{"paper_id":"565eb5bf-1b2b-450d-a1a0-810ee147d9f9","body_text":"Computational Molecular Bioscience, 2024, 14, 59-123 \nhttps://www.scirp.org/journal/cmb \nISSN Online: 2165-3453 \nISSN Print: 2165-3445 \n \nDOI: 10.4236/cmb.2024.142004  Jun. 13, 2024 59 Computational Molecular Bioscience \n \n \n \n \nMolecular Docking Studies of Botanical \nBeverage Mix Berries (LIFEGREENTM) against \nBreast Cancer Cells from Targeted Protein \n1QQG, 7B5Q & 7B5O & Uterine Fibroid from \nTargeted Protein 2AYR, 6T41 & 3GRF \nUmmi Shahieda Lazaroo Bt Zurrein Shah Lazaroo1,2,3,4, Navanithan Sivanananthan4,  \nChua Kia How5,6,7 \n1Department of Scientific Research of Lifetree Asia Sdn Bhd, Selangor, Malaysia \n2Department of Scientific Research of Lifetree Biotech Sdn Bhd, Selangor, Malaysia \n3Department of Research and Development of Nutrimedt Sdn Bhd, Selangor, Malaysia \n4Faculty of Health & Life Sciences, Management & Science University, Selangor, Malaysia \n5Lifetree Asia Sdn Bhd, Selangor, Malaysia \n6Lifetree Biotech Sdn Bhd, Selangor, Malaysia \n7Nutrimedt Sdn Bhd, Selangor, Malaysia \n \n \n \nAbstract \nFibroids, also called leiomyomas or myomas, are communal tumors of the \nmuscle or uterine wall that affect about 20% of females who are of reprodu c-\ntive age. They can look as if singly or in clusters, and they often cease to grow \nafter menopause. Fibroids can be classified as intramural, sub serosal, p e-\ndunculated, or submucosal based on where they are positioned in the uterus. \nAlthough fibroid s are benign, they can grow quickly and cause a range of \nsymptoms, such as pelvic pressure, heavy menstrual flow, and infertility. As a \nresult, fibroids are a main reason behind hysterectomy surgeries. The majori-\nty of cases of breast cancer are ductal and lobular cancers, making it the \nsecond utmost common cancer in women international. Gene mutations like \nthose in BRCA1 or BRCA2 knowingly raise the risk of breast and other ca n-\ncers, typically with an earlier cancer onset. Cancer risk is influenced by a \ncomplex interplay of genetic abnormalities, environmental factors, and lifestyle \nselections. Further research into these relations is domineering. Although they \nare common in uterine leiomyomas, especially multiple leiomyomas, MED12 \nmutations do not significan tly correlate with tumor size. These mutations \nhave also been noticed in smooth muscle tumors and leiomyosarcomas, two \nHow to cite this paper: Shahieda Lazaroo \nBt Zurrein Shah Lazaroo, U., Sivanananthan, \nN. and How, C.K. (2024) Molecular Docking \nStudies of Botanical Bev erage Mix Berries \n(LIFEGREENTM) against Breast Cancer Cells \nfrom Targeted Protein 1QQG, 7B5Q & 7B5O \n& Uterine Fibroid from Targeted Protein \n2AYR, 6T41 & 3GRF. Computational Mole-\ncular Bioscience, 14, 59-123. \nhttps://doi.org/10.4236/cmb.2024.142004 \n \nReceived:  April 24, 2024 \nAccepted: June 10, 2024 \nPublished:  June 13, 2024 \n \nCopyright © 2024 by author(s) and  \nScientific Research Publishing Inc. \nThis work is licensed under the Creative \nCommons Attribution International  \nLicense (CC BY 4.0). \nhttp://creativecommons.org/licenses/by/4.0/  \n  \nOpen Access\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 60 Computational Molecular Bioscience \n \nother types of uterine cancer. The identification of MED12 mutations as the \nsole genetic abnormality originates in leiomyomas raises the opportunity of a \nrole in the genesis of cancer. 10%  - 15% of women who are of reproductive \nage have endometriosis, which grants serious difficulties because of its \nchronic nature and range of clinical symptoms. Even after effective surgeries, \nissues reoccur often, adding to the enormous financial burden. The effects of \nMED12 mutations have been experiential in recent studies examining the \nmolecular causes of endometriosis -associated infertility, which have shown \nanomalies in cellular connections and signali ng cascades. Computational \ntechniques were used in this study to investigate LifeGreen TM’s potential to \nprevent uterine fibroids and breast cancer. The efficacy of LifeGreen TM as a \npreventive measure or a treatment for common gynecological matters was \nexamined and modeled. We investigated the mechanisms underlying Life-\nGreenTM’s benefits in the treatment of uterine fibroids and breast cancer using \ncomputational techniques. Our research contributes to our understanding of \nits potential therapeutic benefits for women’s health. \n \nKeywords \nUterine Fibroid, Breast Cancer, Molecular Docking, IRS Protein, BRCA1, \nBRCA2, MED12-a, Endometriosis \n \n1. Introduction \n1.1. LifeGreen™  \nLifeGreen™ Cactus Powder is known as a highly concentrated cactus extract which \nmade using 22 different types of fruit and vegetable extracts, Italian mixed berries, \nand TRUEBROC® broccoli seed extract, a US-patented ingredient, Oxxynea® is a \npopular French health drink. According to studies, cactus polysaccharides having \nthe ability to boost immunity and inhibit abnormal cell developments when con-\nsumed over time. In addition, Truebroc® broccoli seed extract promotes aberrant \ncell death, reduces abnormal cell blood supply, and prevents abnormal cell repro-\nduction and spread. Figure 1 shows the packaging of the LifeGreen™ Beverage [1]. \n1.2. Ingredients and Benefits \nLifeGreen™ contains Italian Premium Mixed Berries (Blueberry, Blackcurrant, \nRaspberry, Elderberry, Red Grape, Strawberry, Cranberry), Cactus Powder, Oxxy-\nnea® (Green Tea Extract, Red Grape Extract, White Grape Extract, Bilberry, Car-\nrot, Grapefruit, Papaya, Pineapple, Strawberry, Apple, Apricot, Cherry, Ora nge, \nBroccoli, Green Cabbage, Onion, Garlic, Olive, Cucumber, Blackcurrant, Tomato, \nAsparagus), TRUEBROC\n® broccoli seed extract, and immune deficiencies include \nfatigue and weakness, allergies, and the need to restore immunity. Moreover, Life-\nGreen™’s ingre dients, particularly Cactus Powder, have been extensively re-\nsearched and studied for their anticancer effects. Figure 2  shows the picture of \nLifeGreen™ Beverage drink [1]. \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 61 Computational Molecular Bioscience \n \n \nFigure 1 . Lifegreen™ Beverage sachet and packaging [1]. \n \n \nFigure 2 . Lifegreen™ Beverage drink [1]. \n1.3. Cancer Cell Growth \nThe body creates molecules are known as growth factors, which govern cell divi-\nsion. Growth factors occur in a number of types and each operate s differently. \nSome growth factors advise cells on how to specialize and what type of cell they \nshould become. Some cause cell division and proliferation to generate new cells. \nCells can be told to cease growing or die. Growth factors operate by binding to \ncell surface receptors. This sends a signal to the cell’s inside, initiating a sequence  \nof complex chemical reactions. There are multiple different growth factors [2] . \nThese include epidermal growth factor (EGF), vascular endothelial growth factor \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 62 Computational Molecular Bioscience \n \n(VEGF), platelet -derived endothelial growth factor (PDGF), and fibroblast \ngrowth factor (FGF), which all regulate cell growth. Each growth factor works \nby attaching to it s corresponding cell surface receptor. For example, epidermal \ngrowth factor (EGF) interacts with the EGFR. Tyrosine Kinases are chemical \nmessengers (enzymes) that control cells ’ capacity to divide and grow. Similar \nto an “ on-off” switch feature , Tyrosine kinase is activated when a growth fac-\ntor attaches to a cell ’s surface. This will prompt cell division as shown in Figure \n3 [2]. \n1.4. Uterine Fibroids \nFibroids, also known as leiomyoma or myoma, are frequent tumors that develop \nin the uterine wall or muscle. Fibroids in women of their reproductive years can \npresent as single or multiple growths. Fibroids are uncommon in women who \nhave not yet started menstruation, although they afflict around 20% of women of \nreproductive age. Usually, growth slows after menopause [3] . Fibroids can be \nclassified into various types based on where they exist. The submucosal fibroid is \na fibroid that usually develops inside the uterus while intramural fibroid is a fi-\nbroid that develops within the musculature of the uterine wall. Moreover, su b-\nserosa fibroid is known as a uterine fibroid that protrudes from the body. Lastly, \na fibroid with a stalk that extent from the uterus into the pelvis or else disco v-\nered inside the inner uterine cavity and extends through cervix is known as p e-\ndunculated fibroid. Figure 4  shows the locations of the various types of fibroids \n[4]. \nDespite their benign nature, they can grow rapidly and dramatically [5] . They \nproduce heavy and irregular menstrual bleeding (HMB), which leads to severe \nanemia, dysmenorrhea, pelvic pressure and discomfort, urinary incontinence, \ndyspareunia, infertility, premature labor, and recurrent early and late pregnancy \nlosses [6]. More than 70% of women have UFs, with only around 30% experiencing  \n \n \nFigure 3 . The cancer cells growth factor that affects the body [2]. \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 63 Computational Molecular Bioscience \n \n \nFigure 4 . The common locations for uterine fibroid to occur [4]. \n \nsymptoms, making UFs the most common clinical cause for hysterectomy, \nwhich removes a woman’s capacity to produce early [7]. \n1.5. Breast Cancer Cells (IRS-1) \nBreast cancer is the world ’s second most frequent illness, affecting more women \nthan any other malignancy. The two most common types of breast cancer are \nductal and lobular. In situ (localized to a single location), ductal and lobular \ncancers account for 85 % - 90% and 8% of all breast cancers, respectively. In a d-\ndition, aggressive inflammatory breast cancers exist, as do invasive ductal and lo-\nbular tumors. Although chemotherapy has been demonstrated to improve breast \ncancer patients’ survival rates, a significant minority of individuals only have a \nbrief response to the treatment before succumbing to metastatic disease. IRS1 \nhas been shown to enhance breast cancer cell growth rather than prevent ing \nmetastasis [8]. \nFigure 5  shows the process of IRS- 1 occurring before metas tasis \nhappen in which responsible for the growth of breast cancer cell [9]. \n1.6. BRCA 1 & BRCA 2 \nPeople protecting injurious variants in BRCA1 or BRCA2 genes aspect  signifi-\ncantly higher risks of emerging various cancers, together with breast, ovarian, \nfallopian tube, and primary peritoneal cancers [10] . The in cidence of these m u-\ntations significantly rises the lifetime risk of cancer onset, with pretentious \npeople often facing earlier age of cancer diagnosis equated to the general popula-\ntion. While BRCA mutations are sturdily associated with increased cancer risk, \nthe extent of risk variability amongst carriers is inclined by various factors [10] . \nThese issues include environmental contacts, lifestyle choices, hormonal effects, \nand genetic modifiers that may interrelate with BRCA mutations to modulate \ncancer susceptibility. In spite of extensive research, some of these factors remain \nsomewhat characterized, highlighting the need for further investigation into the \ncomplex in terplay between genetic and environmental determinants of cancer \nrisk [11]. \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 64 Computational Molecular Bioscience \n \n \nFigure 5 . The process of IRS -1 occurring before metastasis happens causes breast cancer \ncell growth. \n1.7. MED12-a \nTumors did not exhibit more than one mutation, and previous research did not \naddress the MED12 mutation status of multiple leiomyomas in one patient. \nMultiple uterine leiomyomas appeared to have a higher incidence of MED12 \nmutations compared to single uterine leiomyomas (72.73% versus 59.26%), a l-\nthough this difference was not statistically significant [12]. Moreover, patients \nwith multiple leiomyomas