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Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches.

التفاصيل البيبلوغرافية
العنوان: Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches.
المؤلفون: Das AP; Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, I-7, Sector-39, Noida, Uttar Pradesh, 201301, India., Agarwal SM; Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, I-7, Sector-39, Noida, Uttar Pradesh, 201301, India. smagarwal@yahoo.com.
المصدر: Molecular diversity [Mol Divers] 2024 Apr; Vol. 28 (2), pp. 901-925. Date of Electronic Publication: 2023 Jan 21.
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: ESCOM Science Publishers Country of Publication: Netherlands NLM ID: 9516534 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-501X (Electronic) Linking ISSN: 13811991 NLM ISO Abbreviation: Mol Divers Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Leiden, The Netherlands : ESCOM Science Publishers, c1995-
مواضيع طبية MeSH: Drug Discovery*/methods , Phytochemicals*/chemistry , Phytochemicals*/pharmacology, Humans ; Quantitative Structure-Activity Relationship ; Antineoplastic Agents, Phytogenic/chemistry ; Antineoplastic Agents, Phytogenic/pharmacology ; Antineoplastic Agents/chemistry ; Antineoplastic Agents/pharmacology ; Neoplasms/drug therapy ; Machine Learning ; Computational Biology/methods ; Molecular Dynamics Simulation
مستخلص: Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.
(© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
References: Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660. (PMID: 10.3322/caac.2166033538338)
Choudhari AS, Mandave PC, Deshpande M et al (2019) Phytochemicals in cancer treatment: from preclinical studies to clinical practice. Front Pharmacol 10:1614. https://doi.org/10.3389/fphar.2019.01614. (PMID: 10.3389/fphar.2019.0161432116665)
Cragg GM, Newman DJ (2013) Natural products: a continuing source of novel drug leads. Biochim Biophys Acta 1830:3670–3695. https://doi.org/10.1016/j.bbagen.2013.02.008. (PMID: 10.1016/j.bbagen.2013.02.008234285723672862)
Thomford NE, Senthebane DA, Rowe A et al (2018) Natural products for drug discovery in the 21st century: innovations for novel drug discovery. Int J Mol Sci 19. https://doi.org/10.3390/ijms19061578.
Sharma A, Sharma S, Gupta M et al (2018) Pharmacokinetic profiling of anticancer phytocompounds using computational approach. Phytochem Anal 29:559–568. https://doi.org/10.1002/pca.2767. (PMID: 10.1002/pca.276729667756)
Ntie-Kang F, Mbah JA, Lifongo LL et al (2013) Assessing the pharmacokinetic profile of the CamMedNP natural products database: an in silico approach. Org Med Chem Lett 3:10. https://doi.org/10.1186/2191-2858-3-10. (PMID: 10.1186/2191-2858-3-10242294553767462)
Ntie-Kang F, Nwodo JN, Ibezim A et al (2014) Molecular modeling of potential anticancer agents from African medicinal plants. J Chem Inf Model 54:2433–2450. https://doi.org/10.1021/ci5003697. (PMID: 10.1021/ci500369725116740)
Fatima S, Gupta P, Sharma S et al (2019) ADMET profiling of geographically diverse phytochemical using chemoinformatic tools. Future Med Chem 12:69–87. https://doi.org/10.4155/fmc-2019-0206. (PMID: 10.4155/fmc-2019-020631793338)
Cragg GM, Pezzuto JM (2016) Natural products as a vital source for the discovery of cancer chemotherapeutic and chemopreventive agents. Med Princ Pract 25:41–59. https://doi.org/10.1159/000443404. (PMID: 10.1159/00044340426679767)
Singh H, Kumar R, Singh S et al (2016) Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines. BMC Cancer 16:1–10. https://doi.org/10.1186/s12885-016-2082-y. (PMID: 10.1186/s12885-016-2082-y)
Singh S, Sharma B, Kanwar SS, Kumar A (2016) Lead phytochemicals for anticancer drug development. Front Plant Sci 7:1–13. https://doi.org/10.3389/fpls.2016.01667. (PMID: 10.3389/fpls.2016.01667)
Moraes DFC, de Mesquita LSS, do Amaral FMM et al (2017) Anticancer drugs from plants. In: Malik S (ed) Biotechnology and production of anti-cancer compounds. Springer, Cham, pp 121–142. (PMID: 10.1007/978-3-319-53880-8_5)
Newman DJ, Cragg GM (2016) natural products as sources of new drugs from 1981 to 2014. J Nat Prod 79:629–661. https://doi.org/10.1021/acs.jnatprod.5b01055. (PMID: 10.1021/acs.jnatprod.5b0105526852623)
Mangal M, Sagar P, Singh H et al (2013) NPACT: naturally occurring plant-based anti-cancer compound-activity-target database. Nucleic Acids Res 41:1124–1129. https://doi.org/10.1093/nar/gks1047. (PMID: 10.1093/nar/gks1047)
Marx V (2013) Biology: the big challenges of big data. Nature 498:255–260. https://doi.org/10.1038/498255a. (PMID: 10.1038/498255a23765498)
Rampogu S, Kim SM, Son M et al (2020) A computational approach with biological evaluation: combinatorial treatment of curcumin and exemestane synergistically regulates ddx3 expression in cancer cell lines. Biomolecules 10:1–20. https://doi.org/10.3390/biom10060857. (PMID: 10.3390/biom10060857)
Jairajpuri DS, Mohammad T, Adhikari K et al (2020) Identification of sphingosine kinase-1 inhibitors from bioactive natural products targeting cancer therapy. ACS Omega 5:14720–14729. https://doi.org/10.1021/acsomega.0c01511. (PMID: 10.1021/acsomega.0c01511325966097315586)
Alkaff AH, Saragih M, Imana SN et al (2021) Identification of DNA methyltransferase-1 inhibitor for breast cancer therapy through computational fragment-based drug design. Molecules 26:1–18. https://doi.org/10.3390/molecules26020375. (PMID: 10.3390/molecules26020375)
Yadav D, Nath Mishra B, Khan F (2019) 3D-QSAR and docking studies on ursolic acid derivatives for anticancer activity based on bladder cell line T24 targeting NF-kB pathway inhibition. J Biomol Struct Dyn 37:3822–3837. https://doi.org/10.1080/07391102.2018.1528888. (PMID: 10.1080/07391102.2018.152888830261824)
Dhiman K, Agarwal SM (2016) NPred: QSAR classification model for identifying plant based naturally occurring anti-cancerous inhibitors. RSC Adv 6:49395–49400. https://doi.org/10.1039/c6ra02772e. (PMID: 10.1039/c6ra02772e)
Chen Y, Stork C, Hirte S, Kirchmair J (2019) NP-scout: machine learning approach for the quantification and visualization of the natural product-likeness of small molecules. Biomolecules 9:43. https://doi.org/10.3390/biom9020043.
