Bioinformatics approaches to explore the discovery of new plant-derived drugs: a brief review

Autores

Ruana Carolina Cabral da Silva Universidade Federal de Ouro Preto, Ouro Preto-MG https://orcid.org/0000-0003-0435-1871
Maria Cidinaria Silva Alves Universidade Federal de Pernambuco - UFPE https://orcid.org/0000-0003-0625-416X
Dalton Ferreira Matos Universidade Federal do Rio Grande do Sul, Porto Alegre, Porto Alegre-RS https://orcid.org/0000-0002-6188-7536
Sayonara Alves Campos Quaresma Universidade Federal de Ouro Preto, Ouro Preto-MG https://orcid.org/0000-0001-6905-1584

Palavras-chave:

Computational analyses, Virtual screening, Molecular docking, Phytochemistry

Resumo

This article reviews the role of bioinformatics approaches in drug discovery from medicinal plants, with an emphasis on the use of computational tools for the analysis of genomic, proteomic, and metabolomic data. These techniques facilitate the screening of bioactive compounds with therapeutic potential, optimizing the identification of new drug candidates. Methods such as molecular docking and molecular modeling are fundamental for predicting interactions between ligands and target proteins, supporting the rational development of new drugs. The article also discusses the importance of metabolic pathway analyses and molecular interaction networks in the selection of promising plant species. The combination of bioinformatics and phytochemistry emerges as a crucial strategy to accelerate the drug discovery process, reducing both costs and development time, with great potential to enhance medical therapies.

Downloads

Não há dados estatísticos.

Referências

Agbor, A. M., & Naidoo, S. (2016). A review of the role of African traditional medicine in the management of oral diseases. African Journal of Traditional, Complementary and Alternative Medicines, 13(2), 133-142. https://doi.org/10.4314/ajtcam.v13i2.19

Aken, B. L., Achuthan, P., Akanni, W., Amode, M. R., Bernsdorff, F., Bhai, J., Billis, K., Carvalho-Silva, D., Cummins, C., Clapham, P., Gil, L., García, G. C., Gordon, L., Hourlier, T., Hunt, S. E., Janacek, S. H., Juettemann, T., Keenan, S., Laird, M. R., & Flicek, P. (2017). Ensembl 2017. Nucleic Acids Research, 45(D1), D635-D642. https://doi.org/10.1093/nar/gkw1104

Babar, M. M., Zaidi, N.-S.-S., Pothineni, V. R., Ali, Z., Faisal, S., Hakeem, K. R., & Gul, A. (2017). Application of bioinformatics and system biology in medicinal plant studies. In Plant Bioinformatics: Decoding the Phyta (pp. 375-393).

Bhachoo, J., & Beuming, T. (2017). Investigating protein–peptide interactions using the Schrödinger computational suite. In O. Schueler-Furman & N. London (Eds.), Modeling Peptide-Protein Interactions: Methods and Protocols (pp. 235-254). Springer. https://doi.org/10.1007/978-1-4939-6875-6_14

Basu, S., Duren, W., Evans, C. R., Burant, C. F., Michailidis, G., & Karnovsky, A. (2017). Sparse network modeling and MetScape-based visualization methods for the analysis of large-scale metabolomics data. Bioinformatics, 33(10), 1545-1553. https://doi.org/10.1093/bioinformatics/btw811

Bento, A. P., Hersey, A., Félix, E., Landrum, G., Gaulton, A., Atkinson, F., Bellis, L. J., De Veij, M., & Leach, A. R. (2020). An open-source chemical structure curation pipeline using RDKit. Journal of Cheminformatics, 12, 51. https://doi.org/10.1186/s13321-020-00456-1

Bitencourt-Ferreira, G., & Azevedo, W. F. (2019). Docking with SwissDock. In W. F. de Azevedo Jr. (Ed.), Docking Screens for Drug Discovery (pp. 189-202). Springer. https://doi.org/10.1007/978-1-4939-9121-1_12

