phosphocholine C26H54NO7P CCCCCCCCCCCCCCCCCC(=O)OCC(COP(=O)([O-])OC C[N+](C)(C)C)O Sinigrin C10H17NO9S2 C=CCC(=NOS(=O)(=O)O)SC1C(C(C(C(O1)CO)O)O)O Spirilloxanthin C42H60O2 CC(=CC=CC(=CC=CC(=CC=CC=C(C)C=CC=C(C)C=CC= C(C)C=CCC(C)(C)OC)C)C)C=CCC(C)(C)OC Aridanin C38H61NO8 CC(=O)NC1C(C(C(OC1OC2CCC3(C(C2(C)C)CCC4(C3CC =C5C4(CCC6(C5CC(CC6)(C)C)C(=O)O)C)C)C)CO)O)O 1-Hexadecanoyl-2-(9Z-octadecenoyl)-sn-glycero- 3-phosphoethanolamine C39H76NO8P CCCCCCCCCCCCCCCC(=O)OCC(COP(=O)(O)OCCN)O C(=O)CCCCCCCC=CCCCCCCCC U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 78 Computational Molecular Bioscience Continued Hexadecasphinganine C16H35NO2 CCCCCCCCCCCCCC(C(CO)N)O Phytosphingosine C18H39NO3 CCCCCCCCCCCCCCC(C(C(CO)N)O)O Piperonal C8H6O3 C1OC2=C(O1)C=C(C=C2)C=O Lupanine C15H24N2O C1CCN2CC3CC(C2C1)CN4C3CCCC4=O Flavanone C15H12O2 C1C(OC2=CC=CC=C2C1=O)C3=CC=CC=C3 2-hydroxy flavone C22H26ClNO4 CC1=CC=C(C=C1)C2=CC(=O)C3=C(O2)C=C(C=C3)OCC( CNC(C)C)O.Cl Dihydrostillbene base C14H12O2 C1=CC(=CC=C1C=CC2=CC=C(C=C2)O)O 5-Sulfosalicylate C7H6O6S C1=CC(=C(C=C1S(=O)(=O)O)C(=O)O)O Glabranin C20H20O4 CC(=CCC1=C(C=C(C2=C1OC(CC2=O)C3=CC=CC=C3)O) O)C 2-Deoxy-scyllo-inosose C6H10O5 C1C(C(C(C(C1=O)O)O)O)O Acetanilide C8H9NO CC(=O)NC1=CC=CC=C1 Niridazole C6H6N4O3S C1CN(C(=O)N1)C2=NC=C(S2)[N+](=O)[O-] Citrinin C13H14O5 [H][C@]1(C)OC=C2C(O)=C(C(O)=O)C(=O)C(C)=C2[C@] 1([H])C Aspartame C14H18N2O5 COC(=O)C(CC1=CC=CC=C1)NC(=O)C(CC(=O)O)N epsilon-Caprolactam C6H11NO C1CCC(=O)NCC1 cis-3-(3-Carboxyethenyl)-3,5-cyclohexadiene-1,2- diol C9H10O4 C1=CC(C(C(=C1)C=CC(=O)O)O)O Isobavachalcone C20H20O4 CC(=CCC1=C(C=CC(=C1O)C(=O)C=CC2=CC=C(C=C2)O )O)C Glabridin C20H20O4 CC1(C=CC2=C(O1)C=CC3=C2OCC(C3)C4=C(C=C(C=C4) O)O)C Mycocyclosin C18H16N2O4 C1C2C(=O)NC(CC3=CC(=C(C=C3)O)C4=C(C=CC1=C4)O )C(=O)N2 2,3-Dehydro-UWM6 C19H16O5 CC1=CC(=O)C2C3=C(C(=O)CC2(C1)O)C(=C4C(=C3)C=C C=C4O)O Prazepam C19H17ClN2O C1CC1CN2C(=O)CN=C(C3=C2C=CC(=C3)Cl)C4=CC=CC =C4 (-)-Phaseollinisoflavan C20H20O4 CC1(C=CC2=C(O1)C=CC(=C2O)C3CC4=C(C=C(C=C4)O) OC3)C Phaseollidin C20H20O4 CC(=CCC1=C(C=CC2=C1OC3C2COC4=C3C=CC(=C4)O) O)C Bis-D-fructose 2’’,1:2,1’’-dianhydride C12H20O10 C1C2(C(C(C(O2)CO)O)O)OCC3(O1)C(C(C(O3)CO)O)O 2-Dehydro-3-deoxy-D-fuconate C6H10O5 CC(C(CC(=O)C(=O)O)O)O 2-Dehydro-3-deoxy-L-rhamnonate C6H10O5 CC(C(CC(=O)C(=O)O)O)O 2-Dehydro-3-deoxy-L-fuconate C6H10O5 CC(C(CC(=O)C(=O)O)O)O Diethyl pyrocarbonate C6H10O5 CCOC(=O)OC(=O)OCC U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 79 Computational Molecular Bioscience Continued 3,6-Anhydro-alpha-L-galactopyranose C6H10O5 C1C2C(C(O1)C(C(O2)O)O)O Eugenol quinone methide C10H10O2 COC1=CC(=CC=C)C=CC1=O Methyl cinnamate C10H10O2 COC(=O)C=CC1=CC=CC=C1 p-Methoxycinnamaldehyde C10H10O2 COC1=CC=C(C=C1)C=CC=O 2-Hydroxymethylserine C4H9NO4 C(C(CO)(C(=O)O)N)O 2-Phenylacetamide C8H9NO C1=CC=C(C=C1)CC(=O)N 4-Hydroxy-L-threonine C4H9NO4 C(C(C(C(=O)O)N)O)O N-Benzylformamide C8H9NO C1=CC=C(C=C1)CNC=O Adenine C5H5N5 C1=NC2=C(N1)C(=NC=N2)N (E)-Phenylacetaldoxime C8H9NO C1=CC=C(C=C1)CC=NO (Z)-Phenylacetaldehyde oxime C8H9NO C1=CC=C(C=C1)CC=NO Dibenzo[1,4]dioxin-2,3-dione C12H6O4 C1=CC=C2C(=C1)OC3=CC(=O)C(=O)C=C3O2 5-Deoxyribose-1-phosphate C5H11O7P CC1C(C(C(O1)OP(=O)(O)O)O)O 2-Deoxy-D-ribose 1-phosphate C5H11O7P C1C(C(OC1OP(=O)(O)O)CO)O 1-Deoxy-D-xylulose 5-phosphate C5H11O7P CC(=O)C(C(COP(=O)(O)O)O)O 3,5-Dinitroguaiacol C7H6N2O6 COC1=C(C=C(C=C1O)[N+](=O)[O-])[N+](=O)[O-] 2-(5’’-Methylthio)pentylmalic acid C10H18O5S CSCCCCCC(CC(=O)O)(C(=O)O)O 3-(5’’-Methylthio)pentylmalic acid C10H18O5S CSCCCCCC(C(C(=O)O)O)C(=O)O 3-(m-Aminophenyl)-2-(p-methoxyphenyl)acrylon itrile C16H14N2O COC1=CC=C(C=C1)C(=CC2=CC(=CC=C2)N)C#N Glycophymoline C16H14N2O COC1=NC(=NC2=CC=CC=C21)CC3=CC=CC=C3 Flindersiachromone C17H14O2 C1=CC=C(C=C1)CCC2=CC(=O)C3=CC=CC=C3O2 Arborine C16H14N2O CN1C2=CC=CC=C2C(=O)N=C1CC3=CC=CC=C3 4,4’’-Methylenediphenyl diisocyanate C15H10N2O2 C1=CC(=CC=C1CC2=CC=C(C=C2)N=C=O)N=C=O Methaqualone C16H14N2O CC1=CC=CC=C1N2C(=NC3=CC=CC=C3C2=O)C Triamiphos C12H19N6OP CN(C)P(=O)(N1C(=NC(=N1)C2=CC=CC=C2)N)N(C)C 2-[3-Ethyl-5-(4-methoxyphenyl)-1H-pyrazol-4-yl] phenol C18H18N2O2 CCC1=C(C(=NN1)C2=CC=C(C=C2)OC)C3=CC=CC=C3O (2-Butylbenzofuran-3-yl)(4-hydroxyphenyl)keton e C19H18O3 CCCCC1=C(C2=CC=CC=C2O1)C(=O)C3=CC=C(C=C3)O Tutin C15H18O6 CC(=C)C1C2C(C3(C4(CO4)C5C(C3(C1C(=O)O2)O)O5)C) O 3-Methoxy-4-hydroxyphenylglycolaldehyde C9H10O4 COC1=C(C=CC(=C1)C(C=O)O)O (R)-3-(4-Hydroxyphenyl)lactate C9H10O4 C1=CC(=CC=C1CC(C(=O)O)O)O 3,4-Dihydroxyphenylpropanoate C9H10O4 C1=CC(=C(C=C1CCC(=O)O)O)O 2’’,6’’-Dihydroxy-4’’-methoxyacetophenone C9H10O4 CC(=O)C1=C(C=C(C=C1O)OC)O U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 80 Computational Molecular Bioscience Continued 3-(4-Hydroxyphenyl)lactate C9H10O4 C1=CC(=CC=C1CC(C(=O)O)O)O Homovanillate C9H10O4 COC1=C(C=CC(=C1)CC(=O)O)O 3-(2,3-Dihydroxyphenyl)propanoate C9H10O4 C1=CC(=C(C(=C1)O)O)CCC(=O)O Mannitol C6H14O6 C(C(C(C(C(CO)O)O)O)O)O D-Sorbitol C6H14O6 C(C(C(C(C(CO)O)O)O)O)O This systematic scrutiny aimed to discern the compounds exhibiting favorable pharmacological properties conducive to combatting breast cancer and uterine fibroids, thus offering promising avenues for therapeutic intervention. Within this rigorous evaluation framework, several key metrics were scrutinized, i n- cluding compliance with Lipinski’s Rule of Five, a pivotal criterion for predicting oral bioavailability and permeability of potential drug candidates. Additionally, lead-likeness, hydrogen bond donor and acceptor counts, and bioavailability scores were meticulously examined, offering valuable insights into the co m- pounds’ drug-like properties and therapeutic potential [19] . By leveraging the insights gleaned from SwissADME analysis, compounds exhibiting optimal pharmacokinetic profiles and bioavailability were identified as prime candidates for further investig ation and therapeutic development. These compounds, ch a- racterized by their propensity to permeate biological barriers, maintain favorable drug-like properties, and exhibit high bioavailability, hold promise in the ta r- geted management and mitigation of breast cancer and uterine fibroids [19]. The systematic integration of LC -TOF MS analysis, compound identification, and SwissADME evaluation represents a pivotal step towards the rational design and discovery of novel therapeutics aimed at addressing the unmet clinical needs a s- sociated with breast cancer and uterine fibroids. Through this concerted effort, the identification of lead compounds with enhanced efficacy and safety profiles heralds a significant stride towards personalized and precision medicine a p- proaches tailored to combat these debilitating diseases [19]. 