Sorokina M, Steinbeck C (2019) Naples: a natural products likeness scorer—web application and database. J Cheminform 11:1–7. https://doi.org/10.1186/s13321-019-0378-z. (PMID: 10.1186/s13321-019-0378-z)
Jayaseelan KV, Moreno P, Truszkowski A et al (2012) Natural product-likeness score revisited: an open-source, open-data implementation. BMC Bioinformatics 13:106. https://doi.org/10.1186/1471-2105-13-106. (PMID: 10.1186/1471-2105-13-106226072713436723)
Kim C, Kim B (2018) Anti-cancer natural products and their bioactive compounds inducing ER stress-mediated apoptosis: a review. Nutrients 10:1021. https://doi.org/10.3390/nu10081021.
Ezzati M, Yousefi B, Velaei K, Safa A (2020) A review on anti-cancer properties of Quercetin in breast cancer. Life Sci 248:117463. https://doi.org/10.1016/j.lfs.2020.117463. (PMID: 10.1016/j.lfs.2020.11746332097663)
Blowman K, Magalhães M, Lemos MFL et al (2018) Anticancer properties of essential oils and other natural products. Evid Based Complement Altern Med 2018:3149362. https://doi.org/10.1155/2018/3149362.
Chen J, Li W, Yao H, Xu J (2015) Insights into drug discovery from natural products through structural modification. Fitoterapia 103:231–241. https://doi.org/10.1016/j.fitote.2015.04.012. (PMID: 10.1016/j.fitote.2015.04.01225917513)
Li G, Lou HX (2018) Strategies to diversify natural products for drug discovery. Med Res Rev 38:1255–1294. https://doi.org/10.1002/med.21474. (PMID: 10.1002/med.2147429064108)
Lin X, Li X, Lin X (2020) A review on applications of computational methods in drug screening and design. Molecules 25:1–17. https://doi.org/10.3390/molecules25061375. (PMID: 10.3390/molecules25061375)
Prada-Gracia D, Huerta-Yépez S, Moreno-Vargas LM (2016) Application of computational methods for anticancer drug discovery, design, and optimization. Bol Med Hosp Infant Mex 73:411–423. https://doi.org/10.1016/j.bmhime.2017.11.040. (PMID: 10.1016/j.bmhime.2017.11.040294212867110968)
Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28:235–242. https://doi.org/10.1093/nar/28.1.235. (PMID: 10.1093/nar/28.1.23510592235102472)
Raj U, Kumar H, Varadwaj PK (2017) Molecular docking and dynamics simulation study of flavonoids as BET bromodomain inhibitors. J Biomol Struct Dyn 35:2351–2362. https://doi.org/10.1080/07391102.2016.1217276. (PMID: 10.1080/07391102.2016.121727627494802)
Wermuth CG, Villoutreix B, Grisoni S et al (2015) Strategies in the search for new lead compounds or original working hypotheses. In: Wermuth CG, Aldous D, Raboisson P, Rognan D (eds) The practice of medicinal chemistry, 4th edn. Academic Press, San Diego, pp 73–99. (PMID: 10.1016/B978-0-12-417205-0.00004-3)
Bommu UD, Konidala KK, Pabbaraju N, Yeguvapalli S (2017) Ligand-based virtual screening, molecular docking, QSAR and pharmacophore analysis of quercetin-associated potential novel analogs against epidermal growth factor receptor. J Recept Signal Transduct 37:600–610. https://doi.org/10.1080/10799893.2017.1377237. (PMID: 10.1080/10799893.2017.1377237)
Labbé CM, Rey J, Lagorce D et al (2015) MTiOpenScreen: a web server for structure-based virtual screening. Nucleic Acids Res 43:W448–W454. https://doi.org/10.1093/nar/gkv306. (PMID: 10.1093/nar/gkv306258558124489289)
Zarezade V, Abolghasemi M, Rahim F et al (2018) In silico assessment of new progesterone receptor inhibitors using molecular dynamics: a new insight into breast cancer treatment. J Mol Model 24. https://doi.org/10.1007/s00894-018-3858-6.
Agarwal SM, Nandekar P, Saini R (2022) Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach. RSC Adv 12:16779–16789. https://doi.org/10.1039/d2ra00373b. (PMID: 10.1039/d2ra00373b357548759170516)
Muhseen ZT, Li G (2020) Promising terpenes as natural antagonists of cancer: an in-silico approach. Molecules 25:1–17. https://doi.org/10.3390/molecules25010155. (PMID: 10.3390/molecules25010155)
Sterling T, Irwin JJ (2015) ZINC 15 – Ligand discovery for everyone. J Chem Inf Model 55:2324–2337. https://doi.org/10.1021/acs.jcim.5b00559.
Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. https://doi.org/10.1002/jcc.21256. (PMID: 10.1002/jcc.21256193997802760638)
Biswas R, Chowdhury N, Mukherjee R, Bagchi A (2018) Identification and analyses of natural compounds as potential inhibitors of TRAF6-Basigin interactions in melanoma using structure-based virtual screening and molecular dynamics simulations. J Mol Graph Model 85:281–293. https://doi.org/10.1016/j.jmgm.2018.09.008. (PMID: 10.1016/j.jmgm.2018.09.00830253283)
Pierce BG, Wiehe K, Hwang H et al (2014) ZDOCK server: interactive docking prediction of protein–protein complexes and symmetric multimers. Bioinformatics 30:1771–1773. https://doi.org/10.1093/bioinformatics/btu097. (PMID: 10.1093/bioinformatics/btu097245327264058926)
Verdonk ML, Cole JC, Hartshorn MJ et al (2003) Improved protein-ligand docking using GOLD. Proteins 52:609–623. https://doi.org/10.1002/prot.10465. (PMID: 10.1002/prot.1046512910460)
Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res 33:W363–W367. https://doi.org/10.1093/nar/gki481. (PMID: 10.1093/nar/gki481159804901160241)
Halgren TA (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 49:377–389. https://doi.org/10.1021/ci800324m.
Friesner RA, Murphy RB, Repasky MP et al (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49:6177–6196. https://doi.org/10.1021/jm051256o. (PMID: 10.1021/jm051256o17034125)
Hilbig M, Urbaczek S, Groth I et al (2013) MONA—interactive manipulation of molecule collections. J Cheminform 5:1–10. https://doi.org/10.1186/1758-2946-5-38. (PMID: 10.1186/1758-2946-5-38)
Hindle SA, Rarey M, Buning C, Lengauer T (2002) Flexible docking under pharmacophore type constraints. J Comput Aided Mol Des 16:129–149. https://doi.org/10.1023/A:1016399411208. (PMID: 10.1023/A:101639941120812188022)
Güner OF, Bowen JP (2014) Setting the record straight: the origin of the pharmacophore concept. J Chem Inf Model 54:1269–1283. https://doi.org/10.1021/ci5000533. (PMID: 10.1021/ci500053324745881)
Kalyaanamoorthy S, Chen YPP (2011) Structure-based drug design to augment hit discovery. Drug Discov Today 16:831–839. https://doi.org/10.1016/j.drudis.2011.07.006. (PMID: 10.1016/j.drudis.2011.07.00621810482)
Babu TMC, Rammohan A, Baki VB et al (2016) Development of novel HER2 inhibitors against gastric cancer derived from flavonoid source of Syzygium alternifolium through molecular dynamics and pharmacophore-based screening. Drug Des Dev Ther 10:3611–3632. https://doi.org/10.2147/DDDT.S111914. (PMID: 10.2147/DDDT.S111914)
Singh PK, Silakari O (2018) Pharmacophore and molecular dynamics based activity profiling of natural products for kinases involved in lung cancer. J Mol Model 24. https://doi.org/10.1007/s00894-018-3849-7.
Alamri MA, Afzal O, Alamri MA (2020) Computational screening of natural and natural-like compounds to identify novel ligands for sigma-2 receptor. SAR QSAR Environ Res 31:837–856. https://doi.org/10.1080/1062936X.2020.1819870. (PMID: 10.1080/1062936X.2020.181987033100033)
Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5:725–738. https://doi.org/10.1038/nprot.2010.5. (PMID: 10.1038/nprot.2010.5203607672849174)
Nastasi G, Miceli C, Pittalà V et al (2017) S2RSLDB: a comprehensive manually curated, internet-accessible database of the sigma-2 receptor selective ligands. J Cheminform 9:3. https://doi.org/10.1186/s13321-017-0191-5. (PMID: 10.1186/s13321-017-0191-5281234525250622)
Kumar V, Parate S, Thakur G et al (2021) Identification of cdk7 inhibitors from natural sources using pharmacoinformatics and molecular dynamics simulations. Biomedicines 9:1197. https://doi.org/10.3390/biomedicines9091197.
Hähnke VD, Kim S, Bolton EE (2018) PubChem chemical structure standardization. J Cheminform 10:1–40. https://doi.org/10.1186/s13321-018-0293-8. (PMID: 10.1186/s13321-018-0293-8)
Banerjee P, Erehman J, Gohlke BO et al (2015) Super Natural II—a database of natural products. Nucleic Acids Res 43:D935–D939. https://doi.org/10.1093/nar/gku886. (PMID: 10.1093/nar/gku88625300487)
Cherkasov A, Muratov EN, Fourches D et al (2014) QSAR modeling: where have you been? where are you going to? J Med Chem 57:4977–5010. https://doi.org/10.1021/jm4004285. (PMID: 10.1021/jm4004285243510514074254)
Pratim Roy P, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14:1660–1701. https://doi.org/10.3390/molecules14051660.