Bohler, A., Wu, G., Kutmon, M., Pradhana, L. A., Coort, S. L., Hanspers, K., Haw, R., Pico, A. R., & Evelo, C. T. (2016). Reactome from a WikiPathways perspective. PLoS Computational Biology, 12(5), e1004941. https://doi.org/10.1371/journal.pcbi.1004941

Bouhaddani, S. el., Höllerhage, M., Uh, H.-W., Moebius, C., Bickle, M., Höglinger, G., & Houwing-Duistermaat, J. (2023). Statistical integration of multi-omics and drug screening data from cell lines. PLoS Comput Biol 20(1): e1011809. https://doi.org/10.1371/journal.pcbi.1011809

Cai, H., Chen, H., Yi, T., Daimon, C. M., Boyle, J. P., Peers, C., Maudsley, S., & Martin, B. (2013). VennPlex: A novel Venn diagram program for comparing and visualizing datasets with differentially regulated datapoints. PLOS ONE, 8(1), e53388. https://doi.org/10.1371/journal.pone.0053388

Chen, T., Yang, M., Cui, G., Tang, J., Shen, Y., Liu, J., Yuan, Y., Guo, J., & Huang, L. (2024). IMP: Bridging the gap for medicinal plant genomics. Nucleic Acids Research, 52(D1), D1347-D1354. https://doi.org/10.1093/nar/gkad120

Cominetti, O., Agarwal, S., & Oller-Moreno, S. (2023). Advances in methods and tools for multi-omics data analysis. Frontiers in Molecular Biosciences, 10. https://doi.org/10.3389/fmolb.2023.1234567

Cottret, L., Wildridge, D., Vinson, F., Barrett, M. P., Charles, H., Sagot, M.-F., & Jourdan, F. (2010). MetExplore: A web server to link metabolomic experiments and genome-scale metabolic networks. Nucleic Acids Research, 38(suppl_2), W132-W137. https://doi.org/10.1093/nar/gkq312

Davidson, R. L., Weber, R. J., Liu, H., Sharma-Oates, A., & Viant, M. R. (2016). Galaxy-M: A Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data. GigaScience, 5(1), s13742-016-0115-8. https://doi.org/10.1186/s13742-016-0115-8

Deschamps, E., Durand-Hulak, M., Castagnos, D., Hubert-Roux, M., Schmitz, I., Froelicher, Y., & Afonso, C. (2024). Metabolite variations during the first weeks of growth of immature Citrus sinensis and Citrus reticulata by untargeted liquid chromatography–mass spectrometry/mass spectrometry metabolomics. Molecules, 29(16), 3718. https://doi.org/10.3390/molecules29163718

Diniz, W. J. S., & Canduri, F. (2017). Bioinformatics: An overview and its applications. Genetics and Molecular Research, 16(1), 17. https://doi.org/10.4238/gmr16029645

Dorn, M., E Silva, M. B., Buriol, L. S., & Lamb, L. C. (2014). Three-dimensional protein structure prediction: Methods and computational strategies. Computational biology and chemistry, 53PB, 251–276. https://doi.org/10.1016/j.compbiolchem.2014.10.001

Du, X., Smirnov, A., Pluskal, T., Jia, W., & Sumner, S. (2020). Metabolomics Data Preprocessing Using ADAP and MZmine 2. Methods in molecular biology (Clifton, N.J.), 2104, 25–48. https://doi.org/10.1007/978-1-0716-0239-3_3

En-nahli, F., Baammi, S., Hajji, H., Alaqarbeh, M., Lakhlifi, T., & Bouachrine, M. (2023). High-throughput virtual screening approach of natural compounds as target inhibitors of plasmepsin-II. Journal of Biomolecular Structure and Dynamics, 41, 10070–10080. https://doi.org/10.1080/07391102.2022.2075364