3.3.2. RPBS Following the initial filtration process employing SwissADME, which identified 51 compounds with potential efficacy against breast cancer and uterine fibroids, a subsequent analysis utilizing RPBS was conducted to evaluate rotatable bonds and energy profiles. This additional scrutiny yielded a refined subset of 34 co m- pounds, each characterized by optimal structural flexibility and energetics co n- ducive to molecular docking studies [20]. The RPBS assessment provided crucial insights into the molecular dynamics of the selected compounds, elucidating their ability to adopt diverse conformations and facilitating efficient interactions with target biomolecules implicated in disease pathogenesis [20] . By prioritizing compounds with favorable rotatable bond counts and energy profiles, this iter a- tive screening process enhances the rati onal selection of lead candidates poised for further preclinical evaluation, Table 5 [20]. U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 81 Computational Molecular Bioscience Table 5. Shows down streaming results from RPBS tools. COMPOUND NAME RB E (1QQG) E (2AYR) E(7B E (6T41) E (3GRF) E (7B5O) <5 <− 5 Bergaptol 0 −7.9 −8.4 −8.7 −8.9 −8.8 −8.6 Bergapten 1 −7.6 −8 −8.9 −9.1 −8.9 −8.1 Isobergaptol 0 −8.2 −8.6 −8.7 −8.6 −8.6 −8.6 Abscisic acid 3 −7.9 −7.7 −8.9 −8.6 −7.9 −7.9 (-)-Abscisic acid 3 −8 −7.5 −8.9 −8.6 −8.5 −7.9 (+)-8’-Hydroxyabscisic acid 4 −7.4 −7.3 −8.5 −8 −8.1 −8 (+)-abscisic acid beta-D-glucopyranosyl ester 6 −9.6 −9.2 −9.7 −9.4 −8.9 −9.5 10-Hydroxycamptothecin 1 −11.1 −11.4 −12.4 −12.9 −10.4 −11.4 4-hydroxycoumarin 0 −7.5 −7.4 −8.1 −7.5 −7.9 −7.4 Alpha-beta-Dihydroresveratrol 3 −8.2 −7.8 −9.4 −9.2 −8.4 −9 Casticin 5 −9 −8.4 −8.8 −8.7 −8 −9.1 Scopoletin 1 −7 −7.3 −8.2 −7.9 −7.8 −7.5 Methylarbutin 4 −7.8 −8 −8.4 −8.2 −7.7 −8.5 3’’,5’’-Dihydroxyflavanone 1 −9.5 −9.3 −10.3 −10.4 −9.4 −9.9 (2S)-2’,7-Dimethoxy-3’,5-dihydroxyflavanone 3 −8.9 −8.9 −9.8 −9.9 −8.9 −9.3 Lupanine 0 −8.8 −9 −10.1 −10.1 −8.4 −9.8 Flavanone 1 −9.4 −9.3 −10.4 −10.3 −10 −10.2 5-Sulfosalicylate 2 −7.2 −7.5 −7.4 −7.3 −6.7 −7.3 Glabranin 3 −9.1 −9.6 −10.8 −10.6 −9.6 −10.5 Niridazole 2 −6.6 −7 −6.7 −6.9 −6.8 −6.5 Citrinin 1 −8.3 −8.9 −9.1 −9.4 −9.8 −8.9 Aspartame 9 −7.5 −7.1 −7.8 −8.1 −7.6 −7.5 cis-3-(3-Carboxyethenyl)-3,5- cyclohexadiene-1,2-diol 2 −5.3 −5.3 −6.3 −5.8 −5.5 −5.6 Glabridin 1 −10.2 −11.1 −10.8 −12.3 −11 −10.7 Mycocyclosin 0 −11.1 −11.8 −12.7 −12.8 −12.3 −12.1 2,3-Dehydro-UWM6 0 −10.3 −10.5 −11.6 −11.9 −10.7 −11.6 Prazepam 3 −9.4 −9.9 −10.5 −10.5 −9.3 −10.3 (-)-Phaseollinisoflavan 1 −10.1 −11 −10.3 −12.4 −12.1 −10.6 Phaseollidin 2 −9.6 −10.1 −10.8 −10.8 −10 −10.4 Diethyl pyrocarbonate 6 −4.9 −5 −4.6 −5 −5.1 −5.1 Eugenol quinone methide 2 −6.2 −6.6 −7.3 −6.9 −7.1 −6.7 Methyl cinnamate 3 −6.6 −6.4 −7 −6.9 −7 −6.8 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 82 Computational Molecular Bioscience Continued p-Methoxycinnamaldehyde 3 −6.4 −6.1 −6.9 −6.7 −6.8 −6.7 Dibenzo[1,4]dioxin-2,3-dione 0 −9.4 −9.2 −10.6 −9.7 −10.7 −9.4 3,5-Dinitroguaiacol 3 −6.2 −7 −7.1 −6.7 −6.8 −6.9 2-(5’’-Methylthio)pentylmalic acid 9 −5.7 −5.7 −6 −5.9 −6 −5.4 3-(5’’-Methylthio)pentylmalic acid 9 −4.9 −5.4 −6.6 −5.9 −6.3 −6.3 3-(m-Aminophenyl)-2-(p-methoxyphenyl)acr ylonitrile 3 −8.2 −8.2 −7.6 −7.6 −8.2 −7.7 Glycophymoline 3 −8.8 −9.5 −10.5 −10.5 −10.1 −10.4 Flindersiachromone 3 −9.2 −9.4 −11 −10.5 −10.1 −10.5 Arborine 2 −9.2 −9.3 −10.8 −10 −9.7 −10.2 Methaqualone 1 −8.8 −9.6 −10.5 −10.3 −10.2 −9.9 Triamiphos 4 −7.5 −7.9 −8.1 −7.8 −7.6 −7.8 2-[3-Ethyl-5-(4-methoxyphenyl)-1H- pyrazol-4-yl]phenol 4 −8.4 −9 −8.5 −9.1 −9 −8.5 (2-Butylbenzofuran-3-yl)(4- hydroxyphenyl)ketone 5 −8 −7.1 −8.1 −8 −8.3 −8.3 Tutin 1 −8.6 −8.9 −9.1 −9.5 −8.6 −9 3-Methoxy-4-hydroxyphenylglycolaldehyde 3 −6.1 −6.7 −6.7 −6.5 −6.7 −6.5 (R)-3-(4-Hydroxyphenyl)lactate 3 −6.8 −6.9 −7.3 −7.4 −6.9 −7 2’’,6’’-Dihydroxy-4’’-methoxyacetophenone 2 −6.3 −6.5 −6.3 −6.8 −6.9 −6.8 3-(4-Hydroxyphenyl)lactate 3 −6.6 −7 −7.3 −7.4 −7.2 −7 Homovanillate 3 −6.2 −7.1 −7.1 −6.9 −7.1 −6.8 Out of the initial pool of 34 compounds, only 22 demonstrated the remarkable capability to bind with all six protein diseases associated with both breast cancer and uterine fibroid. This subset of compounds exhibits broad- spectrum activity, indicating their potential to target multiple pathological pathways implicated in the progression of these diseases. Their ability to interact with diverse protein targets underscores their versatility and promise as candidate therapeutics [20]. In contrast, the remaining 12 compounds displayed a more selective binding profile, interacting with a subset of two to four protein diseases. While these compounds may exhibit efficacy against specific disease subtypes or pathways, their narrower spectrum of activity suggests a more targeted mode of action. Despite this sele c- tivity, these compounds still hold considerable therapeutic potential and merit further investigation for their specific applications in breast cancer and uterine fibroid management [20]. 3.3.3. Dogsitescorer Dogsitescorer, a specialized computational tool, is employed to discern the U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 83 Computational Molecular Bioscience highest binding energy exhibited by potential compounds against protein targets implicated in breast cancer and uterine fibroid pathogenesis. Subsequently, coordinates corresponding to the identified binding sites are extracted from the output generated by Dogsitescorer [21] . These coordinates play a pivotal role in elucidating the precise molecular interactions between the candidate compounds and their respective protein targets. By pinpointing the specific binding sites on the protein surfaces, these coordinates provide valuable insights into the mol e- cular mechanisms underpinning the observed binding affinities [21]. Furthermore, the coordinates obtained from Dogsitescorer serve as crucial input parameters for subsequent molecular docking simulations [21] . Leveraging ad- vanced computational algorithms, molecular docking studies enable the prediction of the binding modes and affinities of the candidate compounds within the protein binding sites, offering valuable predictive insights into their therapeutic potential. Through the iterative integration of computational tools such as Dogsitescorer and molecular docking simulations, the identification of lead compounds with optimal binding energies and favorable interactio n profiles against protein targets asso- ciated with breast cancer and uterine fibroids is facilitated. This systematic a p- proach enhances the rational design and optimization of novel therapeutics aimed at mitigating the progression of these debilitating diseases [21]. 