exhibited smaller mean sizes of leiomyomas signifi-\ncantly, consistent with previous research. Nearly twofold difference in MED12 \nmutation frequency between multiple and single uterine leiomyomas from a c o-\nhort of 122 patients. However, they could not establish a significant association \nbetween MED12 mutation and tumor size [12] . Therefore, larger sample sizes \nare required to evaluate the relationship between MED12 mutation frequency \nand the number or size of uterine leiomyomas. MED12 mutation has also been \ndetected in other uterine tumors such as leiomyosarcoma s and smooth muscle \ntumors of uncertain malignant potential, but not in tumors of other organs. I n-\nterestingly, breast fibroadenoma also harbored highly frequent MED12 mut a-\ntions. Whole exome sequencing revealed no genes other than MED12 mutation \nin MED12 mutation-positive and -negative leiomyomas, suggesting that MED12 \nmutation alone may be sufficient for leiomyoma tumorigenesis [12]. \n1.8. Endometriosis \nEndometriosis is a multifaceted gynecological state affecting 10%  - 15% of r e-\nproductive-age females and around 70% of women facing persistent pelvic pain. \nWhile the ovaries and pelvic peritoneum are the primary sites of endometriotic \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 65 Computational Molecular Bioscience \n \nlesions, they can also patent in various other locations inside the body [13]. The \netiologic of endometriosis- associated pain remains poorly unspoken, with i n-\nflammation broadly believed to play a significant role. Despite fruitful surgical \ninterventions, recurrence of symptoms is communal, underscoring the chronic \nnature of the condition. Endometriosis poses a substantial economic burden, \nparticularly in countries like India, with costs estimated a t approximately 1 to 2 \nlakhs per affected woman. The participation of structures such as the uterosacral \nligaments, posterior vaginal wall, rectovaginal space, intestines, and urinary sy s-\ntem is frequently experiential in endometriosis cases. Various studie s have e x-\nplored the molecular mechanisms underlying endometriosis -associated infertili-\nty, highlighting factors such as apoptosis, cell cycle alterations, and oxidative \nstress in granulosa cells. Moreover, recent research endeavors have sought to \nelucidate the role of MED12 mutations in endometriosis pathogenesis, revealing \ndisruptions in cellular interactions and signaling pathways [13]. \n2. Methodology \nLifeGreen™ uses proteomic computing to identify compounds that can interact \nwith IRS-1 (breast cancer cells) as well as BRCA 1 and 2 - 1QQG, 7B5Q & 7B5O \nand endometriosis and mutated Med -a (uterine fibroid) -2AYR, 3GRF & 6T41 \nproteins [14]. Furthermore, additional research and investigations on each effec-\ntive molecule with the highest binding energy and its advantages to human \nhealth. Figure 6  shows the general methodology of this research while Figure 7  \n \n \nFigure 6 . Overview of the whole methodology \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 66 Computational Molecular Bioscience \n \nuntil Figure 1 0 shows the in-depth methodology. \n2.1. Phase 1: Metabolomics Analysis \nIn Figure 7 , in chromatography process, separation was performed using The r-\nmo Scientific C18 column (Acclaim TM Polar Advantage II, 3 × 150 mm, 3 um \nparticle size) on an UltiMate 3000 UHPLC system (Dionex) [15] . Gradient elu-\ntion was performed at flow rate of 0.4  ml/min and 40˚C  column temperature \nusing H2O + 0.1% Formic Acid (A) and 100% ACN (B) with 22 minutes total \nrun time. The injection volume o f sample was 5 ul. The gradient started at 5% B \n(0 - 3 min); 80% B (3 - 10 min); 80% B (10 - 15 min) and 5% B (15 - 22 min). For \nmass-spectrophotometry, sample is analyzed with positive ionization parameter \n[15], Table 1 . \nIn data processing steps, the accurate mass data of the molecular ions, pr o-\nvided by the TOF analyzer, were processed by Compass Data Analysis software  \n \n \nFigure 7 . Phase 1: Metabolomics Analysis. \n \nTable 1 . Positive mode ionization. \nAcquisition Parameter  \nSource Type  ESI Ion Polarity  Positive  Set Nebulizer  2.0 \nFocus Active Set Capillary 4500 V Set Dry Heater 300˚C \nScan Begin 50 m/z Set End Place Offset −500 V Set Dry Gas 8.0 l/min \nScan End 1500 m/z Set Collision Cell RF 200.0 Vpp Set Divert Valve Waste \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 67 Computational Molecular Bioscience \n \n(Bruker Daltonik GmbH). Further process by using metfrag (In silico fragmen-\ntation for computer assisted identification of metabolite mass spectra) in order \nto pull out the list of compound present in each peak presented by LC -MS/TOF \n[16] gi-bin/portal.py#welcome.  \n2.2. Phase 2: Compound Analysis \nBefore proceeding with the analysis, each compound underwent meticulous \nquality control procedures employing ZoBio by NMR, ensuring the integrity \nand reliability of subsequent results. Following this initial step, the compounds \nwere subjected to an array of sophisticated analyses aimed at elucidating their \nproperties and functions. Significance testing was employed to discern me a-\nningful patterns and deviations within the dataset, shedding light on potential \nbiological implications. Quantitation- pattern recognition techniques were a p-\nplied to discern quantitative relationships and trends within the data, facilitat-\ning a deeper understandin g of compound behavior. Compound assignment \nmethodologies were utilized to accurately identify and classify each co m-\npound, ensuring precise cataloguing and characterization. Finally, functional \nassessment protocols were implemented to assess the biological  activities and \npotential applications of the compounds, providing valuable insights for further \nresearch, \nFigure 8 . \n2.3. Phase 3: Protein Analysis \nDuring this third phase of the study Figure 9 , an extensive protein analysis was \nundertaken utilizing resources available on the Protein Data Bank (PDB) \nhttps://www.rcsb.org/\n website [17]. This involved a meticulous examination of \nprotein structures and relevant data to glean insights into the molecular  \n \n \nFigure 8 . Phase 2: Compound analysis. \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 68 Computational Molecular Bioscience \n \n \nFigure 9 . Phase 3: Protein analysis of 1QQG, 2AYR, 3GRF, 6T41, 7B5Q and 7B5O. \n \nTable 2 . Breast cancer and uterine fibroid flow. \nBreast Cancer Uterine Fibroid \nIRS-1 BRCA1 & BRCA 2 Endometriosis MED a (mutated) \nIRS1 was found to promote  \nbreast cancer cell proliferation \nXPB & XPD Tissue lining grows  \noutside the uterus CDK 8/Cyclin C. \nTFIIH \n1QQG 7B5Q & 7B5O 2AYR 3RGF & 6T41 \n \nunderpinnings of the investigated conditions. To augment our understanding, a \nthorough literature review was conducted. This involved delving into published \nstudies and scientific literature to identify and elucidate the roles of specific pro-\nteins implicated in breast cancer and uterine fibroids. By synthesizing inform a-\ntion from various sources, we aimed to pinpoint key proteins associated with \nthese diseases, providing a comprehensive foundation for our research. \nTable 2  \nserves as a comprehensive repository of the gathered results pertaining to both \nbreast cancer and uterine fibroids. This tabulated data offers a detailed overview \nof the proteins identified and their respective implications in the pathogenesis of \nthese conditions. Through meticulous documentation and analysis, we aim to \nuncover potential biomarkers and therapeutic targets, contributing to the a d-\nvancement of diagnostics and treatment strategies for breast cancer and uterine  \nfibroids. \n2.4. Phase 4 and Phase 5: Protein-Ligand Interactions and  \nMolecular Docking \nIn Figure 1 0, proteomic molecular docking represents a sophisticated computa-\ntional approach used in the field of proteomics to predict and analyses the  \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 69 Computational Molecular Bioscience \n \n \nFigure 1 0. Phase 4: Protein-Ligand Interaction (RPBS) and Phase 5: Molecular Docking. \n \ninteractions between proteins and other molecules,  Achilles Blind Docking \nServer, https://bio-hpc.ucam.edu/achilles/. This method integrates principles \nfrom both proteomics and molecular docking, leveraging computational alg o-\nrithms to simulate and predict the binding affinity and spatial or ientation of \nproteins with various ligands, substrates, or inhibitors. The process typically b e-\ngins with the identification of target proteins of interest through proteomic \ntechniques such as mass spectrometry or protein microarrays. Once the target \nproteins are identified, molecular docking algorithms are employed to simulate \nthe binding interactions between these proteins and small molecules, peptides, \nor other proteins. These docking algorithms use complex scoring functions and \nsearch algorithms to explo re the conformational space and predict the most \nenergetically favorable binding poses between the proteins and their ligands. By \nanalyzing these predicted binding conformations, researchers can gain valuable \ninsights into the molecular mechanisms underlyi ng protein-ligand interactions, \nincluding the identification of key binding residues and structural determinants. \nProteomic molecular docking holds significant promise for various applications \nin drug discovery, structural biology, and systems pharmacology . It enables the \nscreening of large compound libraries to identify potential drug candidates or \nlead compounds that modulate the activity of target proteins implicated in di s-\neases. Additionally, it facilitates the elucidation of protein -protein interaction  \nnetworks and signaling pathways, providing valuable insights into complex bio-\nlogical processes and disease mechanisms. \n3. Results \n3.1. LC-TOF-MS \nIn Figure 1 1, the chemical composition of LifeGreen TM samples, LC- TOF MS \nanalysis emerged as a pivotal investigative tool, yielding a comprehensive co m-\npound spectrum graph delineating the peaks indicative of molecular entities  \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 70 Computational Molecular Bioscience \n \n \nFigure 1 1. Shows the compound spectrum graph and its peaks. \n \npresent within the  samples. Notably, the analysis revealed approximately 140 \ndistinct compounds, with varying degrees of detectability attributable to the i n-\nherent limitations posed by sample composition and instrumental sensitivity. \nWithin this array, certain compounds wer e readily discernible, while others r e-\nmained undetected, a phenomenon attributed to the relatively sparse population \nof compounds within the samples. \nIn our examination of the compound spectrum graph, each peak was charac-\n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 71 Computational Molecular Bioscience \n \nterized by its corresponding retent ion time (RT) in minutes, intensity, si g-\nnal-to-noise ratio, maximum mass -to-charge ratio (m/z), and area under the \npeak. These parameters collectively provided insights into the abundance, purity, \nand spectral characteristics of the identified compounds. F urthermore, to au g-\nment our understanding of the molecular identities associated with the observed \npeaks, collision energy (eV) values were assigned to selected peaks, facilitating \nsubsequent tandem mass spectrometry (MS/MS) analyses. \nTo unveil the chemical  identities corresponding to the observed peaks, we \nemployed the computational tools afforded by MetFrag. Leveraging the spectral \ninformation encapsulated within the LC -TOF MS data, MetFrag facilitated the \ndeconvolution of complex spectra, enabling the retrieval and annotation of puta-\ntive compounds associated with each peak, \nTable 3 . \n \nTable 3 . Shows the compound spectrum list of LifeGreenTM. \n# RT [min]  Area Int. Type  I S/N Chromatogram  Max. m/z  \n1 0.3 14319 MolFeatur 