Alam S, Khan F (2017) 3D-QSAR studies on maslinic acid analogs for anticancer activity against breast cancer cell line MCF-7. Sci Rep 7:1–13. https://doi.org/10.1038/s41598-017-06131-0. (PMID: 10.1038/s41598-017-06131-0)
Chen G, Zhou D, Li XZ et al (2017) A natural chalcone induces apoptosis in lung cancer cells: 3D-QSAR, docking and an in vivo/vitro assay. Sci Rep 7:1–10. https://doi.org/10.1038/s41598-017-11369-9. (PMID: 10.1038/s41598-017-11369-9)
Yadav DK, Kumar S, Saloni et al (2017) Molecular docking, QSAR and ADMET studies of with anolide analogs against breast cancer. Drug Des Dev Ther 11:1859–1870. https://doi.org/10.2147/DDDT.S130601. (PMID: 10.2147/DDDT.S130601)
Spitzer R, Jain AN (2012) Surflex-Dock: Docking benchmarks and real-world application. J Comput Aided Mol Des 26:687–699. https://doi.org/10.1007/s10822-011-9533-y. (PMID: 10.1007/s10822-011-9533-y225695903398190)
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26. https://doi.org/10.1016/s0169-409x(00)00129-0. (PMID: 10.1016/s0169-409x(00)00129-011259830)
Shukla A, Tyagi R, Meena S et al (2020) 2D- and 3D-QSAR modelling, molecular docking and in vitro evaluation studies on 18β-glycyrrhetinic acid derivatives against triple-negative breast cancer cell line. J Biomol Struct Dyn 38:168–185. https://doi.org/10.1080/07391102.2019.1570868. (PMID: 10.1080/07391102.2019.157086830686140)
Meneses-Sagrero SE, Rascón-Valenzuela LA, Sotelo-Mundo R et al (2021) Antiproliferative activity of cardenolides on cell line A549: structure-activity relationship analysis. Mol Divers 25:2289–2305. https://doi.org/10.1007/s11030-020-10119-w. (PMID: 10.1007/s11030-020-10119-w32627094)
Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474. https://doi.org/10.1002/jcc.21707.
Rampogu S, Ravinder D, Pawar SC, Lee KW (2018) Natural compound modulates the cervical cancer microenvironment—a pharmacophore guided molecular modelling approaches. J Clin Med 7. https://doi.org/10.3390/jcm7120551.
Taha KF, Khalil M, Abubakr MS, Shawky E (2020) Identifying cancer-related molecular targets of Nandina domestica Thunb. by network pharmacology-based analysis in combination with chemical profiling and molecular docking studies. J Ethnopharmacol 249:112413. https://doi.org/10.1016/j.jep.2019.112413.
Sinha S, Patel S, Athar M et al (2019) Structure-based identification of novel sirtuin inhibitors against triple negative breast cancer: an in silico and in vitro study. Int J Biol Macromol 140:454–468. https://doi.org/10.1016/j.ijbiomac.2019.08.061. (PMID: 10.1016/j.ijbiomac.2019.08.06131404596)
Cavuturu BM, Bhandare VV, Ramaswamy A, Arumugam N (2019) Molecular dynamics of interaction of Sesamin and related compounds with the cancer marker β-catenin: an in silico study. J Biomol Struct Dyn 37:877–891. https://doi.org/10.1080/07391102.2018.1442250. (PMID: 10.1080/07391102.2018.144225029455637)
Prabhavathi H, Dasegowda KR, Renukananda KH et al (2020) Molecular docking and dynamic simulation to identify potential phytocompound inhibitors for EGFR and HER2 as anti-breast cancer agents. J Biomol Struct Dyn 0:1–12. https://doi.org/10.1080/07391102.2020.1861982.
Gupta P, Mohammad T, Dahiya R et al (2019) Evaluation of binding and inhibition mechanism of dietary phytochemicals with sphingosine kinase 1: towards targeted anticancer therapy. Sci Rep 9:1–15. https://doi.org/10.1038/s41598-019-55199-3. (PMID: 10.1038/s41598-019-55199-3)
Mohammad T, Siddiqui S, Shamsi A et al (2020) Virtual screening approach to identify high-affinity inhibitors of serum and glucocorticoid-regulated kinase 1 among bioactive natural products: combined molecular docking and simulation studies. Molecules 25:823. https://doi.org/10.3390/molecules25040823.
Hadavand Mirzaei H, Jassbi AR, Pirhadi S, Firuzi O (2020) Study of the mechanism of action, molecular docking, and dynamics of anticancer terpenoids from Salvia lachnocalyx. J Recept Signal Transduct 40:24–33. https://doi.org/10.1080/10799893.2019.1710847. (PMID: 10.1080/10799893.2019.1710847)
Maiti P, Nand M, Joshi T et al (2021) Identification of luteolin -7-glucoside and epicatechin gallate from Vernonia cinerea, as novel EGFR L858R kinase inhibitors against lung cancer: Docking and simulation-based study. J Biomol Struct Dyn 39:5048–5057. https://doi.org/10.1080/07391102.2020.1784791. (PMID: 10.1080/07391102.2020.178479132579072)
Gurung AB, Ali MA, Lee J et al (2021) Molecular docking and dynamics simulation study of bioactive compounds from Ficus carica L. With important anticancer drug targets. PLoS ONE 16:1–16. https://doi.org/10.1371/journal.pone.0254035. (PMID: 10.1371/journal.pone.0254035)