3.3.4. Molecular Docking - Achilles Blind Docking Server The 34 identified compounds, targeting breast cancer and uterine fibroid-associated protein diseases, underwent comprehensive molecular docking simulations using the ACHILLES BLIND DOCKING SERVER [22]. This state-of-the-art computa- tional tool fac ilitated the exploration of compound -protein interactions across multiple protein targets, providing valuable insights into their binding affinities and binding site preferences. Through the blind docking approach employed by ACHILLES, the compatibility between each compound and the diverse array of protein targets associated with breast cancer and uterine fibroids was systemat i- cally evaluated. By considering multiple protein structures representative of di f- ferent disease states, this approach enabled a com prehensive assessment of com- pound efficacy across various pathological contexts [22]. Furthermore, the molecular docking simulations facilitated the identification of potential binding sites within the protein coordinates for each compound. The number and distribution of these binding sites served as critical indicators of compound versatility and potential therapeutic efficacy. Compounds exhibi t- ing a higher propensity to bind at multiple locations within the protein coord i- nates were deemed particularly promising, as they may exert broader therapeutic effects and target diverse disease mechanisms [22] . By integrating the insights gleaned from molecular docking simulations across multiple protein targets, a comprehensive understanding of compound -protein interactions and their p o- tential implications for breast cancer and uterine fibroid management was at- tained, Table 6. U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 84 Computational Molecular Bioscience Table 6. Shows the number of locations within protein coordinate set by dogsitescorer’s energy binding with a total residue > − 6. COMPOUND NAME ID NUMBER OF LOCATION WITHIN COORDINATE TOTAL RESIDUE (>−6) 1QQG 2AYR 3GRF 6T41 7B5Q 7B5O Isobergaptol 10198122 1 1 1 6 6 6 10-Hydroxycamptothecin 97226 3 2 2 4 3 6 Casticin 5315263 1 2 1 4 5 6 3’’,5’’-Dihydroxyflavanone 11954216 3 1 1 9 3 6 (2S)-2’,7-Dimethoxy-3’,5-dihydroxyflavanone 102066377 2 2 2 6 4 7 Lupanine 91471 1 2 1 4 3 3 Flavanone 10251 2 2 1 4 4 6 Glabranin 124049 2 2 2 5 2 6 Citrinin 54680783 1 1 1 4 4 5 Glabridin 124052 3 2 2 3 4 6 Mycocyclosin 59053147 3 1 1 3 5 6 2,3-Dehydro-UWM6 25195328 3 1 5 5 6 5 Prazepam 4890 4 1 2 3 4 6 (-)-Phaseollinisoflavan 162412 3 2 2 3 4 6 Phaseollidin 119268 3 2 4 5 6 4 Dibenzo[1,4]dioxin-2,3-dione 17036 3 2 1 5 6 5 Glycophymoline 5480 3 2 1 6 4 3 Flindersiachromone 441964 4 2 4 4 3 5 Arborine 63123 3 2 3 5 5 7 Methaqualone 6292 2 2 2 6 6 6 2-[3-Ethyl-5-(4-methoxyphenyl)-1H-pyrazol-4-yl]phenol 257428 2 1 2 8 5 5 Tutin 75729 1 1 1 8 4 4 (-)-Abscisic acid 643732 2 NA 0 9 7 NA Alpha-beta-Dihydroresveratrol 185914 3 NA 2 11 5 6 3-(m-Aminophenyl)-2-(p-methoxyphenyl)acrylonitrile 79559 2 2 2 NA NA NA (2-Butylbenzofuran-3-yl)(4-hydroxyphenyl)ketone 79569 3 NA 2 NA 3 6 Bergaptol 5280371 NA 2 1 12 6 5 Bergapten 2355 NA 2 1 8 5 6 Methylarbutin 80131 NA 2 NA 11 3 7 Abscisic acid 5280896 NA NA NA 11 5 NA (+)-8’-Hydroxyabscisic acid 11954194 NA NA 1 10 4 7 4-hydroxycoumarin 54682930 NA NA NA NA 5 NA Scopoletin 5280460 NA NA NA NA 8 NA Triamiphos 13943 NA NA NA NA 6 NA U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 85 Computational Molecular Bioscience To visualize the binding profiles of the compounds across all six protein di s- eases, a graph can be constructed with the compounds on the x -axis and the number of binding locations within the protein coordinates on the y -axis. Each compound is represented by a bar, with the height of the bar indicating the number of binding locations within the protein coordinates for that compound. The graph provides an overview of the binding versatility of each compound across multiple protein targets associated with breast cancer and uterine fibroid , Graph 1 . U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 86 Computational Molecular Bioscience Graph 1 . Show the graph for 22 compounds with the number of locations it’s able to bind. The comprehensive analysis of compound-protein interactions reveals distinct binding profiles across multiple protein targets implicated in breast cancer and uterine fibroid pathogenesis. Specifically, against the 1QQG protein, 26 com- pounds exhibit diverse binding patterns, with each compound displaying a va- U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 87 Computational Molecular Bioscience rying number of binding locations within the protein coordinates. Similarly, in- teractions with the 2AYR protein reveal comparable trends among 26 com- pounds, while interactions with the 3GRF, 6T41, 7B5Q, and 7B5O proteins demonstrate unique binding profiles for 29, 29, 33, and 28 compounds, respec- tively. These findings underscore the compound-specific nature of binding inte- ractions and provide valuable insights for further exploration of therapeutic in- terventions targeting breast cancer and uterine fibroids in Graph s 2(a)-(f). Fig- ures 12-17 show the one of the compounds of 10251 for each protein’s locations within set coordinates. (a) (b) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 88 Computational Molecular Bioscience (c) (d) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 89 Computational Molecular Bioscience (e) (f) Graph 2 . (a) Shows protein 1QQG ’s number of location binding for each compound ; (b) Shows protein 2AYR’s number of location binding for each compound ; (c) Shows protein 3GRF ’s number of location binding for each compound; (d) Shows protein 6T41 ’s number of location binding for each compound ; (e) Shows protein 7B5Q ’s number of location binding for each compound ; (f) Shows protein 7B5O ’s number of location binding for each compound. U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 90 Computational Molecular Bioscience Figure 1 2. Shows compound 10251 location within set coordinate in 1QQG protein. Figure 1 3. Shows compound 10251 location within set coordinate in 2AYR protein. U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 91 Computational Molecular Bioscience Figure 1 4. Shows compound 10251 location within set coordinate in 3GRF protein. Figure 1 5. Shows compound 10251 location within set coordinate in 6T41 protein. U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 92 Computational Molecular Bioscience Figure 1 6. Shows compound 10251 location within set coordinate in 7B5Q protein. Figure 1 7. Shows compound 10251 location within set coordinate in 7B5O protein. U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 93 Computational Molecular Bioscience The figure displays the distances between a specific compound and the amino acids of proteins associated with breast cancer and uterine fibroid pathogenesis. Specifically, it illustrates the distances to amino acids of proteins 1QQG, 7B5Q, and 7B5O, representing breast cancer-related proteins, as well as proteins 2AYR, 3GRF, and 6T41, which are associated with uterine fibroid disease. These di s- tances serve as indicators of the successful rate of binding energy for the co m- pound towards each protein disease. Variations