Erlina, L., Paramita, R. I., Kusuma, W. A., Fadilah, F., Tedjo, A., Pratomo, I. P., Ramadhanti, N. S., Nasution, A. K., Surado, F. K., Fitriawan, A., Istiadi, K. A., & Yanuar, A. (2022). Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: Machine learning and pharmacophore modeling approaches. BMC Complementary Medicine and Therapies, 22, 207. https://doi.org/10.1186/s12906-022-03591-1

Fabricant, D. S., & Farnsworth, N. R. (2001). The value of plants used in traditional medicine for drug discovery. Environmental Health Perspectives, 109(1), 69–75. https://doi.org/10.1289/ehp.0110969

Firouzi, R., & Ashouri, M. (2023). Identification of potential anti-COVID-19 drug leads from medicinal plants through virtual high-throughput screening. ChemistrySelect, 8, e202203865. https://doi.org/10.1002/slct.202203865

Fitzgerald, M., Heinrich, M., & Booker, A. (2020). Medicinal plant analysis: A historical and regional discussion of emergent complex techniques. Frontiers in Pharmacology, 10, 1480. https://doi.org/10.3389/fphar.2019.01480

Gardinassi, L. G., Xia, J., Safo, S. E., & Li, S. (2017). Bioinformatics tools for the interpretation of metabolomics data. Current Pharmacology Reports, 3, 374-383. https://doi.org/10.1007/s40495-017-0094-2

Giacomoni, F., Le Corguillé, G., Monsoor, M., Landi, M., Pericard, P., Pétéra, M., Duperier, C., Tremblay-Franco, M., Martin, J. F., Jacob, D., Goulitquer, S., Thévenot, E. A., & Caron, C. (2015). Workflow4Metabolomics: A collaborative research infrastructure for computational metabolomics. Bioinformatics, 31(9), 1493–1495. https://doi.org/10.1093/bioinformatics/btu813

Gonulalan, E.-H., Nemutlu, E., & Demirezer, L.-O. (2019). A new perspective on evaluation of medicinal plant biological activities: The correlation between phytomics and matrix metalloproteinases activities of some medicinal plants. Saudi Pharmaceutical Journal, 27(3), 446-452. https://doi.org/10.1016/j.jsps.2019.01.003

Goodstein, D. M., Shu, S., Howson, R., Neupane, R., Hayes, R. D., Fazo, J., Mitros, T., Dirks, W., Hellsten, U., Putnam, N., & Rokhsar, D. S. (2012). Phytozome: A comparative platform for green plant genomics. Nucleic Acids Research, 40(D1), D1178-D1186. https://doi.org/10.1093/nar/gkr944

Gupta, R., Sharma, P., & Gupta, J. (2023). A review: In silico study and characterization bioactive compound by using LCMS techniques from plant extract. IJFMR - International Journal of Multidisciplinary Research, 5. https://doi.org/10.36948/ijfmr.2023.v05i02.1904

Halder, S. K., Sultana, I., Shuvo, M. N., Shil, A., Himel, M. K., Hasan, Md. A., & Shawan, M. M. A. K. (2023). In silico identification and analysis of potentially bioactive antiviral phytochemicals against SARS-CoV-2: A molecular docking and dynamics simulation approach. BioMed Research International, 2023, 5469258. https://doi.org/10.1155/2023/5469258

Hawkins, R. D., Hon, G. C., & Ren, B. (2010). Next-generation genomics: an integrative approach. Nature reviews. Genetics, 11(7), 476–486. https://doi.org/10.1038/nrg2795

Hussain, W., Rasool, N., & Khan, Y. D. (2021). Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD. Current drug discovery technologies, 18(4), 463–472. https://doi.org/10.2174/1570163817666200806165934

Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., & Coleman, R. G. (2012). ZINC: a free tool to discover chemistry for biology. Journal of chemical information and modeling, 52(7), 1757–1768. https://doi.org/10.1021/ci3001277

Iwaloye, O., Ottu, P.O., Olawale, F., Babalola, O.O., Elekofehinti, O.O., Kikiowo, B., Adegboyega, A.E., Ogbonna, H.N., Adeboboye, C.F., Folorunso, I.M., Fakayode, A.E., Akinjiyan, M.O., Onikanni, S.A., & Shityakov, S., (2023). Computer-aided drug design in anti-cancer drug discovery: What have we learnt and what is the way forward? Inform. Med. Unlocked 41, 101332.