2290 25.4  158.9642 \n2 1.1 7552 MolFeatur 364 8.1  892.302 \n3 1.1 24660 MolFeatur 858 4.9  1297.4271 \n4 1.1 16541 MolFeatur 814 3.9  1135.3787 \n5 1.1 25047 MolFeatur 2021 7.7  325.1159 \n6 1.1 13410 MolFeatur 364 3.9  1378.9585 \n7 1.1 21376 MolFeatur 1240 5.8  973.3256 \n8 1.1 11438 MolFeatur 889 9.9  487.165 \n9 1.1 17711 MolFeatur 1216 13.5  649.221 \n10 1.3 46188 MolFeatur 2300 3.2  125.9873 \n11 1.3 11554 MolFeatur 595 3.3  153.0319 \n12 1.4 41690 MolFeatur 2138 7.8  143.998 \n13 1.4 78569 AutoMS(n) 5811 66.7 AutoMSn (242.0010) 218.9853 \n14 1.8 50134 AutoMS(n) 9113 116 AutoMSn (147.0776) 147.0777 \n15 1.9 49668 AutoMS(n) 5860 73 AutoMSn (309.1324) 325.1181 \n16 1.9 222361 AutoMS(n) 14,913 72.4 AutoMSn (163.0613) 325.118 \n17 1.9 462886 AutoMS(n) 63,448 664.5 AutoMSn (325.1166) 325.1181 \n18 1.9 59405 AutoMS(n) 5810 63 AutoMSn (203.0533) 325.118 \n19 1.9 3860 MolFeatur 2800 49.8  265.0968 \n20 1.9 781734 MolFeatur 41,601 425.2  325.118 \n21 2 79661 AutoMS(n) 8555 88.6 AutoMSn (365.1092) 163.0609 \n22 2 11871 MolFeatur 8484 96.9  365.1103 \n23 2.1 66073 MolFeatur 1004 10.8  145.0516 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 72 Computational Molecular Bioscience \n \nContinued  \n24 2.1 170832 MolFeatur 1939 21.2  163.0615 \n25 2.2 188364 MolFeatur 1939 21.2  163.0614 \n26 2.2 35326 MolFeatur 459 3.7  973.3298 \n27 2.2 41514 MolFeatur 489 4.5  1054.3589 \n28 2.2 50877 MolFeatur 684 7.6  1135.3881 \n29 2.3 14168 MolFeatur 283 3.1  1236.9162 \n30 2.3 46769 MolFeatur 563 6.3  1216.9174 \n31 2.4 7917 MolFeatur 984 9.3  497.1519 \n32 2.4 6183 MolFeatur 612 6.6  198.0778 \n33 2.4 49982 MolFeatur 534 3  1297.4404 \n34 2.5 23467 MolFeatur 383 3.3  1378.9711 \n35 2.5 18898 MolFeatur 345 3.8  1387.9728 \n36 2.5 11851 MolFeatur 318 3.5  1460 \n37 2.5 8619 MolFeatur 319 3.8  1469.0016 \n38 2.6 12885 MolFeatur 1764 13.7  307.0905 \n39 2.6 29720 AutoMS(n) 7065 70.1 AutoMSn (268.1092) 136.064 \n40 2.6 4844 MolFeatur 356 4  117.0568 \n41 2.6 17930 MolFeatur 954 10.4  163.062 \n42 2.7 24200 MolFeatur 1679 8.2  294.1604 \n43 2.8 24504 MolFeatur 749 3.3  261.0418 \n44 2.8 7757 MolFeatur 343 3.9  1423.7855 \n45 3 44717 AutoMS(n) 6350 70.9 AutoMSn (130.0523) 215.0195 \n46 3.1 19762 MolFeatur 8840 62.6  230.994 \n47 3.1 131922 AutoMS(n) 8910 93 AutoMSn (230.9921) 215.0192 \n48 3.1 316934 AutoMS(n) 24,784 315.4 AutoMSn (407.0478) 215.0193 \n49 3.1 749494 AutoMS(n) 54,493 349.9 AutoMSn (215.0178) 215.0192 \n50 3.1 102957 AutoMS(n) 7504 67.5 AutoMSn (193.0361) 215.0192 \n51 3.1 103373 AutoMS(n) 6945 60.7 AutoMSn (308.0244) 215.0191 \n52 3.1 95222 MolFeatur 4404 48.9  193.0371 \n53 3.2 301781 MolFeatur 12,438 138.2  215.0194 \n54 3.3 697424 MolFeatur 20,160 149.4  215.0191 \n55 3.7 10392 MolFeatur 1057 8.2  302.0911 \n56 3.7 8383 MolFeatur 909 7.5  166.0876 \n57 4.1 8535 MolFeatur 321 3.6  328.1426 \n58 4.4 11022 MolFeatur 710 4.9  277.1583 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 73 Computational Molecular Bioscience \n \nContinued  \n59 5.4 12378 MolFeatur 548 4.7  460.0383 \n60 5.4 20326 MolFeatur 925 10.3  358.1004 \n61 5.4 14422 MolFeatur 737 8.2  196.0475 \n62 6.4 11266 MolFeatur 873 9.5  311.1273 \n63 6.5 6705 MolFeatur 361 4  361.1123 \n64 7 3669 MolFeatur 315 3.5  389.1815 \n65 7.3 247093 AutoMS(n) 60,926 887.7 AutoMSn (310.1649) 251.0943 \n66 7.4 3888 MolFeatur 306 3.4  896.4008 \n67 7.5 5669 MolFeatur 416 4.6  476.2284 \n68 7.6 1454 MolFeatur 396 6.8  703.351 \n69 7.6 297019 AutoMS(n) 60,619 549 AutoMSn (589.2497) 295.1332 \n70 7.6 1613847 AutoMS(n) 294,260 3251.2 AutoMSn (295.1298) 295.1332 \n71 7.6 27313 AutoMS(n) 5368 44.5 AutoMSn (611.2289) 295.1332 \n72 7.6 36156 MolFeatur 17,093 379.8  590.255 \n73 7.6 1860691 MolFeatur 99,937 41.2  295.1331 \n74 7.7 8045 MolFeatur 5178 44.1  287.0581 \n75 7.7 94945 AutoMS(n) 9837 82.1 AutoMSn (279.0470) 295.1315 \n76 7.7 39453 AutoMS(n) 5249 51.3 AutoMSn (449.1033) 295.1315 \n77 7.7 143129 MolFeatur 6626 22.2  279.0498 \n78 7.8 39909 MolFeatur 1495 7.3  211.06 \n79 7.9 5869 MolFeatur 451 10  1156.4916 \n80 8 6146 MolFeatur 844 6.9  453.2078 \n81 8 8300 MolFeatur 459 5.1  247.0831 \n82 8 7534 MolFeatur 1513 16.8  195.0889 \n83 8.2 11008 MolFeatur 1359 30.2  765.2588 \n84 8.2 4727 MolFeatur 631 6.7  635.2532 \n85 8.3 49149 AutoMS(n) 7874 68.1 AutoMSn (362.2406) 362.2436 \n86 8.3 28410 AutoMS(n) 6319 66.5 AutoMSn (340.2600) 322.2509 \n87 8.4 16121 MolFeatur 1257 12  481.1332 \n88 8.6 2674 MolFeatur 317 3.5  787.2362 \n89 8.7 16551 MolFeatur 846 9.4  141.0545 \n90 8.8 3726 MolFeatur 1266 4.9  429.1734 \n91 8.8 103448 AutoMS(n) 12,920 116.5 AutoMSn (475.3227) 475.3273 \n92 8.8 52265 AutoMS(n) 6608 60.4 AutoMSn (453.3422) 453.3441 \n93 8.9 11754 MolFeatur 723 8  625.1779 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 74 Computational Molecular Bioscience \n \nContinued  \n94 8.9 76886 AutoMS(n) 11,827 104.2 AutoMSn (566.4229) 566.4304 \n95 8.9 115283 AutoMS(n) 14,400 107.5 AutoMSn (588.4059) 588.4125 \n96 8.9 57648 AutoMS(n) 9152 93.9 AutoMSn (283.7180) 588.4104 \n97 9.1 410677 AutoMS(n) 72,826 766.7 AutoMSn (340.2600) 114.0925 \n98 9.1 212347 AutoMS(n) 39,912 260.3 AutoMSn (679.5096) 679.5153 \n99 9.1 175923 AutoMS(n) 28,600 176.1 AutoMSn (701.4907) 701.4972 \n100 9.1 855268 MolFeatur 32,414 37.6  114.0925 \n101 9.2 257033 MolFeatur 9413 209.2  701.4972 \n102 9.2 549469 AutoMS(n) 69,035 633.7 AutoMSn (396.8013) 396.8049 \n103 9.2 137634 AutoMS(n) 17,220 114.8 AutoMSn (814.5751) 814.5812 \n104 9.2 122930 AutoMS(n) 15,509 123.3 AutoMSn (792.5921) 396.8049 \n105 9.3  AutoMS(n) 5379  AutoMS (n): TIC + MS2 (340.2587) \n106 9.3 207392 AutoMS(n) 28,786 404.8 AutoMSn (274.2738) 274.2773 \n107 9.3 50477 AutoMS(n) 6733 71.2 AutoMSn (318.2989) 274.277 \n108 9.5 7283 MolFeatur 638 6.2  155.047 \n109 9.5 26104 MolFeatur 12,699 61.1  183.0786 \n110 9.5 75436 AutoMS(n) 12,781 152.9 AutoMSn (183.0768) 183.0786 \n111 9.5 34410 AutoMS(n) 7875 97.2 AutoMSn (290.2694) 183.0787 \n112 9.5 13638 MolFeatur 7844 151.7  290.2727 \n113 10 18573 MolFeatur 2371 3.6  389.2515 \n114 10.1 8421 MolFeatur 1406 15.6  304.3024 \n115 10.1 3945 MolFeatur 618 4.9  318.3028 \n116 10.4 8374 MolFeatur 998 7.1  188.0479 \n117 10.4 21705 MolFeatur 2542 3.8  447.2916 \n118 10.9 12476 MolFeatur 1335 5.2  505.3321 \n119 11.3 2349 MolFeatur 273 3  235.1688 \n120 11.5 8109 MolFeatur 621 6  343.2974 \n121 11.6 25151 MolFeatur 3668 40.8  288.2552 \n122 11.8 12750 MolFeatur 2517 36.2  225.1943 \n123 12.3 37695 MolFeatur 1329 14.8  239.1618 \n124 12.3 15554 MolFeatur 2306 25.6  199.1688 \n125 12.3 12451 MolFeatur 1074 8.8  209.1528 \n126 13 4050 MolFeatur 693 3.8  421.2309 \n127 13.2 25382 MolFeatur 3875 43.1  383.2033 \n128 13.2 115665 MolFeatur 544 6  506.3295 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 75 Computational Molecular Bioscience \n \nContinued  \n129 13.9 31914 MolFeatur 4401 48.9  425.2118 \n130 13.9 6244 MolFeatur 835 9.3  441.1855 \n131 14.2 5321 MolFeatur 726 8.1  370.2006 \n132 15 277893 MolFeatur 832 6.1  907.7663 \n133 15 138295 MolFeatur 484 3.9  404.3161 \n134 15.4 9501 MolFeatur 870 8  398.2321 \n135 16.2 324760 MolFeatur 933 5.5  758.5641 \n136 16.3 398562 MolFeatur 1304 6.2  603.5311 \n137 16.6 54641 MolFeatur 379 3.8  782.5625 \n138 16.7 257755 MolFeatur 1157 9  756.5476 \n139 17.3 43661 MolFeatur 317 3.4  897.7209 \n140 17.3 432817 MolFeatur 1504 10.7  923.74 \n3.2. MetFrag \nIn Table A1, through the utilization of MetFrag, a comprehensive compilation of \ncompounds was extracted from each designated peak within the compound spec-\ntrum graph generated via LC-TOF MS analysis of the LifeGreenTM samples. These \ncompounds were meticulously identified, characterized, and annotated, facilitating \nthe elucidation of their chemical nature and potential functional attributes [18]. \nEach identified compound was assigned a canoni cal SMILES (Simplified M o-\nlecular Input Line Entry System) representation, serving as a concise yet co m-\nprehensive descriptor of its molecular structure. Additionally, molecular form u-\nlas were determined, encapsulating the precise arrangement of atoms constit ut-\ning each compound, thus providing crucial insights into their elemental comp o-\nsition and stoichiometry [18]. The benefits and potential applications associated \nwith each identified compound were elucidated, leveraging existing knowledge \nand literature resources. These benefits encompassed a diverse array of domains, \nincluding pharmaceuticals, agriculture, food science, cosmetics, and enviro n-\nmental remediation, among others [18]. \nBy integrating the structural information derived from canonical SMILES no-\ntation and molecular formulas with the contextual understanding of their fun c-\ntional properties, a holistic perspective on the chemical constituents present \nwithin the LifeGreen\nTM samples was attained, Table 4. \n3.3. LifeGreen™ Compounds  \n3.3.1. Swiss-ADME \nThe compiled list of 117 compounds extracted from the LC -TOF MS analysis of \nLifeGreenTM samples underwent comprehensive evaluation via SwissADME, a \nrobust computational platform designed to assess various pharmacokinetic and \npharmacodynamic parameters crucial for drug discovery and development [19] . \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 76 Computational Molecular Bioscience \n \nTable 4. Shows list of compounds in LifeGreenTM. \nCOMPOUND NAME  FORMULA  SMILES  \nPhenethyl anthranilate C15H15NO2 C1=CC=C(C=C1)CCOC(=O)C2=CC=CC=C2N \nD-glutamine C5H10N2O3 C(CC(=O)N)C(C(=O)O)N \nCitrus red 2 C18H16N2O3 COC1=CC(=C(C=C1)OC)N=NC2=C(C=CC3=CC=CC=C32\n)O \n1,5-Anhydro-D-fructose C6H10O5 C1C(=O)C(C(C(O1)CO)O)O \nBis-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 \nBergaptol C11H6O4 C1=CC(=O)OC2=CC3=C(C=CO3)C(=C21)O \nBergapten C12H8O4 COC1=C2C=CC(=O)OC2=CC3=C1C=CO3 \nIsobergaptol C11H6O4 C1=CC(=O)OC2=C1C(=CC3=C2C=CO3)O \nAbscisic acid C15H20O4 CC1=CC(=O)CC(C1(C=CC(=CC(=O)O)C)O)(C)C \n(-)-Abscisic acid  CC1=CC(=O)CC(C1(C=CC(=CC(=O)O)C)O)(C)C \n(+)-8’-Hydroxyabscisic acid C15H20O5 CC1=CC(=O)CC(C1(C=CC(=CC(=O)O)C)O)(C)CO \n(+)-abscisic acid beta-D-glucopyranosyl ester C21H30O9 CC1=CC(=O)CC(C1(C=CC(=CC(=O)OC2C(C(C(C(O2)CO\n)O)O)O)C)O)(C)C \nD-Fructofuranose 1,2’’:2,3’’-dianhydride C12H20O10 C1C2(C(C(C(O2)CO)O)O)OC3C(C(OC3(O1)CO)CO)O \n10-Hydroxycamptothecin C20H16N2O5 CCC1(C2=C(COC1=O)C(=O)N3CC4=C(C3=C2)N=C5C=C\nC(=CC5=C4)O)O \n2-(Hydroxymethyl)pentanedioic acid C6H10O5 C(CC(=O)O)C(CO)C(=O)O \nAdenosin  C1=NC(=C2C(=N1)N(C=N2)C3C(C(C(O3)CO)O)O)N \n4-hydroxycoumarin  C1=CC=C2C(=C1)C(=CC(=O)O2)O \nAlpha-beta-Dihydroresveratrol C14H14O3 C1=CC(=CC=C1CCC2=CC(=CC(=C2)O)O)O \nCasticin C19H18O8 COC1=C(C=C(C=C1)C2=C(C(=O)C3=C(C(=C(C=C3O2)O\nC)OC)O)OC)O \n4-nitrophenylalanine C9H10N2O4 C1=CC(=CC=C1CC(C(=O)O)N)[N+](=O)[O-] \ncitrate C6H8O7 C(C(=O)O)C(CC(=O)O)(C(=O)O)O \n3-Hydroxy-3-Carboxy-Adipic Acid C7H10O7 C(CC(CC(=O)O)(C(=O)O)O)C(=O)O \n(1R,2S)-1-hydroxybutane-1,2,4-tricarboxylic acid C7H10O7 C(CC(=O)O)C(C(C(=O)O)O)C(=O)O \n(2R)-dihomocitric acid C8H12O7 C(CC(=O)O)CC(CC(=O)O)(C(=O)O)O \n(-)-Threo-isodihomocitric acid C8H12O7 C(CC(C(C(=O)O)O)C(=O)O)CC(=O)O \n(2R)-trihomocitric acid C9H14O7 C(CCC(CC(=O)O)(C(=O)O)O)CC(=O)O \n1-Hydroxyhexane-1,2,6-tricarboxylate C9H14O7 C(CCC(=O)O)CC(C(C(=O)O)O)C(=O)O \nRiccionidin A C15H9O6+ C1=C2C=C3C(=C4C(=CC(=CC4=[OH+])O)O3)OC2=CC(=\nC1O)O \nScopoletin C10H8O4 COC1=C(C=C2C(=C1)C=CC(=O)O2)O \n2-Deoxy-D-ribose 5-phosphate C5H11O7P C1C(C(OC1O)COP(=O)(O)O)O \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 77 Computational Molecular Bioscience \n \nContinued  \ncyanidin C15H11O6+ C1=CC(=C(C=C1C2=[O+]C3=CC(=CC(=C3C=C2O)O)O)\nO)O \nDinoflagellate luciferin C33H40N4O6 CCC1=C(NC(=C1C)CC2C(=C(C(=O)N2)C)C=C)CC3=C(C\n4=C(N3)C(=C5C(C(C(N5)C(=O)O)C)CCC(=O)O)CC4=O)\nC \n(3S,4S,5E)-4-(2-carboxyethyl)-5-[2-({5-[(3-ethenyl\n-4-methyl-5-oxo-2,5-dihydro-1H-pyrrol-2-yl)met\nhyl]-3-ethyl-4-methyl-1H-pyrrol-2-yl}methyl)-3-\nme-\nthyl-4,5-dioxo-4,5-dihydrocyclopenta[b]pyrrol-6(\n1H)-ylidene]-3-methyl-L-proline \nC33H38N4O7 CCC1=C(NC(=C1C)CC2C(=C(C(=O)N2)C)C=C)CC3=C(C\n4=C(N3)C(=C5C(C(C(N5)C(=O)O)C)CCC(=O)O)C(=O)C4\n=O)C \nN-Glycosyl-L-asparagine C10H18N2O8 C(C1C(C(C(C(O1)NC(=O)CC(C(=O)O)N)O)O)O)O \nHesperidin C28H34O15 CC1C(C(C(C(O1)OCC2C(C(C(C(O2)OC3=CC(=C4C(=O)C\nC(OC4=C3)C5=CC(=C(C=C5)OC)O)O)O)O)O)O)O)O \nMethyl hesperidin C29H36O15 CC1C(C(C(C(O1)OCC2C(C(C(C(O2)OC3=CC(=C4C(=O)C\nC(OC4=C3)C5=CC(=C(C=C5)OC)OC)O)O)O)O)O)O)O \nArbutin C12H16O7 C1=CC(=CC=C1O)OC2C(C(C(C(O2)CO)O)O)O \nalpha-Arbutin C12H16O7 C1=CC(=CC=C1O)OC2C(C(C(C(O2)CO)O)O)O \nMethylarbutin C13H18O7 COC1=CC=C(C=C1)OC2C(C(C(C(O2)CO)O)O)O \nQuercitrin C21H20O11 CC1C(C(C(C(O1)OC2=C(OC3=CC(=CC(=C3C2=O)O)O)C\n4=CC(=C(C=C4)O)O)O)O)O \n3’’,5’’-Dihydroxyflavanone C15H12O4 C1C(OC2=CC=CC=C2C1=O)C3=CC(=CC(=C3)O)O \n(2S)-2’,7-Dimethoxy-3’,5-dihydroxyflavanone C17H16O6 COC1=CC(=C2C(=O)CC(OC2=C1)C3=C(C(=CC=C3)O)O\nC)O \nOrientin C21H20O11 C1=CC(=C(C=C1C2=CC(=O)C3=C(O2)C(=C(C=C3O)O)C\n4C(C(C(C(O4)CO)O)O)O)O)O \nEvocarpine C23H33NO CCCCC=CCCCCCCCC1=CC(=O)C2=CC=CC=C2N1C \n(Z)-1-Methyl-2-(tridec-8-en-1-yl)quinolin-4(1H)-\none \nC23H33NO CCCCC=CCCCCCCCC1=CC(=O)C2=CC=CC=C2N1C \nAspalathin C21H24O11 C1=CC(=C(C=C1CCC(=O)C2=C(C=C(C(=C2O)C3C(C(C(\nC(O3)CO)O)O)O)O)O)O)O \n1-Stearoylglycerophosphocholine C26H55NO7P+ CCCCCCCCCCCCCCCCCC(=O)OCC(COP(=O)(O)OCC[\nN+](C)(C)C)O \n1-Octadecanoyl-sn-glycero-3-phosphocholine C26H54NO7P CCCCCCCCCCCCCCCCCC(=O)OCC(COP(=O)([O-])OC\nC[N+](C)(C)C)O \nSinigrin C10H17NO9S2 C=CCC(=NOS(=O)(=O)O)SC1C(C(C(C(O1)CO)O)O)O \nSpirilloxanthin C42H60O2 CC(=CC=CC(=CC=CC(=CC=CC=C(C)C=CC=C(C)C=CC=\nC(C)C=CCC(C)(C)OC)C)C)C=CCC(C)(C)OC \nAridanin C38H61NO8 CC(=O)NC1C(C(C(OC1OC2CCC3(C(C2(C)C)CCC4(C3CC\n=C5C4(CCC6(C5CC(CC6)(C)C)C(=O)O)C)C)C)CO)O)O \n1-Hexadecanoyl-2-(9Z-octadecenoyl)-sn-glycero-\n3-phosphoethanolamine \nC39H76NO8P CCCCCCCCCCCCCCCC(=O)OCC(COP(=O)(O)OCCN)O\nC(=O)CCCCCCCC=CCCCCCCCC \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 78 Computational Molecular Bioscience \n \nContinued  \nHexadecasphinganine C16H35NO2 CCCCCCCCCCCCCC(C(CO)N)O \nPhytosphingosine C18H39NO3 CCCCCCCCCCCCCCC(C(C(CO)N)O)O \nPiperonal C8H6O3 C1OC2=C(O1)C=C(C=C2)C=O \nLupanine C15H24N2O C1CCN2CC3CC(C2C1)CN4C3CCCC4=O \nFlavanone C15H12O2 C1C(OC2=CC=CC=C2C1=O)C3=CC=CC=C3 \n2-hydroxy flavone C22H26ClNO4 CC1=CC=C(C=C1)C2=CC(=O)C3=C(O2)C=C(C=C3)OCC(\nCNC(C)C)O.Cl \nDihydrostillbene base C14H12O2 C1=CC(=CC=C1C=CC2=CC=C(C=C2)O)O \n5-Sulfosalicylate C7H6O6S C1=CC(=C(C=C1S(=O)(=O)O)C(=O)O)O \nGlabranin C20H20O4 CC(=CCC1=C(C=C(C2=C1OC(CC2=O)C3=CC=CC=C3)O)\nO)C \n2-Deoxy-scyllo-inosose C6H10O5 C1C(C(C(C(C1=O)O)O)O)O \nAcetanilide C8H9NO CC(=O)NC1=CC=CC=C1 \nNiridazole C6H6N4O3S C1CN(C(=O)N1)C2=NC=C(S2)[N+](=O)[O-] \nCitrinin C13H14O5 [H][C@]1(C)OC=C2C(O)=C(C(O)=O)C(=O)C(C)=C2[C@]\n1([H])C \nAspartame C14H18N2O5 COC(=O)C(CC1=CC=CC=C1)NC(=O)C(CC(=O)O)N \nepsilon-Caprolactam C6H11NO C1CCC(=O)NCC1 \ncis-3-(3-Carboxyethenyl)-3,5-cyclohexadiene-1,2-\ndiol \nC9H10O4 C1=CC(C(C(=C1)C=CC(=O)O)O)O \nIsobavachalcone C20H20O4 CC(=CCC1=C(C=CC(=C1O)C(=O)C=CC2=CC=C(C=C2)O\n)O)C \nGlabridin C20H20O4 CC1(C=CC2=C(O1)C=CC3=C2OCC(C3)C4=C(C=C(C=C4)\nO)O)C \nMycocyclosin C18H16N2O4 C1C2C(=O)NC(CC3=CC(=C(C=C3)O)C4=C(C=CC1=C4)O\n)C(=O)N2 \n2,3-Dehydro-UWM6 C19H16O5 CC1=CC(=O)C2C3=C(C(=O)CC2(C1)O)C(=C4C(=C3)C=C\nC=C4O)O \nPrazepam C19H17ClN2O C1CC1CN2C(=O)CN=C(C3=C2C=CC(=C3)Cl)C4=CC=CC\n=C4 \n(-)-Phaseollinisoflavan C20H20O4 CC1(C=CC2=C(O1)C=CC(=C2O)C3CC4=C(C=C(C=C4)O)\nOC3)C \nPhaseollidin C20H20O4 CC(=CCC1=C(C=CC2=C1OC3C2COC4=C3C=CC(=C4)O)\nO)C \nBis-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 \n2-Dehydro-3-deoxy-D-fuconate C6H10O5 CC(C(CC(=O)C(=O)O)O)O \n2-Dehydro-3-deoxy-L-rhamnonate C6H10O5 CC(C(CC(=O)C(=O)O)O)O \n2-Dehydro-3-deoxy-L-fuconate C6H10O5 CC(C(CC(=O)C(=O)O)O)O \nDiethyl pyrocarbonate C6H10O5 CCOC(=O)OC(=O)OCC \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 79 Computational Molecular Bioscience \n \nContinued  \n3,6-Anhydro-alpha-L-galactopyranose C6H10O5 C1C2C(C(O1)C(C(O2)O)O)O \nEugenol quinone methide C10H10O2 COC1=CC(=CC=C)C=CC1=O \nMethyl cinnamate C10H10O2 COC(=O)C=CC1=CC=CC=C1 \np-Methoxycinnamaldehyde C10H10O2 COC1=CC=C(C=C1)C=CC=O \n2-Hydroxymethylserine C4H9NO4 C(C(CO)(C(=O)O)N)O \n2-Phenylacetamide C8H9NO C1=CC=C(C=C1)CC(=O)N \n4-Hydroxy-L-threonine C4H9NO4 C(C(C(C(=O)O)N)O)O \nN-Benzylformamide C8H9NO C1=CC=C(C=C1)CNC=O \nAdenine C5H5N5 C1=NC2=C(N1)C(=NC=N2)N \n(E)-Phenylacetaldoxime C8H9NO C1=CC=C(C=C1)CC=NO \n(Z)-Phenylacetaldehyde oxime C8H9NO C1=CC=C(C=C1)CC=NO \nDibenzo[1,4]dioxin-2,3-dione C12H6O4 C1=CC=C2C(=C1)OC3=CC(=O)C(=O)C=C3O2 \n5-Deoxyribose-1-phosphate C5H11O7P CC1C(C(C(O1)OP(=O)(O)O)O)O \n2-Deoxy-D-ribose 1-phosphate C5H11O7P C1C(C(OC1OP(=O)(O)O)CO)O \n1-Deoxy-D-xylulose 5-phosphate C5H11O7P CC(=O)C(C(COP(=O)(O)O)O)O \n3,5-Dinitroguaiacol C7H6N2O6 COC1=C(C=C(C=C1O)[N+](=O)[O-])[N+](=O)[O-] \n2-(5’’-Methylthio)pentylmalic acid C10H18O5S CSCCCCCC(CC(=O)O)(C(=O)O)O \n3-(5’’-Methylthio)pentylmalic acid C10H18O5S CSCCCCCC(C(C(=O)O)O)C(=O)O \n3-(m-Aminophenyl)-2-(p-methoxyphenyl)acrylon\nitrile \nC16H14N2O COC1=CC=C(C=C1)C(=CC2=CC(=CC=C2)N)C#N \nGlycophymoline C16H14N2O COC1=NC(=NC2=CC=CC=C21)CC3=CC=CC=C3 \nFlindersiachromone C17H14O2 C1=CC=C(C=C1)CCC2=CC(=O)C3=CC=CC=C3O2 \nArborine C16H14N2O CN1C2=CC=CC=C2C(=O)N=C1CC3=CC=CC=C3 \n4,4’’-Methylenediphenyl diisocyanate C15H10N2O2 C1=CC(=CC=C1CC2=CC=C(C=C2)N=C=O)N=C=O \nMethaqualone C16H14N2O CC1=CC=CC=C1N2C(=NC3=CC=CC=C3C2=O)C \nTriamiphos C12H19N6OP CN(C)P(=O)(N1C(=NC(=N1)C2=CC=CC=C2)N)N(C)C \n2-[3-Ethyl-5-(4-methoxyphenyl)-1H-pyrazol-4-yl]\nphenol \nC18H18N2O2 CCC1=C(C(=NN1)C2=CC=C(C=C2)OC)C3=CC=CC=C3O \n(2-Butylbenzofuran-3-yl)(4-hydroxyphenyl)keton\ne \nC19H18O3 CCCCC1=C(C2=CC=CC=C2O1)C(=O)C3=CC=C(C=C3)O \nTutin C15H18O6 CC(=C)C1C2C(C3(C4(CO4)C5C(C3(C1C(=O)O2)O)O5)C)\nO \n3-Methoxy-4-hydroxyphenylglycolaldehyde C9H10O4 COC1=C(C=CC(=C1)C(C=O)O)O \n(R)-3-(4-Hydroxyphenyl)lactate C9H10O4 C1=CC(=CC=C1CC(C(=O)O)O)O \n3,4-Dihydroxyphenylpropanoate C9H10O4 C1=CC(=C(C=C1CCC(=O)O)O)O \n2’’,6’’-Dihydroxy-4’’-methoxyacetophenone C9H10O4 CC(=O)C1=C(C=C(C=C1O)OC)O \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 80 Computational Molecular Bioscience \n \nContinued  \n3-(4-Hydroxyphenyl)lactate C9H10O4 C1=CC(=CC=C1CC(C(=O)O)O)O \nHomovanillate C9H10O4 COC1=C(C=CC(=C1)CC(=O)O)O \n3-(2,3-Dihydroxyphenyl)propanoate C9H10O4 C1=CC(=C(C(=C1)O)O)CCC(=O)O \nMannitol C6H14O6 C(C(C(C(C(CO)O)O)O)O)O \nD-Sorbitol C6H14O6 C(C(C(C(C(CO)O)O)O)O)O \n \nThis systematic scrutiny aimed to discern the compounds exhibiting favorable \npharmacological properties conducive to combatting breast cancer and uterine \nfibroids, thus offering promising avenues for therapeutic intervention. Within \nthis rigorous evaluation framework, several key metrics were scrutinized, i n-\ncluding compliance with Lipinski’s Rule of Five, a pivotal criterion for predicting \noral bioavailability and permeability of potential drug candidates. Additionally, \nlead-likeness, hydrogen bond donor and acceptor counts, and bioavailability \nscores were meticulously examined, offering valuable insights into the co m-\npounds’ drug-like properties and therapeutic potential [19] . By leveraging the \ninsights gleaned from SwissADME analysis, compounds exhibiting optimal \npharmacokinetic profiles and bioavailability were identified as prime candidates \nfor further investig ation and therapeutic development. These compounds, ch a-\nracterized by their propensity to permeate biological barriers, maintain favorable \ndrug-like properties, and exhibit high bioavailability, hold promise in the ta r-\ngeted management and mitigation of breast cancer and uterine fibroids [19]. The \nsystematic integration of LC -TOF MS analysis, compound identification, and \nSwissADME evaluation represents a pivotal step towards the rational design and \ndiscovery of novel therapeutics aimed at addressing the unmet clinical needs a s-\nsociated with breast cancer and uterine fibroids. Through this concerted effort, \nthe identification of lead compounds with enhanced efficacy and safety profiles \nheralds a significant stride towards personalized and precision medicine a p-\nproaches tailored to combat these debilitating diseases [19]. \n3.3.2. RPBS \nFollowing the initial filtration process employing SwissADME, which identified \n51 compounds with potential efficacy against breast cancer and uterine fibroids, \na subsequent analysis utilizing RPBS was conducted to evaluate rotatable bonds \nand energy profiles. This additional scrutiny yielded a refined subset of 34 co m-\npounds, each characterized by optimal structural flexibility and energetics co n-\nducive to molecular docking studies [20]. The RPBS assessment provided crucial \ninsights into the molecular dynamics of the selected compounds, elucidating \ntheir ability to adopt diverse conformations and facilitating efficient interactions \nwith target biomolecules implicated in disease pathogenesis [20] . By prioritizing \ncompounds with favorable rotatable bond counts and energy profiles, this iter a-\ntive screening process enhances the rati onal selection of lead candidates poised \nfor further preclinical evaluation, \nTable 5 [20]. \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 81 Computational Molecular Bioscience \n \nTable 5. Shows down streaming results from RPBS tools. \nCOMPOUND NAME  \nRB E (1QQG)  E (2AYR)  E(7B E (6T41)  E (3GRF)  E (7B5O)  \n<5 <− 5 \nBergaptol 0 −7.9 −8.4 −8.7 −8.9 −8.8 −8.6 \nBergapten 1 −7.6 −8 −8.9 −9.1 −8.9 −8.1 \nIsobergaptol 0 −8.2 −8.6 −8.7 −8.6 −8.6 −8.6 \nAbscisic acid 3 −7.9 −7.7 −8.9 −8.6 −7.9 −7.9 \n(-)-Abscisic acid 3 −8 −7.5 −8.9 −8.6 −8.5 −7.9 \n(+)-8’-Hydroxyabscisic acid 4 −7.4 −7.3 −8.5 −8 −8.1 −8 \n(+)-abscisic acid beta-D-glucopyranosyl ester 6 −9.6 −9.2 −9.7 −9.4 −8.9 −9.5 \n10-Hydroxycamptothecin 1 −11.1 −11.4 −12.4 −12.9 −10.4 −11.4 \n4-hydroxycoumarin 0 −7.5 −7.4 −8.1 −7.5 −7.9 −7.4 \nAlpha-beta-Dihydroresveratrol 3 −8.2 −7.8 −9.4 −9.2 −8.4 −9 \nCasticin 5 −9 −8.4 −8.8 −8.7 −8 −9.1 \nScopoletin 1 −7 −7.3 −8.2 −7.9 −7.8 −7.5 \nMethylarbutin 4 −7.8 −8 −8.4 −8.2 −7.7 −8.5 \n3’’,5’’-Dihydroxyflavanone 1 −9.5 −9.3 −10.3 −10.4 −9.4 −9.9 \n(2S)-2’,7-Dimethoxy-3’,5-dihydroxyflavanone 3 −8.9 −8.9 −9.8 −9.9 −8.9 −9.3 \nLupanine 0 −8.8 −9 −10.1 −10.1 −8.4 −9.8 \nFlavanone 1 −9.4 −9.3 −10.4 −10.3 −10 −10.2 \n5-Sulfosalicylate 2 −7.2 −7.5 −7.4 −7.3 −6.7 −7.3 \nGlabranin 3 −9.1 −9.6 −10.8 −10.6 −9.6 −10.5 \nNiridazole 2 −6.6 −7 −6.7 −6.9 −6.8 −6.5 \nCitrinin 1 −8.3 −8.9 −9.1 −9.4 −9.8 −8.9 \nAspartame 9 −7.5 −7.1 −7.8 −8.1 −7.6 −7.5 \ncis-3-(3-Carboxyethenyl)-3,5- \ncyclohexadiene-1,2-diol 2 −5.3 −5.3 −6.3 −5.8 −5.5 −5.6 \nGlabridin 1 −10.2 −11.1 −10.8 −12.3 −11 −10.7 \nMycocyclosin 0 −11.1 −11.8 −12.7 −12.8 −12.3 −12.1 \n2,3-Dehydro-UWM6 0 −10.3 −10.5 −11.6 −11.9 −10.7 −11.6 \nPrazepam 3 −9.4 −9.9 −10.5 −10.5 −9.3 −10.3 \n(-)-Phaseollinisoflavan 1 −10.1 −11 −10.3 −12.4 −12.1 −10.6 \nPhaseollidin 2 −9.6 −10.1 −10.8 −10.8 −10 −10.4 \nDiethyl pyrocarbonate 6 −4.9 −5 −4.6 −5 −5.1 −5.1 \nEugenol quinone methide 2 −6.2 −6.6 −7.3 −6.9 −7.1 −6.7 \nMethyl cinnamate 3 −6.6 −6.4 −7 −6.9 −7 −6.8 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 82 Computational Molecular Bioscience \n \nContinued  \np-Methoxycinnamaldehyde 3 −6.4 −6.1 −6.9 −6.7 −6.8 −6.7 \nDibenzo[1,4]dioxin-2,3-dione 0 −9.4 −9.2 −10.6 −9.7 −10.7 −9.4 \n3,5-Dinitroguaiacol 3 −6.2 −7 −7.1 −6.7 −6.8 −6.9 \n2-(5’’-Methylthio)pentylmalic acid 9 −5.7 −5.7 −6 −5.9 −6 −5.4 \n3-(5’’-Methylthio)pentylmalic acid 9 −4.9 −5.4 −6.6 −5.9 −6.3 −6.3 \n3-(m-Aminophenyl)-2-(p-methoxyphenyl)acr\nylonitrile 3 −8.2 −8.2 −7.6 −7.6 −8.2 −7.7 \nGlycophymoline 3 −8.8 −9.5 −10.5 −10.5 −10.1 −10.4 \nFlindersiachromone 3 −9.2 −9.4 −11 −10.5 −10.1 −10.5 \nArborine 2 −9.2 −9.3 −10.8 −10 −9.7 −10.2 \nMethaqualone 1 −8.8 −9.6 −10.5 −10.3 −10.2 −9.9 \nTriamiphos 4 −7.5 −7.9 −8.1 −7.8 −7.6 −7.8 \n2-[3-Ethyl-5-(4-methoxyphenyl)-1H- \npyrazol-4-yl]phenol 4 −8.4 −9 −8.5 −9.1 −9 −8.5 \n(2-Butylbenzofuran-3-yl)(4- \nhydroxyphenyl)ketone 5 −8 −7.1 −8.1 −8 −8.3 −8.3 \nTutin 1 −8.6 −8.9 −9.1 −9.5 −8.6 −9 \n3-Methoxy-4-hydroxyphenylglycolaldehyde 3 −6.1 −6.7 −6.7 −6.5 −6.7 −6.5 \n(R)-3-(4-Hydroxyphenyl)lactate 3 −6.8 −6.9 −7.3 −7.4 −6.9 −7 \n2’’,6’’-Dihydroxy-4’’-methoxyacetophenone 2 −6.3 −6.5 −6.3 −6.8 −6.9 −6.8 \n3-(4-Hydroxyphenyl)lactate 3 −6.6 −7 −7.3 −7.4 −7.2 −7 \nHomovanillate 3 −6.2 −7.1 −7.1 −6.9 −7.1 −6.8 \n \nOut of the initial pool of 34 compounds, only 22 demonstrated the remarkable \ncapability to bind with all six protein diseases associated with both breast cancer \nand uterine fibroid. This subset of compounds exhibits broad- spectrum activity, \nindicating their potential to target multiple pathological pathways implicated in \nthe progression of these diseases. Their ability to interact with diverse protein \ntargets underscores their versatility and promise as candidate therapeutics [20]. In \ncontrast, the remaining 12 compounds displayed a more selective binding profile, \ninteracting with a subset of two to four protein diseases. While these compounds \nmay exhibit efficacy against specific disease subtypes or pathways, their narrower \nspectrum of activity suggests a more targeted mode of action. Despite this sele c-\ntivity, these compounds still hold considerable therapeutic potential and merit \nfurther investigation for their specific applications in breast cancer and uterine \nfibroid management [20]. \n3.3.3. Dogsitescorer \nDogsitescorer, a specialized computational tool, is employed to discern the \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 83 Computational Molecular Bioscience \n \nhighest binding energy exhibited by potential compounds against protein targets \nimplicated in breast cancer and uterine fibroid pathogenesis. Subsequently, \ncoordinates corresponding to the identified binding sites are extracted from the \noutput generated by Dogsitescorer [21] . These coordinates play a pivotal role in \nelucidating the precise molecular interactions between the candidate compounds \nand their respective protein targets. By pinpointing the specific binding sites on \nthe protein surfaces, these coordinates provide valuable insights into the mol e-\ncular mechanisms underpinning the observed binding affinities [21]. \nFurthermore, the coordinates obtained from Dogsitescorer serve as crucial input \nparameters for subsequent molecular docking simulations [21] . Leveraging ad-\nvanced computational algorithms, molecular docking studies enable the prediction \nof the binding modes and affinities of the candidate compounds within the protein \nbinding sites, offering valuable predictive insights into their therapeutic potential. \nThrough the iterative integration of computational tools such as Dogsitescorer and \nmolecular docking simulations, the identification of lead compounds with optimal \nbinding energies and favorable interactio n profiles against protein targets asso-\nciated with breast cancer and uterine fibroids is facilitated. This systematic a p-\nproach enhances the rational design and optimization of novel therapeutics aimed \nat mitigating the progression of these debilitating diseases [21]. \n3.3.4. Molecular Docking - Achilles Blind Docking Server \nThe 34 identified compounds, targeting breast cancer and uterine fibroid-associated \nprotein diseases, underwent comprehensive molecular docking simulations using \nthe ACHILLES BLIND DOCKING SERVER [22]. This state-of-the-art computa-\ntional tool fac ilitated the exploration of compound -protein interactions across \nmultiple protein targets, providing valuable insights into their binding affinities \nand binding site preferences. Through the blind docking approach employed by \nACHILLES, the compatibility between each compound and the diverse array of \nprotein targets associated with breast cancer and uterine fibroids was systemat i-\ncally evaluated. By considering multiple protein structures representative of di f-\nferent disease states, this approach enabled a com prehensive assessment of com-\npound efficacy across various pathological contexts [22]. \nFurthermore, the molecular docking simulations facilitated the identification \nof potential binding sites within the protein coordinates for each compound. \nThe number and distribution of these binding sites served as critical indicators \nof compound versatility and potential therapeutic efficacy. Compounds exhibi t-\ning a higher propensity to bind at multiple locations within the protein coord i-\nnates were deemed particularly promising, as they may exert broader therapeutic \neffects and target diverse disease mechanisms [22] . By integrating the insights \ngleaned from molecular docking simulations across multiple protein targets, a \ncomprehensive understanding of compound -protein interactions and their p o-\ntential implications for breast cancer and uterine fibroid management was at-\ntained, \nTable 6. \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 84 Computational Molecular Bioscience \n \nTable 6. Shows the number of locations within protein coordinate set by dogsitescorer’s energy binding with a total residue > − 6. \nCOMPOUND NAME  ID \nNUMBER OF LOCATION WITHIN COORDINATE  \nTOTAL RESIDUE (>−6)  \n1QQG 2AYR 3GRF 6T41 7B5Q 7B5O \nIsobergaptol 10198122 1 1 1 6 6 6 \n10-Hydroxycamptothecin 97226 3 2 2 4 3 6 \nCasticin 5315263 1 2 1 4 5 6 \n3’’,5’’-Dihydroxyflavanone 11954216 3 1 1 9 3 6 \n(2S)-2’,7-Dimethoxy-3’,5-dihydroxyflavanone 102066377 2 2 2 6 4 7 \nLupanine 91471 1 2 1 4 3 3 \nFlavanone 10251 2 2 1 4 4 6 \nGlabranin 124049 2 2 2 5 2 6 \nCitrinin 54680783 1 1 1 4 4 5 \nGlabridin 124052 3 2 2 3 4 6 \nMycocyclosin 59053147 3 1 1 3 5 6 \n2,3-Dehydro-UWM6 25195328 3 1 5 5 6 5 \nPrazepam 4890 4 1 2 3 4 6 \n(-)-Phaseollinisoflavan 162412 3 2 2 3 4 6 \nPhaseollidin 119268 3 2 4 5 6 4 \nDibenzo[1,4]dioxin-2,3-dione 17036 3 2 1 5 6 5 \nGlycophymoline 5480 3 2 1 6 4 3 \nFlindersiachromone 441964 4 2 4 4 3 5 \nArborine 63123 3 2 3 5 5 7 \nMethaqualone 6292 2 2 2 6 6 6 \n2-[3-Ethyl-5-(4-methoxyphenyl)-1H-pyrazol-4-yl]phenol 257428 2 1 2 8 5 5 \nTutin 75729 1 1 1 8 4 4 \n(-)-Abscisic acid 643732 2 NA 0 9 7 NA \nAlpha-beta-Dihydroresveratrol 185914 3 NA 2 11 5 6 \n3-(m-Aminophenyl)-2-(p-methoxyphenyl)acrylonitrile 79559 2 2 2 NA NA NA \n(2-Butylbenzofuran-3-yl)(4-hydroxyphenyl)ketone 79569 3 NA 2 NA 3 6 \nBergaptol 5280371 NA 2 1 12 6 5 \nBergapten 2355 NA 2 1 8 5 6 \nMethylarbutin 80131 NA 2 NA 11 3 7 \nAbscisic acid 5280896 NA NA NA 11 5 NA \n(+)-8’-Hydroxyabscisic acid 11954194 NA NA 1 10 4 7 \n4-hydroxycoumarin 54682930 NA NA NA NA 5 NA \nScopoletin 5280460 NA NA NA NA 8 NA \nTriamiphos 13943 NA NA NA NA 6 NA \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 85 Computational Molecular Bioscience \n \nTo visualize the binding profiles of the compounds across all six protein di s-\neases, a graph can be constructed with the compounds on the x -axis and the \nnumber of binding locations within the protein coordinates on the y -axis. Each \ncompound is represented by a bar, with the height of the bar indicating the \nnumber of binding locations within the protein coordinates for that compound. \nThe graph provides an overview of the binding versatility of each compound \nacross multiple protein targets associated with breast cancer and uterine fibroid , \nGraph 1 . \n \n \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 86 Computational Molecular Bioscience \n \n \nGraph 1 . Show the graph for 22 compounds with the number of locations it’s able to bind. \n \nThe comprehensive analysis of compound-protein interactions reveals distinct \nbinding profiles across multiple protein targets implicated in breast cancer and \nuterine fibroid pathogenesis. Specifically, against the 1QQG protein, 26 com-\npounds exhibit diverse binding patterns, with each compound displaying a va-\n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 87 Computational Molecular Bioscience \n \nrying number of binding locations within the protein coordinates. Similarly, in-\nteractions with the 2AYR protein reveal comparable trends among 26 com-\npounds, while interactions with the 3GRF, 6T41, 7B5Q, and 7B5O proteins \ndemonstrate unique binding profiles for 29, 29, 33, and 28 compounds, respec-\ntively. These findings underscore the compound-specific nature of binding inte-\nractions and provide valuable insights for further exploration of therapeutic in-\nterventions targeting breast cancer and uterine fibroids in Graph s 2(a)-(f). Fig-\nures 12-17 show the one of the compounds of 10251 for each protein’s locations \nwithin set coordinates. \n \n \n(a) \n \n(b) \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 88 Computational Molecular Bioscience \n \n \n \n \n(c) \n \n \n \n(d) \n \n \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 89 Computational Molecular Bioscience \n \n \n \n(e) \n \n \n(f) \nGraph 2 . (a) Shows protein 1QQG ’s number of location binding for each compound ; (b) Shows protein 2AYR’s \nnumber of location binding for each compound ; (c) Shows protein 3GRF ’s number of location binding for each \ncompound; (d) Shows protein 6T41 ’s number of location binding for each compound ; (e) Shows protein 7B5Q ’s \nnumber of location binding for each compound ; (f) Shows protein 7B5O ’s number of location binding for each \ncompound. \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 90 Computational Molecular Bioscience \n \n \n \n \n \n \nFigure 1 2. Shows compound 10251 location within set coordinate in 1QQG protein. \n \n \n \n \nFigure 1 3. Shows compound 10251 location within set coordinate in 2AYR protein. \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 91 Computational Molecular Bioscience \n \n \nFigure 1 4. Shows compound 10251 location within set coordinate in 3GRF protein. \n \n \nFigure 1 5. Shows compound 10251 location within set coordinate in 6T41 protein. \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 92 Computational Molecular Bioscience \n \n \nFigure 1 6. Shows compound 10251 location within set coordinate in 7B5Q protein. \n \n \nFigure 1 7. Shows compound 10251 location within set coordinate in 7B5O protein. \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 93 Computational Molecular Bioscience \n \nThe figure displays the distances between a specific compound and the amino \nacids of proteins associated with breast cancer and uterine fibroid pathogenesis. \nSpecifically, it illustrates the distances to amino acids of proteins 1QQG, 7B5Q, \nand 7B5O, representing breast cancer-related proteins, as well as proteins 2AYR, \n3GRF, and 6T41, which are associated with uterine fibroid disease. These di s-\ntances serve as indicators of the successful rate of binding energy for the co m-\npound towards each protein disease. Variations in distance highlight the diffe r-\ning degrees of interaction between the compound and the amino acids within \neach protein structure. Shorter distances suggest stronger binding interactions, \nindicating a higher potential  for therapeutic efficacy in reducing the propensity \nfor breast cancer and uterine fibroid development. Conversely, longer distances \nmay signify weaker binding interactions, suggesting a need for further investig a-\ntion or optimization of the compound’s efficacy. \nBy analyzing the distances to amino acids in the proteins relevant to both \nbreast cancer and uterine fibroids, this table and 4 figures for each proteins pr o-\nvides valuable insights into the compound ’s potential effectiveness in targeting \nthese di seases. This information aids in the identification and prioritization of \ncompounds for further preclinical and clinical studies aimed at mitigating the \nprogression of breast cancer and uterine fibroids, \nFigure s 18-23. \n \n \n \n \n(a) \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 94 Computational Molecular Bioscience \n \n \n(b) \n \n(c) \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 95 Computational Molecular Bioscience \n \n \n(d) \nFigure 1 8. (a) Shows protein 1QQG interactions and distance from compound 441964 \namino acids with the nearest distances of 3.6 ; (b) Shows protein 1QQG