in distance highlight the diffe r- ing degrees of interaction between the compound and the amino acids within each protein structure. Shorter distances suggest stronger binding interactions, indicating a higher potential for therapeutic efficacy in reducing the propensity for breast cancer and uterine fibroid development. Conversely, longer distances may signify weaker binding interactions, suggesting a need for further investig a- tion or optimization of the compound’s efficacy. By analyzing the distances to amino acids in the proteins relevant to both breast cancer and uterine fibroids, this table and 4 figures for each proteins pr o- vides valuable insights into the compound ’s potential effectiveness in targeting these di seases. This information aids in the identification and prioritization of compounds for further preclinical and clinical studies aimed at mitigating the progression of breast cancer and uterine fibroids, Figure s 18-23. (a) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 94 Computational Molecular Bioscience (b) (c) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 95 Computational Molecular Bioscience (d) Figure 1 8. (a) Shows protein 1QQG interactions and distance from compound 441964 amino acids with the nearest distances of 3.6 ; (b) Shows protein 1QQG interactions and distance from compound 10198122 amino acids with the nearest distances of 2.9 ; (c) Shows protein 1QQG interactions and distance from compound 10251 amino acids with the nearest distances of 2.8 ; (d) Shows protein 1QQG interactions and distance from compound 124049 amino acids with the nearest distances of 3.0. (a) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 96 Computational Molecular Bioscience (b) (c) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 97 Computational Molecular Bioscience (d) Figure 1 9. (a) Shows protein 2AYR interactions and distance from compound 2355 amino acids with the nearest distances of 3.1 ; (b) Shows protein 2AYR interactions and distance from compound 10251 amino acids with the nearest distances of 3.6 ; (c) Shows protein 2AYR interactions and distance from compound 80131 amino acids with the nearest distances of 2.5 ; (d) Shows protein 2AYR interactions and distance from com- pound 91471 amino acids with the nearest distances of 3.6 (a) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 98 Computational Molecular Bioscience (b) (c) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 99 Computational Molecular Bioscience (d) Figure 2 0. (a) Shows protein 3GRF interactions and distance from compound 25195328 amino acids with the nearest distances of 3.3 ; (b) Shows protein 3GRF interactions and distance from compound 97226 amino acids with the nearest distances of 3.0 ; (c) Shows protein 3GRF interactions and distance from compound 124049 amino acids with the nearest distances of 3.2 ; (d) Shows protein 3GRF interactions and distance from com- pound 124052 amino acids with the nearest distances of 2.8 (a) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 100 Computational Molecular Bioscience (b) (c) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 101 Computational Molecular Bioscience (d) Figure 2 1. (a) Shows protein 6T41 interactions and distance from compound 5280371 amino acids with the nearest distances of 2.8 ; (b) Shows protein 6T41 interactions and distance from compound 4890 amino acids with the nearest distances of 3.1 ; (c) Shows protein 6T41 interactions and distance from compound 5480 amino acids with the nea r- est distances of 3.9 ; (d) Shows protein 6T41 interactions and distance from compound 6292 amino acids with the nearest distances of 3.1. (a) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 102 Computational Molecular Bioscience (b) (c) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 103 Computational Molecular Bioscience (d) Figure 2 2. (a). Shows protein 7B5Q interactions and distance from compound 5280460 amino a c- ids with the nearest distances of 2.0 ; (b) Shows protein 7B5Q interactions and distance from com- pound 2355 amino acids with the nearest distances of 3.3 ; (c) Shows protein 7B5Q interactions and distance from compound 4890 amino acids with the nearest distances of 2.8 ; (d) Shows protein 7B5Q interactions and distance from compound 5480 amino acids with the nearest distances of 2.9. (a) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 104 Computational Molecular Bioscience (b) (c) U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 105 Computational Molecular Bioscience (d) Figure 2 3. (a) Shows protein 7B5O interactions and distance from compound 11954194 amino acids with the nearest distances of 3.0 ; (b) Shows protein 7B5O interactions and distance from compound 6292 amino acids with the nearest distances of 3.0 ; (c) Shows protein 7B5O interactions and distance from compound 10251 amino acids with the nearest distances of 3.1 ; (d) Shows protein 7B5O interactions and distance from com- pound 17036 amino acids with the nearest distances of 2.9. 4. Discussion The compounds that have been found in the LifeGreen TM product provide vari- ous benefits towards human health, breast cancers and uterine fibroid. Isobe r- gaptol is a compound found in certain plants, particularly in essential oils. It has been studied for its potential an ti-inflammatory and antimicrobial properties, which could contribute to improved immune function and wound healing. Some research suggests that Isobergaptol may also have antioxidant properties, helping to neutralize harmful free radicals in the body and reduce oxidative stress, which is associated with various chronic diseases. 10 -Hydroxycamptothecin is a natu- rally occurring compound found in the Camptotheca acuminata tree. It is a d e- rivative of camptothecin, a well -known anticancer agent. Studies have sho wn that 10 -Hydroxycamptothecin exhibits potent antitumor activity by inhibiting the enzyme topoisomerase I, which is involved in DNA replication. This action can prevent cancer cells from proliferating and induce apoptosis (programmed cell death) in cancer cells. Camptothecin and related analogs have shown pro m- U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 106 Computational Molecular Bioscience ise as anticancer agents, which could lead to the death of tumor cells by targeting the nuclear enzyme, topoisomerase I, and inhibiting the relegation of the cleaved DNA strand. One of the camptothecin analogs, hydroxycamptothecin (HCPT), a plant alkaloid derived from Camptotheca acuminata, has demonstrated strong antitumor activity against gastric, lung, ovarian, breast, and pancreatic carcino- mas [23]. 