Jha V, Devkar S, Gharat K et al (2022) Screening of phytochemicals as potential inhibitors of breast cancer using structure based multitargeted molecular docking analysis. Phytomedicine Plus 2:100227. https://doi.org/10.1016/j.phyplu.2022.100227. (PMID: 10.1016/j.phyplu.2022.100227)
Fan J, De Lannoy IAM (2014) Pharmacokinetics. Biochem Pharmacol 87:93–120. https://doi.org/10.1016/j.bcp.2013.09.007. (PMID: 10.1016/j.bcp.2013.09.00724055064)
Pires DEV, Blundell TL, Ascher DB (2015) pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 58:4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104. (PMID: 10.1021/acs.jmedchem.5b00104258608344434528)
Sander T, Freyss J, von Korff M, Rufener C (2015) DataWarrior: an open-source program for chemistry aware data visualization and analysis. J Chem Inf Model 55:460–473. https://doi.org/10.1021/ci500588j. (PMID: 10.1021/ci500588j25558886)
Pathania S, Ramakrishnan SM, Bagler G (2015) Phytochemica: a platform to explore phytochemicals of medicinal plants. Database 2015:1–8. https://doi.org/10.1093/database/bav075. (PMID: 10.1093/database/bav075)
Pathania S, Ramakrishnan SM, Randhawa V, Bagler G (2015) SerpentinaDB: a database of plant-derived molecules of Rauvolfia serpentina. BMC Complement Altern Med 15:1–8. https://doi.org/10.1186/s12906-015-0683-7. (PMID: 10.1186/s12906-015-0683-7)
Hatherley R, Brown DK, Musyoka TM et al (2015) SANCDB: a South African natural compound database. J Cheminform 7:29. https://doi.org/10.1186/s13321-015-0080-8. (PMID: 10.1186/s13321-015-0080-8260975104471313)
Pilon AC, Valli M, Dametto AC et al (2017) NuBBE(DB): an updated database to uncover chemical and biological information from Brazilian biodiversity. Sci Rep 7:7215. https://doi.org/10.1038/s41598-017-07451-x. (PMID: 10.1038/s41598-017-07451-x287753355543130)
Tao W, Li B, Gao S et al (2015) CancerHSP: anticancer herbs database of systems pharmacology. Sci Rep 5:11481. https://doi.org/10.1038/srep11481. (PMID: 10.1038/srep11481260744884466901)
Murugan K, Shanmugasamy S, Al-Sohaibani S et al (2015) TaxKB: a knowledge base for new taxane-related drug discovery. BioData Min 8:19. https://doi.org/10.1186/s13040-015-0053-5. (PMID: 10.1186/s13040-015-0053-5261310214485360)
Adnan M, Siddiqui AJ, Hamadou WS et al (2021) Phytochemistry, bioactivities, pharmacokinetics and toxicity prediction of Selaginella repanda with its anticancer potential against human lung, breast and colorectal carcinoma cell lines. Molecules 26:768. https://doi.org/10.3390/molecules26030768.
Shang W, Zhang J, Song H et al (2021) Mechanism of tetrandrine against endometrial cancer based on network pharmacology. Drug Des Dev Ther 15:2907–2919. https://doi.org/10.2147/DDDT.S307670. (PMID: 10.2147/DDDT.S307670)
Yue Z, Zhang W, Lu Y, Yang Q, Ding Q, Xia J, Chen Y (2015) Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties. PeerJ 3:e1425. https://doi.org/10.7717/peerj.1425. (PMID: 10.7717/peerj.1425266449764671159)
Chandran U, Mehendale N, Patil S et al (2017) Network pharmacology. Innov Approaches Drug Discov 127–164. https://doi.org/10.1016/B978-0-12-801814-9.00005-2.
Kanehisa M, Furumichi M, Sato Y et al (2021) KEGG: integrating viruses and cellular organisms. Nucleic Acids Res 49:D545–D551. https://doi.org/10.1093/nar/gkaa970. (PMID: 10.1093/nar/gkaa97033125081)
Kuhn M, von Mering C, Campillos M et al (2008) STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res 36:D684–D688. https://doi.org/10.1093/nar/gkm795. (PMID: 10.1093/nar/gkm79518084021)
Dennis GJ, Sherman BT, Hosack DA et al (2003) DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 4:P3. (PMID: 10.1186/gb-2003-4-5-p312734009)
Szklarczyk D, Gable AL, Nastou KC et al (2021) The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49:D605–D612. https://doi.org/10.1093/nar/gkaa1074. (PMID: 10.1093/nar/gkaa107433237311)
Zhang N, Wang J, Sheng A et al (2021) emodin inhibits the proliferation of MCF-7 human breast cancer cells through activation of aryl hydrocarbon receptor (AhR). Front Pharmacol 11:1–12. https://doi.org/10.3389/fphar.2020.622046. (PMID: 10.3389/fphar.2020.622046)
Wang X, Shen Y, Wang S et al (2017) PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res 45:W356–W360. https://doi.org/10.1093/nar/gkx374. (PMID: 10.1093/nar/gkx374284724225793840)
Daina A, Michielin O, Zoete V (2019) SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res 47:W357–W364. https://doi.org/10.1093/nar/gkz382. (PMID: 10.1093/nar/gkz382311063666602486)
Ru J, Li P, Wang J et al (2014) TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform 6:13. https://doi.org/10.1186/1758-2946-6-13. (PMID: 10.1186/1758-2946-6-13247356184001360)
Deng Y, Ye X, Chen Y et al (2021) Chemical characteristics of Platycodon grandiflorum and its mechanism in lung cancer treatment. Front Pharmacol 11. https://doi.org/10.3389/fphar.2020.609825.
Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303. (PMID: 10.1101/gr.123930314597658403769)
Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717. https://doi.org/10.1038/srep42717. (PMID: 10.1038/srep42717282565165335600)
Xiong G, Wu Z, Yi J et al (2021) ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res 49:W5–W14. https://doi.org/10.1093/nar/gkab255. (PMID: 10.1093/nar/gkab255338938038262709)
Tian W, Chen C, Lei X et al (2018) CASTp 3.0: Computed atlas of surface topography of proteins. Nucleic Acids Res 46:W363–W367. https://doi.org/10.1093/nar/gky473. (PMID: 10.1093/nar/gky473298603916031066)
Eberhardt J, Santos-Martins D, Tillack AF, Forli S (2021) AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings. J Chem Inf Model 61:3891–3898. https://doi.org/10.1021/acs.jcim.1c00203. (PMID: 10.1021/acs.jcim.1c002033427879410683950)
Yang J-F, Wang F, Chen Y-Z et al (2020) LARMD: integration of bioinformatic resources to profile ligand-driven protein dynamics with a case on the activation of estrogen receptor. Brief Bioinform 21:2206–2218. https://doi.org/10.1093/bib/bbz141. (PMID: 10.1093/bib/bbz14131799600)
Aarthy M, Muthuramalingam P, Ramesh M, Singh SK (2022) Unraveling the multi-targeted curative potential of bioactive molecules against cervical cancer through integrated omics and systems pharmacology approach. Sci Rep 12:1–19. https://doi.org/10.1038/s41598-022-18358-7. (PMID: 10.1038/s41598-022-18358-7)
Parkinson H, Kapushesky M, Shojatalab M et al (2007) ArrayExpress—a public database of microarray experiments and gene expression profiles. Nucleic Acids Res 35:D747–D750. https://doi.org/10.1093/nar/gkl995. (PMID: 10.1093/nar/gkl99517132828)
Zhou G, Soufan O, Ewald J et al (2019) NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 47:W234–W241. https://doi.org/10.1093/nar/gkz240. (PMID: 10.1093/nar/gkz240309314806602507)
Pomaznoy M, Ha B, Peters B (2018) GOnet: a tool for interactive Gene Ontology analysis. BMC Bioinform 19:470. https://doi.org/10.1186/s12859-018-2533-3. (PMID: 10.1186/s12859-018-2533-3)
Szklarczyk D, Franceschini A, Wyder S et al (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:D447–D452. https://doi.org/10.1093/nar/gku1003. (PMID: 10.1093/nar/gku100325352553)
Dong Y, Hao L, Fang K et al (2021) A network pharmacology perspective for deciphering potential mechanisms of action of Solanum nigrum L. in bladder cancer. BMC Complement Med Ther 21:1–14. https://doi.org/10.1186/s12906-021-03215-3. (PMID: 10.1186/s12906-021-03215-3)
Wang Y, Zhang Y, Wang Y et al (2021) Using network pharmacology and molecular docking to explore the mechanism of Shan Ci Gu (Cremastra appendiculata) against non-small cell lung cancer. Front Chem 9:1–14. https://doi.org/10.3389/fchem.2021.682862. (PMID: 10.3389/fchem.2021.682862)
Chen Z, Lin T, Liao X et al (2021) Network pharmacology based research into the effect and mechanism of Yinchenhao Decoction against Cholangiocarcinoma. Chin Med (UK) 16:1–20. https://doi.org/10.1186/s13020-021-00423-4. (PMID: 10.1186/s13020-021-00423-4)
Zhang S, Mo Z, Zhang S, Li X (2021) A network pharmacology approach to reveal the underlying mechanisms of Artemisia annua on the treatment of hepatocellular carcinoma. Evid Based Complem Altern Med 2021. https://doi.org/10.1155/2021/8947304.
Zhou L, Gu W, Kui F, et al (2021) The mechanism and candidate compounds of aged citrus peel (chenpi) preventing chronic obstructive pulmonary disease and its progression to lung cancer. Food Nutr Res 65. https://doi.org/10.29219/fnr.v65.7526.
Jiang L, Shi Z, Yang Y (2021) Network pharmacology-based approach to investigate the molecular targets of rhubarb for treating cancer. Evid Based Complem Altern Med 2021. https://doi.org/10.1155/2021/9945633.
Zhang MM, Wang D, Lu F et al (2021) Identification of the active substances and mechanisms of ginger for the treatment of colon cancer based on network pharmacology and molecular docking. BioData Min 14:1–16. https://doi.org/10.1186/s13040-020-00232-9. (PMID: 10.1186/s13040-020-00232-9334309397798235)
Fang T, Liu L, Liu W (2020) Network pharmacology-based strategy for predicting therapy targets of Tripterygium wilfordii on acute myeloid leukemia. Medicine (Baltimore) 99:e23546. https://doi.org/10.1097/MD.0000000000023546. (PMID: 10.1097/MD.000000000002354633327305)
Mi JL, Liu C, Xu M, Wang RS (2020) Network pharmacology to uncover the molecular mechanisms of action of LeiGongTeng for the treatment of nasopharyngeal carcinoma. Med Sci Monit Basic Res 26:923431–923432. https://doi.org/10.12659/MSMBR.923431. (PMID: 10.12659/MSMBR.923431)
Liu Y, An T, Wan D et al (2020) Targets and mechanism used by cinnamaldehyde, the main active ingredient in cinnamon, in the treatment of breast cancer. Front Pharmacol 11:1–12. https://doi.org/10.3389/fphar.2020.582719. (PMID: 10.3389/fphar.2020.582719321166897011082)
Ma TT, Zhang GL, Dai CF et al (2020) Scutellaria barbata and Hedyotis diffusa herb pair for breast cancer treatment: potential mechanism based on network pharmacology. J Ethnopharmacol 259:112929. https://doi.org/10.1016/j.jep.2020.112929. (PMID: 10.1016/j.jep.2020.11292932416245)
Yang B, Wang N, Wang S et al (2019) Network-pharmacology-based identification of caveolin-1 as a key target of Oldenlandia diffusa to suppress breast cancer metastasis. Biomed Pharmacother 112:108607. https://doi.org/10.1016/j.biopha.2019.108607. (PMID: 10.1016/j.biopha.2019.10860730784915)
Shawky E (2019) Prediction of potential cancer-related molecular targets of North African plants constituents using network pharmacology-based analysis. J Ethnopharmacol 238:111826. https://doi.org/10.1016/j.jep.2019.111826. (PMID: 10.1016/j.jep.2019.11182630910579)
Liu X, Wu J, Zhang D et al (2018) Network pharmacology-based approach to investigate the mechanisms of Hedyotis diffusa Willd. in the treatment of gastric cancer. Evid Based Complem Altern Med 2018. https://doi.org/10.1155/2018/7802639.