interactions and \ndistance from compound 10198122 amino acids with the nearest distances of 2.9 ; (c) \nShows protein 1QQG interactions and distance from compound 10251 amino acids with \nthe nearest distances of 2.8 ; (d) Shows protein 1QQG interactions and distance from \ncompound 124049 amino acids with the nearest distances of 3.0. \n \n \n(a) \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 96 Computational Molecular Bioscience \n \n \n \n(b) \n \n \n(c) \n \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 97 Computational Molecular Bioscience \n \n \n(d) \nFigure 1 9. (a) Shows protein 2AYR interactions and distance from compound 2355 \namino acids with the nearest distances of 3.1 ; (b) Shows protein 2AYR interactions and \ndistance from compound 10251 amino acids with the nearest distances of 3.6 ; (c) Shows \nprotein 2AYR interactions and distance from compound 80131 amino acids with the \nnearest distances of 2.5 ; (d) Shows protein 2AYR interactions and distance from com-\npound 91471 amino acids with the nearest distances of 3.6 \n \n \n(a) \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 98 Computational Molecular Bioscience \n \n \n \n(b) \n \n \n(c) \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 99 Computational Molecular Bioscience \n \n \n(d) \nFigure 2 0. (a) Shows protein 3GRF interactions and distance from compound 25195328 \namino acids with the nearest distances of 3.3 ; (b) Shows protein 3GRF interactions and \ndistance from compound 97226 amino acids with the nearest distances of 3.0 ; (c) Shows \nprotein 3GRF interactions and distance from compound 124049 amino acids with the \nnearest distances of 3.2 ; (d) Shows protein 3GRF interactions and distance from com-\npound 124052 amino acids with the nearest distances of 2.8 \n \n \n(a) \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 100 Computational Molecular Bioscience \n \n \n(b) \n \n(c) \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 101 Computational Molecular Bioscience \n \n \n(d) \nFigure 2 1. (a) Shows protein 6T41 interactions and distance from compound 5280371 \namino acids with the nearest distances of 2.8 ; (b) Shows protein 6T41 interactions and \ndistance from compound 4890 amino acids with the nearest distances of 3.1 ; (c) Shows \nprotein 6T41 interactions and distance from compound 5480 amino acids with the nea r-\nest distances of 3.9 ; (d) Shows protein 6T41 interactions and distance from compound \n6292 amino acids with the nearest distances of 3.1. \n \n \n(a) \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 102 Computational Molecular Bioscience \n \n \n \n(b) \n \n \n(c) \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 103 Computational Molecular Bioscience \n \n \n(d) \nFigure 2 2. (a). Shows protein 7B5Q interactions and distance from compound 5280460 amino a c-\nids with the nearest distances of 2.0 ; (b) Shows protein 7B5Q interactions and distance from com-\npound 2355 amino acids with the nearest distances of 3.3 ; (c) Shows protein 7B5Q interactions and \ndistance from compound 4890 amino acids with the nearest distances of 2.8 ; (d) Shows protein \n7B5Q interactions and distance from compound 5480 amino acids with the nearest distances of 2.9. \n \n \n \n(a) \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 104 Computational Molecular Bioscience \n \n \n(b) \n \n(c) \n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 105 Computational Molecular Bioscience \n \n \n(d) \nFigure 2 3. (a) Shows protein 7B5O interactions and distance from compound 11954194 \namino acids with the nearest distances of 3.0 ; (b) Shows protein 7B5O interactions and \ndistance from compound 6292 amino acids with the nearest distances of 3.0 ; (c) Shows \nprotein 7B5O interactions and distance from compound 10251 amino acids with the \nnearest distances of 3.1 ; (d) Shows protein 7B5O interactions and distance from com-\npound 17036 amino acids with the nearest distances of 2.9. \n4. Discussion \nThe compounds that have been found in the LifeGreen TM product provide vari-\nous benefits towards human health, breast cancers and uterine fibroid. Isobe r-\ngaptol is a compound found in certain plants, particularly in essential oils. It has \nbeen studied for its potential an ti-inflammatory and antimicrobial properties, \nwhich could contribute to improved immune function and wound healing. Some \nresearch suggests that Isobergaptol may also have antioxidant properties, helping \nto neutralize harmful free radicals in the body and reduce oxidative stress, which \nis associated with various chronic diseases. 10 -Hydroxycamptothecin is a natu-\nrally occurring compound found in the Camptotheca acuminata tree. It is a d e-\nrivative of camptothecin, a well -known anticancer agent. Studies have sho wn \nthat 10 -Hydroxycamptothecin exhibits potent antitumor activity by inhibiting \nthe enzyme topoisomerase I, which is involved in DNA replication. This action \ncan prevent cancer cells from proliferating and induce apoptosis (programmed \ncell death) in cancer cells. Camptothecin and related analogs have shown pro m-\n\n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 106 Computational Molecular Bioscience \n \nise as anticancer agents, which could lead to the death of tumor cells by targeting \nthe nuclear enzyme, topoisomerase I, and inhibiting the relegation of the cleaved \nDNA strand. One of the camptothecin analogs, hydroxycamptothecin (HCPT), a \nplant alkaloid derived from Camptotheca acuminata, has demonstrated strong \nantitumor activity against gastric, lung, ovarian, breast, and pancreatic carcino-\nmas [23]. 10-hydroxycamptothecin (10-HCPT) is an important class of antit u-\nmor agent, it inhibits the DNA topoisomerase I of tumors and suppresses the \nproliferation of cancer cells to elicit antitumor effect [24] . Studies in animal and \nhuman subjects have shown that 10-hydroxycamptothecin (HCPT) is more p o-\ntent and less toxic than the parent compound CPT. Casticin is a p olymethylfla-\nvone isolated from a traditional Chinese therapeutic plant named Vitex trifolia \nL. from the Verbenaceae family [25] . The plant contributes to improve m any \nmorbidities including premenstrual syndrome, mastalgia, inflammation and \nsexual dysfunction, and also helps to relieve pain, and possesses antinociceptive \neffects. This plant is useful in mild hyperprolactinemia and luteal phase defects. \nIt is also hel pful in alleviating menstruation, bleeding management uterine f i-\nbroids, polycystic ovarian syndrome, prostate disorders, migrainous women \nwith premenstrual syndrome [26] . Casticin is a flavonoid compound found in \nseveral medicinal plants, including Vitex agnus -castus (chaste tree). It has been \ninvestigated for its various potential health benefits. Research suggests that casti-\ncin possesses anti-inflammatory and antioxidant properties, which may help r e-\nduce inflammation and oxidative stress in the body. This could potentially ben e-\nfit conditions such as arthritis and cardiovascular disease. Some studies also i n-\ndicate that casticin may have anticancer properties, inhibi ting the growth and \nproliferation of cancer cells in certain types of cancer. \nThis flavonoid compound is found in various fruits and vegetables and has \nbeen studied for its potential health- promoting effects. Like other flavonoids, \n3’,5’-dihydroxyflavanone exhibits antioxidant properties, which can help protect \ncells from oxidative damage and reduce the risk of chronic diseases such as heart \ndisease and cancer. Additionally, some research suggests that this compound \nmay have anti -inflammatory properties, wh ich could help alleviate inflamma-\ntion-related conditions such as arthritis and inflammatory bowel disease. This \nflavonoid compound is also found in various plants and has been investigated \nfor its potential health benefits. Like other flavonoids, (2S)- 2’,7-Dimethoxy- \n3’,5’-dihydroxy flavanone possesses antioxidant properties, which can help pr o-\ntect cells from oxidative damage and reduce the risk of chronic diseases. Some \nresearch suggests that this compound may also have anti -inflammatory effects, \nwhich cou ld potentially benefit conditions such as arthritis and cardiovascular \ndisease. Lupanine is a quinolizidine alkaloid found in various plant species, i n-\ncluding lupin seeds. It has been studied for its potential health benefits. Research \nsuggests that Lupanine may have hypotensive (blood pressure- lowering) effects, \nmaking it potentially beneficial for individuals with hypertension. Additionally, \nLupanine has been investigated for its potential antidiabetic properties, with \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 107 Computational Molecular Bioscience \n \nsome studies indicating that it may help improve insulin sensitivity and glucose \nmetabolism. Flavanones are a class of flavonoid compounds found in various \nfruits and vegetables, particularly citrus fruits. They have been studied for their \nnumerous health benefits [27]. \nFlavanones exhibit antioxidant properties, helping to neutralize harmful free \nradicals in the body and reduce oxidative stress, which is associated with various \nchronic diseases such a s cardiovascular disease and cancer. Some flavanones, \nsuch as hesperidin and naringenin, have been shown to have anti- inflammatory \neffects, which may help reduce inflammation and alleviate symptoms of infla m-\nmatory conditions like arthritis. Flavanones have  been found to have both ant i-\noxidant and anti- inflammatory properties. In particular, different studies have \nfocused their attention on hesperidin and its aglycone form, hesperetin, which \nplay an important role in the prevention of diseases associated with  oxidative \nstress and inflammation, such as cancer and cardiovascular disease [28]. Glabra-\nnin is a compound found in licorice (Glycyrrhiza glabra) and has been studied \nfor its potential health benefits. Research suggests that Glabranin may have an-\nti-inflammatory properties, which could help reduce inflammation in the body \nand alleviate symptoms of inflammatory conditions such as arthritis. Additio n-\nally, Glabranin has been investigated for its potential antiviral and antimicrobial \nproperties, which could contribute to its use in traditional medicine for treating \ninfections. Citrinin is a mycotoxin produced by certain fungi, particularly sp e-\ncies of Penicillium and Asper gillus. While it is primarily known for its toxic e f-\nfects, some research has also explored potential health benefits. Limited studies \nsuggest that Citrinin may have antioxidant properties. Glabridin is a flavonoid \ncompound found in licorice root (Glycyrrhi za glabra) and has been studied for \nits various health benefits. Research suggests that Glabridin may have antiox i-\ndant properties, helping to protect cells from oxidative damage and reduce the \nrisk of chronic diseases such as heart disease and cancer. Addi tionally, Glabridin \nhas been investigated for its potential anti- inflammatory effects, which could \nhelp reduce inflammation in the body and alleviate symptoms of inflammatory \nconditions like arthritis. Glabridin is an isoflavan extracted from licorice (gen us \nGlycyrrhiza) roots, which is also known as a phytoestrogen due to the similarity \nof its structure and lipophilicity to 17\nβ-estradiol. Studies indicated that glabridin is \nable to bind to the ERs and induce estrogenic responses in cardiovascular and \nbone tissues, suggesting its possibility to be used in estrogen replacement therapy. \nMycocyclosin is a compound isolated from certain fungi, particularly m a-\nrine-derived fungi. It has been studied for its potential pharmaceutical prope r-\nties. Research suggests that Mycocyclosin may have antibacterial and antifungal \nproperties, making it potentially useful in the development of new antibiotics or \nantifungal agents. Additionally, some studies indicate that Mycocyclosin may \nhave cytotoxic effects on cancer cells, w hich could make it a candidate for fu r-\nther investigation as a potential anticancer agent. 