10-hydroxycamptothecin (10-HCPT) is an important class of antit u- mor agent, it inhibits the DNA topoisomerase I of tumors and suppresses the proliferation of cancer cells to elicit antitumor effect [24] . Studies in animal and human subjects have shown that 10-hydroxycamptothecin (HCPT) is more p o- tent and less toxic than the parent compound CPT. Casticin is a p olymethylfla- vone isolated from a traditional Chinese therapeutic plant named Vitex trifolia L. from the Verbenaceae family [25] . The plant contributes to improve m any morbidities including premenstrual syndrome, mastalgia, inflammation and sexual dysfunction, and also helps to relieve pain, and possesses antinociceptive effects. This plant is useful in mild hyperprolactinemia and luteal phase defects. It is also hel pful in alleviating menstruation, bleeding management uterine f i- broids, polycystic ovarian syndrome, prostate disorders, migrainous women with premenstrual syndrome [26] . Casticin is a flavonoid compound found in several medicinal plants, including Vitex agnus -castus (chaste tree). It has been investigated for its various potential health benefits. Research suggests that casti- cin possesses anti-inflammatory and antioxidant properties, which may help r e- duce inflammation and oxidative stress in the body. This could potentially ben e- fit conditions such as arthritis and cardiovascular disease. Some studies also i n- dicate that casticin may have anticancer properties, inhibi ting the growth and proliferation of cancer cells in certain types of cancer. This flavonoid compound is found in various fruits and vegetables and has been studied for its potential health- promoting effects. Like other flavonoids, 3’,5’-dihydroxyflavanone exhibits antioxidant properties, which can help protect cells from oxidative damage and reduce the risk of chronic diseases such as heart disease and cancer. Additionally, some research suggests that this compound may have anti -inflammatory properties, wh ich could help alleviate inflamma- tion-related conditions such as arthritis and inflammatory bowel disease. This flavonoid compound is also found in various plants and has been investigated for its potential health benefits. Like other flavonoids, (2S)- 2’,7-Dimethoxy- 3’,5’-dihydroxy flavanone possesses antioxidant properties, which can help pr o- tect cells from oxidative damage and reduce the risk of chronic diseases. Some research suggests that this compound may also have anti -inflammatory effects, which cou ld potentially benefit conditions such as arthritis and cardiovascular disease. Lupanine is a quinolizidine alkaloid found in various plant species, i n- cluding lupin seeds. It has been studied for its potential health benefits. Research suggests that Lupanine may have hypotensive (blood pressure- lowering) effects, making it potentially beneficial for individuals with hypertension. Additionally, Lupanine has been investigated for its potential antidiabetic properties, with U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 107 Computational Molecular Bioscience some studies indicating that it may help improve insulin sensitivity and glucose metabolism. Flavanones are a class of flavonoid compounds found in various fruits and vegetables, particularly citrus fruits. They have been studied for their numerous health benefits [27]. Flavanones exhibit antioxidant properties, helping to neutralize harmful free radicals in the body and reduce oxidative stress, which is associated with various chronic diseases such a s cardiovascular disease and cancer. Some flavanones, such as hesperidin and naringenin, have been shown to have anti- inflammatory effects, which may help reduce inflammation and alleviate symptoms of infla m- matory conditions like arthritis. Flavanones have been found to have both ant i- oxidant and anti- inflammatory properties. In particular, different studies have focused their attention on hesperidin and its aglycone form, hesperetin, which play an important role in the prevention of diseases associated with oxidative stress and inflammation, such as cancer and cardiovascular disease [28]. Glabra- nin is a compound found in licorice (Glycyrrhiza glabra) and has been studied for its potential health benefits. Research suggests that Glabranin may have an- ti-inflammatory properties, which could help reduce inflammation in the body and alleviate symptoms of inflammatory conditions such as arthritis. Additio n- ally, Glabranin has been investigated for its potential antiviral and antimicrobial properties, which could contribute to its use in traditional medicine for treating infections. Citrinin is a mycotoxin produced by certain fungi, particularly sp e- cies of Penicillium and Asper gillus. While it is primarily known for its toxic e f- fects, some research has also explored potential health benefits. Limited studies suggest that Citrinin may have antioxidant properties. Glabridin is a flavonoid compound found in licorice root (Glycyrrhi za glabra) and has been studied for its various health benefits. Research suggests that Glabridin may have antiox i- dant properties, helping to protect cells from oxidative damage and reduce the risk of chronic diseases such as heart disease and cancer. Addi tionally, Glabridin has been investigated for its potential anti- inflammatory effects, which could help reduce inflammation in the body and alleviate symptoms of inflammatory conditions like arthritis. Glabridin is an isoflavan extracted from licorice (gen us Glycyrrhiza) roots, which is also known as a phytoestrogen due to the similarity of its structure and lipophilicity to 17 β-estradiol. Studies indicated that glabridin is able to bind to the ERs and induce estrogenic responses in cardiovascular and bone tissues, suggesting its possibility to be used in estrogen replacement therapy. Mycocyclosin is a compound isolated from certain fungi, particularly m a- rine-derived fungi. It has been studied for its potential pharmaceutical prope r- ties. Research suggests that Mycocyclosin may have antibacterial and antifungal properties, making it potentially useful in the development of new antibiotics or antifungal agents. Additionally, some studies indicate that Mycocyclosin may have cytotoxic effects on cancer cells, w hich could make it a candidate for fu r- ther investigation as a potential anticancer agent. 