Zhang X, Shen T, Zhou X et al (2020) Network pharmacology based virtual screening of active constituents of Prunella vulgaris L. and the molecular mechanism against breast cancer. Sci Rep 10:1–12. https://doi.org/10.1038/s41598-020-72797-8. (PMID: 10.1038/s41598-020-72797-8)
Lo Y-C, Rensi SE, Torng W, Altman RB (2018) Machine learning in chemoinformatics and drug discovery. Drug Discov Today 23:1538–1546. https://doi.org/10.1016/j.drudis.2018.05.010. (PMID: 10.1016/j.drudis.2018.05.010297509026078794)
Vamathevan J, Clark D, Czodrowski P et al (2019) Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 18:463–477. https://doi.org/10.1038/s41573-019-0024-5. (PMID: 10.1038/s41573-019-0024-5309761076552674)
Zhang L, Tan J, Han D, Zhu H (2017) From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today 22:1680–1685. https://doi.org/10.1016/j.drudis.2017.08.010. (PMID: 10.1016/j.drudis.2017.08.01028881183)
Jeon J, Nim S, Teyra J et al (2014) A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening. Genome Med 6:1–18. https://doi.org/10.1186/s13073-014-0057-7. (PMID: 10.1186/s13073-014-0057-7)
Ferrero E, Dunham I, Sanseau P (2017) In silico prediction of novel therapeutic targets using gene-disease association data. J Transl Med 15:1–16. https://doi.org/10.1186/s12967-017-1285-6. (PMID: 10.1186/s12967-017-1285-6)
Godinez WJ, Hossain I, Lazic SE et al (2017) A multi-scale convolutional neural network for phenotyping high-content cellular images. Bioinformatics 33:2010–2019. https://doi.org/10.1093/bioinformatics/btx069. (PMID: 10.1093/bioinformatics/btx06928203779)
Mamoshina P, Volosnikova M, Ozerov IV et al (2018) Machine learning on human muscle transcriptomic data for biomarker discovery and tissue-specific drug target identification. Front Genet 9:1–10. https://doi.org/10.3389/fgene.2018.00242. (PMID: 10.3389/fgene.2018.00242)
Price EJ, Palát J, Coufaliková K et al (2021) Open, high-resolution EI+ spectral library of anthropogenic compounds. Front Public Health 9:622558. https://doi.org/10.3389/fpubh.2021.622558. (PMID: 10.3389/fpubh.2021.622558337680857985345)
Grigalunas M, Burhop A, Zinken S et al (2021) Natural product fragment combination to performance-diverse pseudo-natural products. Nat Commun 12:1883. https://doi.org/10.1038/s41467-021-22174-4. (PMID: 10.1038/s41467-021-22174-4337671987994817)
Chen Y, Mathai N, Kirchmair J (2020) Scope of 3D shape-based approaches in predicting the macromolecular targets of structurally complex small molecules including natural products and macrocyclic ligands. J Chem Inf Model 60:2858–2875. https://doi.org/10.1021/acs.jcim.0c00161. (PMID: 10.1021/acs.jcim.0c00161323689087312400)
Vaskevych AI, Savinchuk NO, Vaskevych RI et al (2021) The PIFA-initiated oxidative cyclization of 2-(3-butenyl)quinazolin-4(3H)-ones—an efficient approach to 1-(hydroxymethyl)-2,3-dihydropyrrolo[1,2-a]quinazolin-5(1H)-ones. Beilstein J Org Chem 17:2787–2794. https://doi.org/10.3762/bjoc.17.189. (PMID: 10.3762/bjoc.17.189349000098630437)
Lianza M, Leroy R, Machado Rodrigues C et al (2021) The three pillars of natural product dereplication. Alkaloids from the bulbs of Urceolina peruviana (C. Presl) J.F. Macbr. as a preliminary test case. Molecules. https://doi.org/10.3390/molecules26030637. (PMID: 10.3390/molecules26030637335306047865595)
Mikhnevich TA, Vyatkina Turkova AV, Grigorenko VG et al (2021) Inhibition of class A β-lactamase (TEM-1) by narrow fractions of humic substances. ACS Omega 6:23873–23883. https://doi.org/10.1021/acsomega.1c02841. (PMID: 10.1021/acsomega.1c02841345686678459357)
Zhou Z, Luo M, Zhang H et al (2022) Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking. Nat Commun 13:6656. https://doi.org/10.1038/s41467-022-34537-6. (PMID: 10.1038/s41467-022-34537-6363333589636193)
Elend L, Jacobsen L, Cofala T et al (2022) Design of SARS-CoV-2 main protease inhibitors using artificial intelligence and molecular dynamic simulations. Molecules 27:4020. https://doi.org/10.3390/molecules27134020.