2,3 -Dehydro-UWM6 is a \nchemical compound with potential pharmacological applications. Prazepam is a \nbenzodiazepine medication used to treat anxiety and panic disorders . It belongs \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 108 Computational Molecular Bioscience \n \nto the class of psychoactive drugs known for their anxiolytic (anxiety -reducing), \nsedative, muscle relaxant, and anticonvulsant properties. Benefits of Prazepam \ninclude its ability to alleviate symptoms of anxiety and panic disorders, promote \nrelaxation, and reduce muscle tension. Prazepam is often prescribed for \nshort-term relief of anxiety symptoms and is considered effective when used as \ndirected under medical supervision. (- )-Phaseollinisoflavan is a type of isoflav o-\nnoid compound found in c ertain plants, particularly in legumes like soybeans \nand chickpeas. Research suggests that isoflavonoids like (- )-Phaseollinisoflavan \nmay have various health benefits, including potential anticancer, antioxidant, \nand anti- inflammatory properties. Some stud ies indicate that dietary intake of \nisoflavonoids may be associated with a reduced risk of certain cancers, such as \nbreast and prostate cancer, as well as cardiovascular disease. Phaseollidin is a \nnatural compound found in certain legumes, including beans and peas. Research \nsuggests that Phaseollidin may have anticancer properties, as it has been shown \nto inhibit the growth of cancer cells in some studies. Additionally, Phaseollidin \nmay possess anti -inflammatory and antioxidant properties, which could contr i-\nbute to its potential health benefits. \nGlycophymoline is a brand name for a topical solution containing various \nherbal extracts, including menthol, eucalyptol, and thymol. It is commonly used \nas a mouthwash and gargle for oral hygiene and minor throat irr itations. Bene-\nfits may include its antiseptic and refreshing properties, which can help to kill \nbacteria in the mouth, reduce bad breath, and soothe sore throats. Abscisic acid \n(ABA) is a plant hormone involved in various physiological processes in plants,  \nsuch as seed dormancy, bud dormancy, and response to environmental stress. \nWhile primarily studied for its role in plants, Abscisic acid (ABA) has also been \ninvestigated for its potential health benefits in humans. Research suggests that \nABA may have anti-inflammatory, antioxidant, and immunomodulatory effects, \nwhich could potentially benefit human health. It has been studied for its pote n-\ntial therapeutic applications in conditions such as diabetes, obesity, inflamm a-\ntion, and autoimmune diseases. Dihydroresveratrol is a derivative of resveratrol, \na polyphenolic compound found in various plants, including grapes, berries, and \npeanuts. Resveratrol and its derivatives have been extensively studied for their \npotential health benefits, including antioxidant, ant i-inflammatory, cardiopr o-\ntective, neuroprotective, and anticancer properties. While research on a l-\npha-beta-dihydroresveratrol specifically may be limited, it likely shares some of \nthe health -promoting properties of resveratrol due to its structural similarity. \nBergaptol is a natural compound found in certain plants, particularly in the c i-\ntrus family. It is structurally related to bergamottin and is primarily known for \nits photosensitizing effects. While Bergaptol itself may not have direct health \nbenefits, it is used in combination with other compounds in phototherapy for \nthe treatment of certain skin conditions, such as psoriasis and vitiligo. \nBergapten, also known as 5-methoxypsoralen, is a natural compound found in \nseveral plant species, including citrus fruits and certain herbs such as parsley and \ncelery. Bergapten is primarily known for its photosensitizing effects, which have \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 109 Computational Molecular Bioscience \n \nbeen utilized in the treatment of skin disorders such as psoriasis, vitiligo, and \neczema through a process known as psoralen plus ultraviolet A (PUVA) therapy. \nAdditionally, Bergapten has been studied for its potential anticancer properties, \nparticularly in the treatment of cutaneous T -cell lymphoma (CTCL). Methyla r-\nbutin is a derivative of arbutin, a natural compound found in variou s plant spe-\ncies such as bearberry, cranberry, and blueberry. Arbutin is well- known for its \nskin-lightening and antioxidant properties. Methylarbutin is often used in co s-\nmetic products for its potential to inhibit melanin production and reduce the \nappearance of hyperpigmentation, dark spots, and uneven skin tone. Additio n-\nally, arbutin and its derivatives like Methylarbutin have been studied for their \npotential antioxidant and anti- inflammatory effects, which could contribute to \ntheir skin -protective properti es. Abscisic acid (ABA) is a plant hormone i n-\nvolved in various physiological processes, including seed dormancy, bud do r-\nmancy, and response to environmental stress. While primarily studied for its \nrole in plants, Abscisic acid (ABA) has also been investiga ted for its potential \nhealth benefits in humans. Research suggests that Abscisic acid (ABA) may have \nanti-inflammatory, antioxidant, and immunomodulatory effects, which could \npotentially benefit human health. It has been studied for its potential therapeutic \napplications in conditions such as diabetes, obesity, inflammation, and autoi m-\nmune diseases. \n(+)-8’-Hydroxyabscisic acid is a derivative of abscisic acid (ABA), a plant \nhormone involved in various physiological processes in plants, including seed \ndormancy, bud dormancy, and response to environmental stress. While prima r-\nily studied for its role in plants, some research suggests that abscisic acid and its \nderivatives may have potential health benefits in humans. Abscisic acid has been \ninvestigated for its potential anti-inflammatory, antioxidant, and immunomodula-\ntory effects, which could potentially benefit human health. It has been studied for \nits potential therapeutic applications in conditions such as diabetes, obesity, i n-\nflammation, and autoimmune diseases. 4-hydroxycoumarin, also known as umbel-\nliferon, is a natural compound found in various plants, including citrus fruits, and is \nalso produced synthetically. Research suggests that 4-hydroxycoumarin may have \nantioxidant, anti- inflammatory, and antimicrob ial properties. It has been st u-\ndied for its potential use in the treatment of various conditions, including skin \ndisorders, inflammatory diseases, and as a natural sunscreen agent. Scopoletin is \na natural coumarin compound found in various plants, including members of \nthe Apiaceae and Rutaceae families. Scopoletin has been studied for its potential \npharmacological activities, including antioxidant, anti -inflammatory, antim i-\ncrobial, and antitumor properties. It has been investigated for its potential the-\nrapeutic applications in conditions such as diabetes, neurodegenerative diseases, \ncancer, and cardiovascular disorders. \n5. Conclusion \nIn summary, using molecular docking studies, we effectively discovered the \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 110 Computational Molecular Bioscience \n \nnumber of compounds present in the LifeGreenTM that have the potential effic a-\ncy against breast cancer and uterine fibroids. From the results, we identified that \nout of 117 compounds that have been extracted from the LC-TOF MS analysis of \nLifeGreenTM, only 34 compounds have the more binding profile against the pr o-\ntein diseases. A total of 22 compounds demonstrated the remarkable capability \nto bid with all the six protein diseases while the remaining 12 compounds di s-\nplayed a more selective binding profile, where those only interact with a subset \nof two t o four protein diseases. Finally, the identified 34 compounds, which are \nassociated with breast cancer and uterine fibroids, studied further by conducting \ncomprehensive molecular docking simulations to extract information on their \nbinding affinities and bi nding site preferences. This study underscores the need \nfor further analytical and experimental studies to establish the safety and efficacy \nof the identified compounds. In the future, this experiment will be conducted in \nanimal studies for both breast can cer and uterine fibroids using the standard \nprocedure dosage recommended by the WHO and also will proceed with the \nphytochemical studies in order to identify the similariton between the co m-\npounds that have been identified in this paper. \nAuthors’ Contribution \nConceptualization: Ummi Shahieda Lazaroo Binti Zurrein Shah Lazaroo, Chua \nKia How. Methodology & Formal analysis: Ummi Shahieda Lazaroo Binti \nZurrein Shah Lazaroo. Writing Original Draft : Ummi Shahieda Lazaroo Binti \nZurrein Shah Lazaroo,  Navanithan Sivanananthan. Discussion & Conclusion: \nNavanithan Sivanananthan. 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Molecules, 20, 19085-19100.  \nhttps://doi.org/10.3390/molecules201019085 \n[28] Khan, A., Ikram, M., Hahm, J .R. and Kim, M. O. (2020) Antioxidant and A n-\nti-Inflammatory Effects of Citrus Flavonoid Hesperetin: Special Focus on Neur o-\nlogical Disorders. Antioxidants, 9, Article 609.  \nhttps://doi.org/10.3390/antiox9070609 \n \n \n \n \n \n  \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 113 Computational Molecular Bioscience \n \nAppendix \nTable A 1. Shows compound spectrum result list which consist of all the details from LC-TOF-MS analysis. \nM/Z\n \nPEAK HIT\n \nGRAPH M/Z\n \nOVER INTENSITY \n \n(PEAK NO)\n \nRT (MIN)\n \nAREA\n \nSIGNAL TO \n \nNOISE RATION\n \n(S/N)\n \nPRECURSOR M/Z \n \n(CHROMATOGRA\nM)\n \nFRAGMENTATION \n \n(INTENSITY)\n \nCOLLISION\n \nENERGY (Ev)\n \nMOLECULAR\n \nFORMULA\n \nMono\n isotopic\n Mass\n \n218.9853 13 1.4 78569 66.7 AutoMSn (242.0010) \n182.9641 598  \n190.9905 1277  \n200.9748 1325  \n201.9746 330  \n218.9853 13669  \n219.9862 3437  \n220.9840 1614  \n242.0023 2244  \n243.0023 616  \n265.0179 314 \n22.1 C7H6O6S 217.989 \n325.1181 15 1.9 49668 73 AutoMSn (309.1324) \n117.0562 449  \n127.0402 1207  \n130.0530 166  \n145.0506 779  \n148.0623 194  \n163.0622 148  \n225.0879 188  \n226.0734 192  \n274.0966 220  \n292.1116 313 \n25.5 C20H20O4 324.136 \n163.0609 21 2 79661 88.6 AutoMSn (365.1092) \n185.0435 195 \n203.0524 417 \n365.1107 521  C6H10O5 162.053 \n136.064 39 2.6 29720 70.1 AutoMSn (268.1092) \n115.0393 134  \n119.0369 254  \n133.0525 263  \n136.0640 26078  \n137.0649 1163  \n268.1101 1319 \n C8H9NO 135.068 \n215.0195 45 3 44717 70.9 AutoMSn (130.0523) \n130.0525 5009  \n163.0611 904  \n175.0267 1013  \n193.0369 2962  \n215.0195 11372  \n230.9944 2343  \n259.0974 1813  \n291.0178 2990  \n322.0834 2695  \n407.0505 4487 \n C6H6N4O3S 214.016 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 114 Computational Molecular Bioscience \n \n251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) \n147.0476 786  \n175.0395 4262  \n176.0436 370  \n207.0649 7163  \n208.0668 876  \n236.0678 1165  \n251.0943 215671  \n252.0974 23390  \n253.0976 1821  \n310.1664 1449 \n C13H14O5 250.084\n1236 \n295.1332 69 7.6 297019 549 AutoMSn (589.2497) \n120.0817 4190  \n180.1027 40659  \n181.1057 4225  \n200.0714 3809  \n235.1091 36428  \n236.1120 4538  \n260.0937 11591  \n277.1198 6738  \n295.1321 29595  \n296.1337 4089 \n C14H18N2O5 294.122 \n114.0925 97 9.1 410677 766.7 AutoMSn (340.2600) \n114.0925 94001  \n115.0948 6098  \n209.1659 22605  \n226.1919 10772  \n227.1785 8580  \n228.1615 15199  \n322.2517 14478  \n340.2634 5283  \n435.3349 6041  \n453.3465 6718 \n C6H11NO 113.084 \n183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) \n118.0919 111  \n122.0829 442  \n242.2479 2983  \n243.2535 413  \n272.2650 272  \n288.2918 397  \n289.2902 120  \n290.2724 5694  \n291.2730 948 \n