2,3 -Dehydro-UWM6 is a chemical compound with potential pharmacological applications. Prazepam is a benzodiazepine medication used to treat anxiety and panic disorders . It belongs U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 108 Computational Molecular Bioscience to the class of psychoactive drugs known for their anxiolytic (anxiety -reducing), sedative, muscle relaxant, and anticonvulsant properties. Benefits of Prazepam include its ability to alleviate symptoms of anxiety and panic disorders, promote relaxation, and reduce muscle tension. Prazepam is often prescribed for short-term relief of anxiety symptoms and is considered effective when used as directed under medical supervision. (- )-Phaseollinisoflavan is a type of isoflav o- noid compound found in c ertain plants, particularly in legumes like soybeans and chickpeas. Research suggests that isoflavonoids like (- )-Phaseollinisoflavan may have various health benefits, including potential anticancer, antioxidant, and anti- inflammatory properties. Some stud ies indicate that dietary intake of isoflavonoids may be associated with a reduced risk of certain cancers, such as breast and prostate cancer, as well as cardiovascular disease. Phaseollidin is a natural compound found in certain legumes, including beans and peas. Research suggests that Phaseollidin may have anticancer properties, as it has been shown to inhibit the growth of cancer cells in some studies. Additionally, Phaseollidin may possess anti -inflammatory and antioxidant properties, which could contr i- bute to its potential health benefits. Glycophymoline is a brand name for a topical solution containing various herbal extracts, including menthol, eucalyptol, and thymol. It is commonly used as a mouthwash and gargle for oral hygiene and minor throat irr itations. Bene- fits may include its antiseptic and refreshing properties, which can help to kill bacteria in the mouth, reduce bad breath, and soothe sore throats. Abscisic acid (ABA) is a plant hormone involved in various physiological processes in plants, such as seed dormancy, bud dormancy, and response to environmental stress. While primarily studied for its role in plants, Abscisic acid (ABA) has also been investigated for its potential health benefits in humans. Research suggests that ABA may have anti-inflammatory, antioxidant, and immunomodulatory effects, which could potentially benefit human health. It has been studied for its pote n- tial therapeutic applications in conditions such as diabetes, obesity, inflamm a- tion, and autoimmune diseases. Dihydroresveratrol is a derivative of resveratrol, a polyphenolic compound found in various plants, including grapes, berries, and peanuts. Resveratrol and its derivatives have been extensively studied for their potential health benefits, including antioxidant, ant i-inflammatory, cardiopr o- tective, neuroprotective, and anticancer properties. While research on a l- pha-beta-dihydroresveratrol specifically may be limited, it likely shares some of the health -promoting properties of resveratrol due to its structural similarity. Bergaptol is a natural compound found in certain plants, particularly in the c i- trus family. It is structurally related to bergamottin and is primarily known for its photosensitizing effects. While Bergaptol itself may not have direct health benefits, it is used in combination with other compounds in phototherapy for the treatment of certain skin conditions, such as psoriasis and vitiligo. Bergapten, also known as 5-methoxypsoralen, is a natural compound found in several plant species, including citrus fruits and certain herbs such as parsley and celery. Bergapten is primarily known for its photosensitizing effects, which have U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 109 Computational Molecular Bioscience been utilized in the treatment of skin disorders such as psoriasis, vitiligo, and eczema through a process known as psoralen plus ultraviolet A (PUVA) therapy. Additionally, Bergapten has been studied for its potential anticancer properties, particularly in the treatment of cutaneous T -cell lymphoma (CTCL). Methyla r- butin is a derivative of arbutin, a natural compound found in variou s plant spe- cies such as bearberry, cranberry, and blueberry. Arbutin is well- known for its skin-lightening and antioxidant properties. Methylarbutin is often used in co s- metic products for its potential to inhibit melanin production and reduce the appearance of hyperpigmentation, dark spots, and uneven skin tone. Additio n- ally, arbutin and its derivatives like Methylarbutin have been studied for their potential antioxidant and anti- inflammatory effects, which could contribute to their skin -protective properti es. Abscisic acid (ABA) is a plant hormone i n- volved in various physiological processes, including seed dormancy, bud do r- mancy, and response to environmental stress. While primarily studied for its role in plants, Abscisic acid (ABA) has also been investiga ted for its potential health benefits in humans. Research suggests that Abscisic acid (ABA) may have anti-inflammatory, antioxidant, and immunomodulatory effects, which could potentially benefit human health. It has been studied for its potential therapeutic applications in conditions such as diabetes, obesity, inflammation, and autoi m- mune diseases. (+)-8’-Hydroxyabscisic acid is a derivative of abscisic acid (ABA), a plant hormone involved in various physiological processes in plants, including seed dormancy, bud dormancy, and response to environmental stress. While prima r- ily studied for its role in plants, some research suggests that abscisic acid and its derivatives may have potential health benefits in humans. Abscisic acid has been investigated for its potential anti-inflammatory, antioxidant, and immunomodula- tory effects, which could potentially benefit human health. It has been studied for its potential therapeutic applications in conditions such as diabetes, obesity, i n- flammation, and autoimmune diseases. 4-hydroxycoumarin, also known as umbel- liferon, is a natural compound found in various plants, including citrus fruits, and is also produced synthetically. Research suggests that 4-hydroxycoumarin may have antioxidant, anti- inflammatory, and antimicrob ial properties. It has been st u- died for its potential use in the treatment of various conditions, including skin disorders, inflammatory diseases, and as a natural sunscreen agent. Scopoletin is a natural coumarin compound found in various plants, including members of the Apiaceae and Rutaceae families. Scopoletin has been studied for its potential pharmacological activities, including antioxidant, anti -inflammatory, antim i- crobial, and antitumor properties. It has been investigated for its potential the- rapeutic applications in conditions such as diabetes, neurodegenerative diseases, cancer, and cardiovascular disorders. 