Akbarzadeh M, Flegel J, Patil S et al (2022) The pseudo-natural product rhonin targets RHOGDI. Angew Chem Int Ed Engl 61:e202115193. https://doi.org/10.1002/anie.202115193. (PMID: 10.1002/anie.202115193351701819313812)
Ceballos J, Schwalfenberg M, Karageorgis G et al (2019) Synthesis of indomorphan pseudo-natural product inhibitors of glucose transporters GLUT-1 and -3. Angew Chem Int Ed Engl 58:17016–17025. https://doi.org/10.1002/anie.201909518. (PMID: 10.1002/anie.201909518314692216900016)
Yang W, Soares J, Greninger P et al (2013) Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 41:D955–D961. https://doi.org/10.1093/nar/gks1111. (PMID: 10.1093/nar/gks111123180760)
Hall M, Frank E, Holmes G et al (2009) The WEKA data mining software. ACM SIGKDD Explor Newsl 11:10–18. https://doi.org/10.1145/1656274.1656278. (PMID: 10.1145/1656274.1656278)
Rayan A, Raiyn J, Falah M (2017) Nature is the best source of anticancer drugs: indexing natural products for their anticancer bioactivity. PLoS ONE 12:1–12. https://doi.org/10.1371/journal.pone.0187925. (PMID: 10.1371/journal.pone.0187925)
Ertl P, Roggo S, Schuffenhauer A (2008) Natural product-likeness score and its application for prioritization of compound libraries. J Chem Inf Model 48:68–74. https://doi.org/10.1021/ci700286x. (PMID: 10.1021/ci700286x18034468)
Newman DJ, Cragg GM, Ahmed J et al (2022) The ChEMBL database in 2017. Nucleic Acids Res 2:1–21. https://doi.org/10.1093/nar/gkq917. (PMID: 10.1093/nar/gkq917)
Chen CY-C (2011) TCM Database@Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. PLoS ONE 6:e15939. https://doi.org/10.1371/journal.pone.0015939. (PMID: 10.1371/journal.pone.0015939212536033017089)
Morsy N (2019) Anticancer agents from plants. Main Gr Chem 18:169–191. https://doi.org/10.3233/MGC-180689. (PMID: 10.3233/MGC-180689)
Ntie-Kang F, Mbah JA, Mbaze LM et al (2013) CamMedNP: building the Cameroonian 3D structural natural products database for virtual screening. BMC Complem Altern Med 13:88. https://doi.org/10.1186/1472-6882-13-88. (PMID: 10.1186/1472-6882-13-88)
Potshangbam AM, Polavarapu R, Rathore RS et al (2019) MedPServer: a database for identification of therapeutic targets and novel leads pertaining to natural products. Chem Biol Drug Des 93:438–446. https://doi.org/10.1111/cbdd.13430. (PMID: 10.1111/cbdd.1343030381914)
Tung C-W, Lin Y-C, Chang H-S et al (2014) TIPdb-3D: the three-dimensional structure database of phytochemicals from Taiwan indigenous plants. Database (Oxford) 2014:bau055. https://doi.org/10.1093/database/bau055.
Kim S-K, Nam S, Jang H et al (2015) TM-MC: a database of medicinal materials and chemical compounds in Northeast Asian traditional medicine. BMC Complem Altern Med 15:218. https://doi.org/10.1186/s12906-015-0758-5. (PMID: 10.1186/s12906-015-0758-5)
Yabuzaki J (2017) Carotenoids database: structures, chemical fingerprints and distribution among organisms. Database (Oxford) 2017:bax004. https://doi.org/10.1093/database/bax004.
Miettinen K, Iñigo S, Kreft L et al (2018) The TriForC database: a comprehensive up-to-date resource of plant triterpene biosynthesis. Nucleic Acids Res 46:D586–D594. https://doi.org/10.1093/nar/gkx925. (PMID: 10.1093/nar/gkx92529045755)
Rothwell JA, Urpi-Sarda M, Boto-Ordoñez M, et al (2012) Phenol-Explorer 2.0: a major update of the Phenol-Explorer database integrating data on polyphenol metabolism and pharmacokinetics in humans and experimental animals. Database (Oxford) 2012:bas031. https://doi.org/10.1093/database/bas031.
Huang J, Wang J (2014) CEMTDD: Chinese ethnic minority traditional drug database. Apoptosis 19:1419–1420. https://doi.org/10.1007/s10495-014-1011-2. (PMID: 10.1007/s10495-014-1011-224952908)
Zeng X, Zhang P, Wang Y et al (2019) CMAUP: a database of collective molecular activities of useful plants. Nucleic Acids Res 47:D1118–D1127. https://doi.org/10.1093/nar/gky965. (PMID: 10.1093/nar/gky96530357356)
Xu H-Y, Zhang Y-Q, Liu Z-M et al (2019) ETCM: an encyclopaedia of traditional Chinese medicine. Nucleic Acids Res 47:D976–D982. https://doi.org/10.1093/nar/gky987. (PMID: 10.1093/nar/gky98730365030)
Li B, Ma C, Zhao X et al (2018) YaTCM: Yet another Traditional Chinese Medicine Database for Drug Discovery. Comput Struct Biotechnol J 16:600–610. https://doi.org/10.1016/j.csbj.2018.11.002. (PMID: 10.1016/j.csbj.2018.11.002305468606280608)
Xue R, Fang Z, Zhang M et al (2013) TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res 41:D1089–D1095. https://doi.org/10.1093/nar/gks1100. (PMID: 10.1093/nar/gks110023203875)
Boonen J, Bronselaer A, Nielandt J et al (2012) Alkamid database: chemistry, occurrence and functionality of plant N-alkylamides. J Ethnopharmacol 142:563–590. https://doi.org/10.1016/j.jep.2012.05.038. (PMID: 10.1016/j.jep.2012.05.03822659196)
Mohanraj K, Karthikeyan BS, Vivek-Ananth RP et al (2018) IMPPAT: a curated database of indian medicinal plants, phytochemistry and therapeutics. Sci Rep 8:1–17. https://doi.org/10.1038/s41598-018-22631-z. (PMID: 10.1038/s41598-018-22631-z)
معلومات مُعتمدة: 59/03/2019/Online/BMS/TRM Indian Council of Medical Research; 58/14/2020/PHA/BMS Indian Council of Medical Research
فهرسة مساهمة: Keywords: Anti-cancer; Bioinformatics; Drug discovery; Natural products; Phytomolecules
المشرفين على المادة: 0 (Phytochemicals)
0 (Antineoplastic Agents, Phytogenic)
0 (Antineoplastic Agents)
تواريخ الأحداث: Date Created: 20230120 Date Completed: 20240505 Latest Revision: 20240507
رمز التحديث: 20240508
مُعرف محوري في PubMed: PMC9859751
DOI: 10.1007/s11030-022-10590-7
PMID: 36670282
قاعدة البيانات: MEDLINE
الوصف
تدمد:1573-501X
DOI:10.1007/s11030-022-10590-7