C9H10O4 182.058 \n325.1181 15 1.9 49668 73 AutoMSn (309.1324) \n117.0562 449  \n127.0402 1207  \n130.0530 166  \n145.0506 779  \n148.0623 194  \n163.0622 148  \n225.0879 188  \n226.0734 192  \n274.0966 220  \n292.1116 313 \n25.5 C20H20O4 324.136 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 115 Computational Molecular Bioscience \n \n325.1181 15 1.9 49668 73 AutoMSn (309.1324) \n117.0562 449  \n127.0402 1207  \n130.0530 166  \n145.0506 779  \n148.0623 194  \n163.0622 148  \n225.0879 188  \n226.0734 192  \n274.0966 220  \n292.1116 313 \n25.5 C20H20O4 324.136 \n325.1181 15 1.9 49668 73 AutoMSn (309.1324) \n117.0562 449  \n127.0402 1207  \n130.0530 166  \n145.0506 779  \n148.0623 194  \n163.0622 148  \n225.0879 188  \n226.0734 192  \n274.0966 220  \n292.1116 313 \n25.5 C18H16N2O4 324.111 \n325.1181 15 1.9 49668 73 AutoMSn (309.1324) \n117.0562 449  \n127.0402 1207  \n130.0530 166  \n145.0506 779  \n148.0623 194  \n163.0622 148  \n225.0879 188  \n226.0734 192  \n274.0966 220  \n292.1116 313 \n25.5 C19H16O5 324.1 \n325.1181 15 1.9 49668 73 AutoMSn (309.1324) \n117.0562 449  \n127.0402 1207  \n130.0530 166  \n145.0506 779  \n148.0623 194  \n163.0622 148  \n225.0879 188  \n226.0734 192  \n274.0966 220  \n292.1116 313 \n25.5 C19H17ClN2O 324.103 \n325.1181 15 1.9 49668 73 AutoMSn (309.1324) \n117.0562 449  \n127.0402 1207  \n130.0530 166  \n145.0506 779  \n148.0623 194  \n163.0622 148  \n225.0879 188  \n226.0734 192  \n274.0966 220  \n292.1116 313 \n25.5 C20H20O4 324.136 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 116 Computational Molecular Bioscience \n \n325.1181 15 1.9 49668 73 AutoMSn (309.1324) \n117.0562 449  \n127.0402 1207  \n130.0530 166  \n145.0506 779  \n148.0623 194  \n163.0622 148  \n225.0879 188  \n226.0734 192  \n274.0966 220  \n292.1116 313 \n25.5 C20H20O4 324.136 \n325.1181 15 1.9 49668 73 AutoMSn (309.1324) \n117.0562 449  \n127.0402 1207  \n130.0530 166  \n145.0506 779  \n148.0623 194  \n163.0622 148  \n225.0879 188  \n226.0734 192  \n274.0966 220  \n292.1116 313 \n25.5 C12H20O10 324.106 \n325.1181 15 1.9 49668 73 AutoMSn (309.1324) \n117.0562 449  \n127.0402 1207  \n130.0530 166  \n145.0506 779  \n148.0623 194  \n163.0622 148  \n225.0879 188  \n226.0734 192  \n274.0966 220  \n292.1116 313 \n25.5 C12H20O10 324.106 \n163.0609 21 2 79661 88.6 AutoMSn (365.1092) \n185.0435 195 \n203.0524 417 \n365.1107 521  C6H10O5 162.053 \n163.0609 21 2 79661 88.6 AutoMSn (365.1092) \n185.0435 195 \n203.0524 417 \n365.1107 521  C6H10O5 162.053 \n163.0609 21 2 79661 88.6 AutoMSn (365.1092) \n185.0435 195 \n203.0524 417 \n365.1107 521  C6H10O5 162.053 \n163.0609 21 2 79661 88.6 AutoMSn (365.1092) \n185.0435 195 \n203.0524 417 \n365.1107 521  C6H10O5 162.053 \n163.0609 21 2 79661 88.6 AutoMSn (365.1092) \n185.0435 195 \n203.0524 417 \n365.1107 521  C6H10O5 162.053 \n163.0609 21 2 79661 88.6 AutoMSn (365.1092) \n185.0435 195 \n203.0524 417 \n365.1107 521  C6H10O5 162.053 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 117 Computational Molecular Bioscience \n \n163.0609 21 2 79661 88.6 AutoMSn (365.1092) \n185.0435 195 \n203.0524 417 \n365.1107 521  \nC10H10O2 162.068 \n163.0609 21 2 79661 88.6 AutoMSn (365.1092) \n185.0435 195 \n203.0524 417 \n365.1107 521  \nC10H10O2 162.068 \n163.0609 21 2 79661 88.6 AutoMSn (365.1092) \n185.0435 195 \n203.0524 417 \n365.1107 521  \nC10H10O2 162.068 \n136.064 39 2.6 29720 70.1 AutoMSn (268.1092) \n115.0393 134  \n119.0369 254  \n133.0525 263  \n136.0640 26078  \n137.0649 1163  \n268.1101 1319 \n \nC4H9NO4 135.053 \n136.064 39 2.6 29720 70.1 AutoMSn (268.1092) \n115.0393 134  \n119.0369 254  \n133.0525 263  \n136.0640 26078  \n137.0649 1163  \n268.1101 1319 \n \nC8H9NO 135.068 \n136.064 39 2.6 29720 70.1 AutoMSn (268.1092) \n115.0393 134  \n119.0369 254  \n133.0525 263  \n136.0640 26078  \n137.0649 1163  \n268.1101 1319 \n \nC4H9NO4 135.053 \n136.064 39 2.6 29720 70.1 AutoMSn (268.1092) \n115.0393 134  \n119.0369 254  \n133.0525 263  \n136.0640 26078  \n137.0649 1163  \n268.1101 1319 \n \nC8H9NO 135.068 \n136.064 39 2.6 29720 70.1 AutoMSn (268.1092) \n115.0393 134  \n119.0369 254  \n133.0525 263  \n136.0640 26078  \n137.0649 1163  \n268.1101 1319 \n \nC5H5N5 135.054 \n136.064 39 2.6 29720 70.1 AutoMSn (268.1092) \n115.0393 134  \n119.0369 254  \n133.0525 263  \n136.0640 26078  \n137.0649 1163  \n268.1101 1319 \n \nC8H9NO 135.068 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 118 Computational Molecular Bioscience \n \n136.064 39 2.6 29720 70.1 AutoMSn (268.1092) \n115.0393 134  \n119.0369 254  \n133.0525 263  \n136.0640 26078  \n137.0649 1163  \n268.1101 1319 \n \nC8H9NO 135.068 \n215.0195 45 3 44717 70.9 AutoMSn (130.0523) \n130.0525 5009  \n163.0611 904  \n175.0267 1013  \n193.0369 2962  \n215.0195 11372  \n230.9944 2343  \n259.0974 1813  \n291.0178 2990  \n322.0834 2695  \n407.0505 4487 \n \nC12H6O4 214.027 \n215.0195 45 3 44717 70.9 AutoMSn (130.0523) \n130.0525 5009  \n163.0611 904  \n175.0267 1013  \n193.0369 2962  \n215.0195 11372  \n230.9944 2343  \n259.0974 1813  \n291.0178 2990  \n322.0834 2695  \n407.0505 4487 \n \nC5H11O7P 214.024 \n215.0195 45 3 44717 70.9 AutoMSn (130.0523) \n130.0525 5009  \n163.0611 904  \n175.0267 1013  \n193.0369 2962  \n215.0195 11372  \n230.9944 2343  \n259.0974 1813  \n291.0178 2990  \n322.0834 2695  \n407.0505 4487 \n \nC5H11O7P 214.024 \n215.0195 45 3 44717 70.9 AutoMSn (130.0523) \n130.0525 5009  \n163.0611 904  \n175.0267 1013  \n193.0369 2962  \n215.0195 11372  \n230.9944 2343  \n259.0974 1813  \n291.0178 2990  \n322.0834 2695  \n407.0505 4487 \n \nC5H11O7P 214.024 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 119 Computational Molecular Bioscience \n \n215.0195 45 3 44717 70.9 AutoMSn (130.0523) \n130.0525 5009  \n163.0611 904  \n175.0267 1013  \n193.0369 2962  \n215.0195 11372  \n230.9944 2343  \n259.0974 1813  \n291.0178 2990  \n322.0834 2695  \n407.0505 4487 \n C5H11O7P 214.024 \n215.0195 45 3 44717 70.9 AutoMSn (130.0523) \n130.0525 5009  \n163.0611 904  \n175.0267 1013  \n193.0369 2962  \n215.0195 11372  \n230.9944 2343  \n259.0974 1813  \n291.0178 2990  \n322.0834 2695  \n407.0505 4487 \n C7H6N2O6 214.023 \n251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) \n147.0476 786  \n175.0395 4262  \n176.0436 370  \n207.0649 7163  \n208.0668 876  \n236.0678 1165  \n251.0943 215671  \n252.0974 23390  \n253.0976 1821  \n310.1664 1449 \n C10H18O5S 250.087 \n251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) \n147.0476 786  \n175.0395 4262  \n176.0436 370  \n207.0649 7163  \n208.0668 876  \n236.0678 1165  \n251.0943 215671  \n252.0974 23390  \n253.0976 1821  \n310.1664 1449 \n C10H18O5S 250.087 \n251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) \n147.0476 786  \n175.0395 4262  \n176.0436 370  \n207.0649 7163  \n208.0668 876  \n236.0678 1165  \n251.0943 215671  \n252.0974 23390  \n253.0976 1821  \n310.1664 1449 \n C16H14N2O 250.111 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 120 Computational Molecular Bioscience \n \n251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) \n147.0476 786  \n175.0395 4262  \n176.0436 370  \n207.0649 7163  \n208.0668 876  \n236.0678 1165  \n251.0943 215671  \n252.0974 23390  \n253.0976 1821  \n310.1664 1449 \n C16H14N2O 250.111 \n251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) \n147.0476 786  \n175.0395 4262  \n176.0436 370  \n207.0649 7163  \n208.0668 876  \n236.0678 1165  \n251.0943 215671  \n252.0974 23390  \n253.0976 1821  \n310.1664 1449 \n C17H14O2 250.099 \n251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) \n147.0476 786  \n175.0395 4262  \n176.0436 370  \n207.0649 7163  \n208.0668 876  \n236.0678 1165  \n251.0943 215671  \n252.0974 23390  \n253.0976 1821  \n310.1664 1449 \n C16H14N2O 250.111 \n251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) \n147.0476 786  \n175.0395 4262  \n176.0436 370  \n207.0649 7163  \n208.0668 876  \n236.0678 1165  \n251.0943 215671  \n252.0974 23390  \n253.0976 1821  \n310.1664 1449 \n C15H10N2O2 250.074 \n251.0943 65 7.3 247093 887.7 AutoMSn (310.1649) \n147.0476 786  \n175.0395 4262  \n176.0436 370  \n207.0649 7163  \n208.0668 876  \n236.0678 1165  \n251.0943 215671  \n252.0974 23390  \n253.0976 1821  \n310.1664 1449 \n C16H14N2O 250.111 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 121 Computational Molecular Bioscience \n \n295.1332 69 7.6 297019 549 AutoMSn (589.2497) \n120.0817 4190  \n180.1027 40659  \n181.1057 4225  \n200.0714 3809  \n235.1091 36428  \n236.1120 4538  \n260.0937 11591  \n277.1198 6738  \n295.1321 29595  \n296.1337 4089 \n C12H19N6OP 294.136 \n295.1332 69 7.6 297019 549 AutoMSn (589.2497) \n120.0817 4190  \n180.1027 40659  \n181.1057 4225  \n200.0714 3809  \n235.1091 36428  \n236.1120 4538  \n260.0937 11591  \n277.1198 6738  \n295.1321 29595  \n296.1337 4089 \n C18H18N2O2 294.137 \n295.1332 69 7.6 297019 549 AutoMSn (589.2497) \n120.0817 4190  \n180.1027 40659  \n181.1057 4225  \n200.0714 3809  \n235.1091 36428  \n236.1120 4538  \n260.0937 11591  \n277.1198 6738  \n295.1321 29595  \n296.1337 4089 \n C18H18N2O2 294.137 \n295.1332 69 7.6 297019 549 AutoMSn (589.2497) \n120.0817 4190  \n180.1027 40659  \n181.1057 4225  \n200.0714 3809  \n235.1091 36428  \n236.1120 4538  \n260.0937 11591  \n277.1198 6738  \n295.1321 29595  \n296.1337 4089 \n C10H18N2O8 294.106 \n295.1332 69 7.6 297019 549 AutoMSn (589.2497) \n120.0817 4190  \n180.1027 40659  \n181.1057 4225  \n200.0714 3809  \n235.1091 36428  \n236.1120 4538  \n260.0937 11591  \n277.1198 6738  \n295.1321 29595  \n296.1337 4089 \n C19H18O3 294.126 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 122 Computational Molecular Bioscience \n \n295.1332 69 7.6 297019 549 AutoMSn (589.2497) \n120.0817 4190  \n180.1027 40659  \n181.1057 4225  \n200.0714 3809  \n235.1091 36428  \n236.1120 4538  \n260.0937 11591  \n277.1198 6738  \n295.1321 29595  \n296.1337 4089 \n \nC15H18O6 294.11 \n183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) \n118.0919 111  \n122.0829 442  \n242.2479 2983  \n243.2535 413  \n272.2650 272  \n288.2918 397  \n289.2902 120  \n290.2724 5694  \n291.2730 948 \n \nC9H10O4 182.058 \n183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) \n118.0919 111  \n122.0829 442  \n242.2479 2983  \n243.2535 413  \n272.2650 272  \n288.2918 397  \n289.2902 120  \n290.2724 5694  \n291.2730 948 \n \nC9H10O4 182.058 \n183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) \n118.0919 111  \n122.0829 442  \n242.2479 2983  \n243.2535 413  \n272.2650 272  \n288.2918 397  \n289.2902 120  \n290.2724 5694  \n291.2730 948 \n \nC9H10O4 182.058 \n183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) \n118.0919 111  \n122.0829 442  \n242.2479 2983  \n243.2535 413  \n272.2650 272  \n288.2918 397  \n289.2902 120  \n290.2724 5694  \n291.2730 948 \n \nC9H10O4 182.058 \n\nU. Shahieda Lazaroo Bt Zurrein Shah Lazaroo et al. \n \n \nDOI: 10.4236/cmb.2024.142004 123 Computational Molecular Bioscience \n \n183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) \n118.0919 111  \n122.0829 442  \n242.2479 2983  \n243.2535 413  \n272.2650 272  \n288.2918 397  \n289.2902 120  \n290.2724 5694  \n291.2730 948 \n C9H10O4 182.058 \n183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) \n118.0919 111  \n122.0829 442  \n242.2479 2983  \n243.2535 413  \n272.2650 272  \n288.2918 397  \n289.2902 120  \n290.2724 5694  \n291.2730 948 \n C9H10O4 182.058 \n183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) \n118.0919 111  \n122.0829 442  \n242.2479 2983  \n243.2535 413  \n272.2650 272  \n288.2918 397  \n289.2902 120  \n290.2724 5694  \n291.2730 948 \n C9H10O4 182.058 \n183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) \n118.0919 111  \n122.0829 442  \n242.2479 2983  \n243.2535 413  \n272.2650 272  \n288.2918 397  \n289.2902 120  \n290.2724 5694  \n291.2730 948 \n C6H14O6 182.079 \n183.0787 111 9.5 34410 97.2 AutoMSn (290.2694) \n118.0919 111  \n122.0829 442  \n242.2479 2983  \n243.2535 413  \n272.2650 272  \n288.2918 397  \n289.2902 120  \n290.2724 5694  \n291.2730 948 \n C6H14O6 182.079","source_license":"CC0","license_restricted":false}