5. Conclusion In summary, using molecular docking studies, we effectively discovered the U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 110 Computational Molecular Bioscience number of compounds present in the LifeGreenTM that have the potential effic a- cy against breast cancer and uterine fibroids. From the results, we identified that out of 117 compounds that have been extracted from the LC-TOF MS analysis of LifeGreenTM, only 34 compounds have the more binding profile against the pr o- tein diseases. A total of 22 compounds demonstrated the remarkable capability to bid with all the six protein diseases while the remaining 12 compounds di s- played a more selective binding profile, where those only interact with a subset of two t o four protein diseases. Finally, the identified 34 compounds, which are associated with breast cancer and uterine fibroids, studied further by conducting comprehensive molecular docking simulations to extract information on their binding affinities and bi nding site preferences. This study underscores the need for further analytical and experimental studies to establish the safety and efficacy of the identified compounds. In the future, this experiment will be conducted in animal studies for both breast can cer and uterine fibroids using the standard procedure dosage recommended by the WHO and also will proceed with the phytochemical studies in order to identify the similariton between the co m- pounds that have been identified in this paper. Authors’ Contribution Conceptualization: Ummi Shahieda Lazaroo Binti Zurrein Shah Lazaroo, Chua Kia How. Methodology & Formal analysis: Ummi Shahieda Lazaroo Binti Zurrein Shah Lazaroo. Writing Original Draft : Ummi Shahieda Lazaroo Binti Zurrein Shah Lazaroo, Navanithan Sivanananthan. Discussion & Conclusion: Navanithan Sivanananthan. Writing Review & Editing: Ummi Shahieda Lazaroo Binti Zurrein Shah Lazaroo. Conflicts of Interest The authors declare no conflicts of interest regarding the publication of this paper.

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M/Z PEAK HIT GRAPH M/Z OVER INTENSITY (PEAK NO) RT (MIN) AREA SIGNAL TO NOISE RATION (S/N) PRECURSOR M/Z (CHROMATOGRA M) FRAGMENTATION (INTENSITY) COLLISION ENERGY (Ev) MOLECULAR FORMULA Mono isotopic Mass 218.9853 13 1.4 78569 66.7 AutoMSn (242.0010) 182.9641 598 190.9905 1277 200.9748 1325 201.9746 330 218.9853 13669 219.9862 3437 220.9840 1614 242.0023 2244 243.0023 616 265.0179 314 22.1 C7H6O6S 217.989 325.1181 15 1.9 49668 73 AutoMSn (309.1324) 117.0562 449 127.0402 1207 130.0530 166 145.0506 779 148.0623 194 163.0622 148 225.0879 188 226.0734 192 274.0966 220 292.1116 313 25.5 C20H20O4 324.136 163.0609 21 2 79661 88.6 AutoMSn (365.1092) 185.0435 195 203.0524 417 365.1107 521 C6H10O5 162.053 136.064 39 2.6 29720 70.1 AutoMSn (268.1092) 115.0393 134 119.0369 254 133.0525 263 136.0640 26078 137.0649 1163 268.1101 1319 C8H9NO 135.068 215.0195 45 3 44717 70.9 AutoMSn (130.0523) 130.0525 5009 163.0611 904 175.0267 1013 193.0369 2962 215.0195 11372 230.9944 2343 259.0974 1813 291.0178 2990 322.0834 2695 407.0505 4487 C6H6N4O3S 214.016 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 114 Computational Molecular Bioscience 251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) 147.0476 786 175.0395 4262 176.0436 370 207.0649 7163 208.0668 876 236.0678 1165 251.0943 215671 252.0974 23390 253.0976 1821 310.1664 1449 C13H14O5 250.084 1236 295.1332 69 7.6 297019 549 AutoMSn (589.2497) 120.0817 4190 180.1027 40659 181.1057 4225 200.0714 3809 235.1091 36428 236.1120 4538 260.0937 11591 277.1198 6738 295.1321 29595 296.1337 4089 C14H18N2O5 294.122 114.0925 97 9.1 410677 766.7 AutoMSn (340.2600) 114.0925 94001 115.0948 6098 209.1659 22605 226.1919 10772 227.1785 8580 228.1615 15199 322.2517 14478 340.2634 5283 435.3349 6041 453.3465 6718 C6H11NO 113.084 183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) 118.0919 111 122.0829 442 242.2479 2983 243.2535 413 272.2650 272 288.2918 397 289.2902 120 290.2724 5694 291.2730 948 C9H10O4 182.058 325.1181 15 1.9 49668 73 AutoMSn (309.1324) 117.0562 449 127.0402 1207 130.0530 166 145.0506 779 148.0623 194 163.0622 148 225.0879 188 226.0734 192 274.0966 220 292.1116 313 25.5 C20H20O4 324.136 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 115 Computational Molecular Bioscience 325.1181 15 1.9 49668 73 AutoMSn (309.1324) 117.0562 449 127.0402 1207 130.0530 166 145.0506 779 148.0623 194 163.0622 148 225.0879 188 226.0734 192 274.0966 220 292.1116 313 25.5 C20H20O4 324.136 325.1181 15 1.9 49668 73 AutoMSn (309.1324) 117.0562 449 127.0402 1207 130.0530 166 145.0506 779 148.0623 194 163.0622 148 225.0879 188 226.0734 192 274.0966 220 292.1116 313 25.5 C18H16N2O4 324.111 325.1181 15 1.9 49668 73 AutoMSn (309.1324) 117.0562 449 127.0402 1207 130.0530 166 145.0506 779 148.0623 194 163.0622 148 225.0879 188 226.0734 192 274.0966 220 292.1116 313 25.5 C19H16O5 324.1 325.1181 15 1.9 49668 73 AutoMSn (309.1324) 117.0562 449 127.0402 1207 130.0530 166 145.0506 779 148.0623 194 163.0622 148 225.0879 188 226.0734 192 274.0966 220 292.1116 313 25.5 C19H17ClN2O 324.103 325.1181 15 1.9 49668 73 AutoMSn (309.1324) 117.0562 449 127.0402 1207 130.0530 166 145.0506 779 148.0623 194 163.0622 148 225.0879 188 226.0734 192 274.0966 220 292.1116 313 25.5 C20H20O4 324.136 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 116 Computational Molecular Bioscience 325.1181 15 1.9 49668 73 AutoMSn (309.1324) 117.0562 449 127.0402 1207 130.0530 166 145.0506 779 148.0623 194 163.0622 148 225.0879 188 226.0734 192 274.0966 220 292.1116 313 25.5 C20H20O4 324.136 325.1181 15 1.9 49668 73 AutoMSn (309.1324) 117.0562 449 127.0402 1207 130.0530 166 145.0506 779 148.0623 194 163.0622 148 225.0879 188 226.0734 192 274.0966 220 292.1116 313 25.5 C12H20O10 324.106 325.1181 15 1.9 49668 73 AutoMSn (309.1324) 117.0562 449 127.0402 1207 130.0530 166 145.0506 779 148.0623 194 163.0622 148 225.0879 188 226.0734 192 274.0966 220 292.1116 313 25.5 C12H20O10 324.106 163.0609 21 2 79661 88.6 AutoMSn (365.1092) 185.0435 195 203.0524 417 365.1107 521 C6H10O5 162.053 163.0609 21 2 79661 88.6 AutoMSn (365.1092) 185.0435 195 203.0524 417 365.1107 521 C6H10O5 162.053 163.0609 21 2 79661 88.6 AutoMSn (365.1092) 185.0435 195 203.0524 417 365.1107 521 C6H10O5 162.053 163.0609 21 2 79661 88.6 AutoMSn (365.1092) 185.0435 195 203.0524 417 365.1107 521 C6H10O5 162.053 163.0609 21 2 79661 88.6 AutoMSn (365.1092) 185.0435 195 203.0524 417 365.1107 521 C6H10O5 162.053 163.0609 21 2 79661 88.6 AutoMSn (365.1092) 185.0435 195 203.0524 417 365.1107 521 C6H10O5 162.053 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 117 Computational Molecular Bioscience 163.0609 21 2 79661 88.6 AutoMSn (365.1092) 185.0435 195 203.0524 417 365.1107 521 C10H10O2 162.068 163.0609 21 2 79661 88.6 AutoMSn (365.1092) 185.0435 195 203.0524 417 365.1107 521 C10H10O2 162.068 163.0609 21 2 79661 88.6 AutoMSn (365.1092) 185.0435 195 203.0524 417 365.1107 521 C10H10O2 162.068 136.064 39 2.6 29720 70.1 AutoMSn (268.1092) 115.0393 134 119.0369 254 133.0525 263 136.0640 26078 137.0649 1163 268.1101 1319 C4H9NO4 135.053 136.064 39 2.6 29720 70.1 AutoMSn (268.1092) 115.0393 134 119.0369 254 133.0525 263 136.0640 26078 137.0649 1163 268.1101 1319 C8H9NO 135.068 136.064 39 2.6 29720 70.1 AutoMSn (268.1092) 115.0393 134 119.0369 254 133.0525 263 136.0640 26078 137.0649 1163 268.1101 1319 C4H9NO4 135.053 136.064 39 2.6 29720 70.1 AutoMSn (268.1092) 115.0393 134 119.0369 254 133.0525 263 136.0640 26078 137.0649 1163 268.1101 1319 C8H9NO 135.068 136.064 39 2.6 29720 70.1 AutoMSn (268.1092) 115.0393 134 119.0369 254 133.0525 263 136.0640 26078 137.0649 1163 268.1101 1319 C5H5N5 135.054 136.064 39 2.6 29720 70.1 AutoMSn (268.1092) 115.0393 134 119.0369 254 133.0525 263 136.0640 26078 137.0649 1163 268.1101 1319 C8H9NO 135.068 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 118 Computational Molecular Bioscience 136.064 39 2.6 29720 70.1 AutoMSn (268.1092) 115.0393 134 119.0369 254 133.0525 263 136.0640 26078 137.0649 1163 268.1101 1319 C8H9NO 135.068 215.0195 45 3 44717 70.9 AutoMSn (130.0523) 130.0525 5009 163.0611 904 175.0267 1013 193.0369 2962 215.0195 11372 230.9944 2343 259.0974 1813 291.0178 2990 322.0834 2695 407.0505 4487 C12H6O4 214.027 215.0195 45 3 44717 70.9 AutoMSn (130.0523) 130.0525 5009 163.0611 904 175.0267 1013 193.0369 2962 215.0195 11372 230.9944 2343 259.0974 1813 291.0178 2990 322.0834 2695 407.0505 4487 C5H11O7P 214.024 215.0195 45 3 44717 70.9 AutoMSn (130.0523) 130.0525 5009 163.0611 904 175.0267 1013 193.0369 2962 215.0195 11372 230.9944 2343 259.0974 1813 291.0178 2990 322.0834 2695 407.0505 4487 C5H11O7P 214.024 215.0195 45 3 44717 70.9 AutoMSn (130.0523) 130.0525 5009 163.0611 904 175.0267 1013 193.0369 2962 215.0195 11372 230.9944 2343 259.0974 1813 291.0178 2990 322.0834 2695 407.0505 4487 C5H11O7P 214.024 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 119 Computational Molecular Bioscience 215.0195 45 3 44717 70.9 AutoMSn (130.0523) 130.0525 5009 163.0611 904 175.0267 1013 193.0369 2962 215.0195 11372 230.9944 2343 259.0974 1813 291.0178 2990 322.0834 2695 407.0505 4487 C5H11O7P 214.024 215.0195 45 3 44717 70.9 AutoMSn (130.0523) 130.0525 5009 163.0611 904 175.0267 1013 193.0369 2962 215.0195 11372 230.9944 2343 259.0974 1813 291.0178 2990 322.0834 2695 407.0505 4487 C7H6N2O6 214.023 251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) 147.0476 786 175.0395 4262 176.0436 370 207.0649 7163 208.0668 876 236.0678 1165 251.0943 215671 252.0974 23390 253.0976 1821 310.1664 1449 C10H18O5S 250.087 251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) 147.0476 786 175.0395 4262 176.0436 370 207.0649 7163 208.0668 876 236.0678 1165 251.0943 215671 252.0974 23390 253.0976 1821 310.1664 1449 C10H18O5S 250.087 251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) 147.0476 786 175.0395 4262 176.0436 370 207.0649 7163 208.0668 876 236.0678 1165 251.0943 215671 252.0974 23390 253.0976 1821 310.1664 1449 C16H14N2O 250.111 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 120 Computational Molecular Bioscience 251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) 147.0476 786 175.0395 4262 176.0436 370 207.0649 7163 208.0668 876 236.0678 1165 251.0943 215671 252.0974 23390 253.0976 1821 310.1664 1449 C16H14N2O 250.111 251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) 147.0476 786 175.0395 4262 176.0436 370 207.0649 7163 208.0668 876 236.0678 1165 251.0943 215671 252.0974 23390 253.0976 1821 310.1664 1449 C17H14O2 250.099 251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) 147.0476 786 175.0395 4262 176.0436 370 207.0649 7163 208.0668 876 236.0678 1165 251.0943 215671 252.0974 23390 253.0976 1821 310.1664 1449 C16H14N2O 250.111 251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) 147.0476 786 175.0395 4262 176.0436 370 207.0649 7163 208.0668 876 236.0678 1165 251.0943 215671 252.0974 23390 253.0976 1821 310.1664 1449 C15H10N2O2 250.074 251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) 147.0476 786 175.0395 4262 176.0436 370 207.0649 7163 208.0668 876 236.0678 1165 251.0943 215671 252.0974 23390 253.0976 1821 310.1664 1449 C16H14N2O 250.111 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 121 Computational Molecular Bioscience 295.1332 69 7.6 297019 549 AutoMSn (589.2497) 120.0817 4190 180.1027 40659 181.1057 4225 200.0714 3809 235.1091 36428 236.1120 4538 260.0937 11591 277.1198 6738 295.1321 29595 296.1337 4089 C12H19N6OP 294.136 295.1332 69 7.6 297019 549 AutoMSn (589.2497) 120.0817 4190 180.1027 40659 181.1057 4225 200.0714 3809 235.1091 36428 236.1120 4538 260.0937 11591 277.1198 6738 295.1321 29595 296.1337 4089 C18H18N2O2 294.137 295.1332 69 7.6 297019 549 AutoMSn (589.2497) 120.0817 4190 180.1027 40659 181.1057 4225 200.0714 3809 235.1091 36428 236.1120 4538 260.0937 11591 277.1198 6738 295.1321 29595 296.1337 4089 C18H18N2O2 294.137 295.1332 69 7.6 297019 549 AutoMSn (589.2497) 120.0817 4190 180.1027 40659 181.1057 4225 200.0714 3809 235.1091 36428 236.1120 4538 260.0937 11591 277.1198 6738 295.1321 29595 296.1337 4089 C10H18N2O8 294.106 295.1332 69 7.6 297019 549 AutoMSn (589.2497) 120.0817 4190 180.1027 40659 181.1057 4225 200.0714 3809 235.1091 36428 236.1120 4538 260.0937 11591 277.1198 6738 295.1321 29595 296.1337 4089 C19H18O3 294.126 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 122 Computational Molecular Bioscience 295.1332 69 7.6 297019 549 AutoMSn (589.2497) 120.0817 4190 180.1027 40659 181.1057 4225 200.0714 3809 235.1091 36428 236.1120 4538 260.0937 11591 277.1198 6738 295.1321 29595 296.1337 4089 C15H18O6 294.11 183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) 118.0919 111 122.0829 442 242.2479 2983 243.2535 413 272.2650 272 288.2918 397 289.2902 120 290.2724 5694 291.2730 948 C9H10O4 182.058 183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) 118.0919 111 122.0829 442 242.2479 2983 243.2535 413 272.2650 272 288.2918 397 289.2902 120 290.2724 5694 291.2730 948 C9H10O4 182.058 183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) 118.0919 111 122.0829 442 242.2479 2983 243.2535 413 272.2650 272 288.2918 397 289.2902 120 290.2724 5694 291.2730 948 C9H10O4 182.058 183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) 118.0919 111 122.0829 442 242.2479 2983 243.2535 413 272.2650 272 288.2918 397 289.2902 120 290.2724 5694 291.2730 948 C9H10O4 182.058 U. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. DOI: 10.4236/cmb.2024.142004 123 Computational Molecular Bioscience 183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) 118.0919 111 122.0829 442 242.2479 2983 243.2535 413 272.2650 272 288.2918 397 289.2902 120 290.2724 5694 291.2730 948 C9H10O4 182.058 183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) 118.0919 111 122.0829 442 242.2479 2983 243.2535 413 272.2650 272 288.2918 397 289.2902 120 290.2724 5694 291.2730 948 C9H10O4 182.058 183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) 118.0919 111 122.0829 442 242.2479 2983 243.2535 413 272.2650 272 288.2918 397 289.2902 120 290.2724 5694 291.2730 948 C9H10O4 182.058 183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) 118.0919 111 122.0829 442 242.2479 2983 243.2535 413 272.2650 272 288.2918 397 289.2902 120 290.2724 5694 291.2730 948 C6H14O6 182.079 183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) 118.0919 111 122.0829 442 242.2479 2983 243.2535 413 272.2650 272 288.2918 397 289.2902 120 290.2724 5694 291.2730 948 C6H14O6 182.079