Jiang, C., He, X., Wang, Y., Chen, C. J., Othman, Y., Hao, Y., Yuan, J., Xie, X. Q., & Feng, Z. (2023). Molecular Modeling Study of a Receptor-Orthosteric Ligand-Allosteric Modulator Signaling Complex. ACS chemical neuroscience, 14(3), 418–434. https://doi.org/10.1021/acschemneuro.2c00554

Jung S, Staton M, Lee T, Blenda A, Svancara R, Abbott A, & Main D (2007). GDR (Genome Database for Rosaceae): integrated web-database for Rosaceae genomics and genetics data. Nucleic acids research, v. 36, n. suppl_1, p. D1034-D1040.

Kamburov, A., Cavill, R., Ebbels, T. M. D., Herwig, R., & Keun, H. C. (2011). Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA. Bioinformatics, 27(20), 2917-2918.

https://doi.org/10.1093/bioinformatics/btr499

Kanehisa M. (2016). KEGG Bioinformatics Resource for Plant Genomics and Metabolomics. Methods in molecular biology (Clifton, N.J.), 1374, 55–70. https://doi.org/10.1007/978-1-4939-3167-5_3

Kapla, J., Rodríguez-Espigares, I., Ballante, F., Selent, J., & Carlsson, J. (2021). Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models? PLOS Computational Biology, 17(4), e1008936. https://doi.org/10.1371/journal.pcbi.1008936

Karp, P. D., Billington, R., Holland, T. A., Kothari, A., Krummenacker, M., Weaver, D., Latendresse, M., & Paley, S. (2015). Computational metabolomics operations at BioCyc.org. Metabolites, 5(2), 291-310. https://doi.org/10.3390/metabo5020291

Katiyar, C., Gupta, A., Kanjilal, S., & Katiyar, S. (2012). Drug discovery from plant sources: An integrated approach. Ayu, 33(1), 10. https://doi.org/10.4103/0974-8520.100295

Kerwin, S. M. (2012). Natural products research. Natural Products Chemistry & Research, 1(1), 1. https://doi.org/10.4172/npcr.1000101

Khan, D. A., Hamdani, S. D. A., Iftikhar, S., Malik, S. Z., Zaidi, N.-S. S., Gul, A., Babar, M. M., Ozturk, M., Turkyilmaz Unal, B., & Gonenc, T. (2022). Pharmacoinformatics approaches in the discovery of drug-like antimicrobials of plant origin. Journal of Biomolecular Structure & Dynamics, 40(16), 7612-7628. https://doi.org/10.1080/07391102.2022.2075127

Kotadiya, M. (2023). Drug Repurposing: Scopes in Herbal/Natural Products-based Drug Discovery and Role of in silico Techniques. IntechOpen. doi: 10.5772/intechopen.109821

Kumar, R., Cucchiarin, M., Jastrzębska, A. M., Caruso, G., Pernaa, J., & Minuchehr, Z. (2024). Cheminformatics, metabolomics, and stem cell tissue engineering: A transformative insight. In Computational Biology for Stem Cell Research (pp. 159-173). Academic Press. https://doi.org/10.1016/B978-0-12-824093-2.00010-9

Kwatra, B. K. (2021). WITHDRAWN: In silico prediction and comparison of resistomes in model Pseudomonas strains by Resistance Gene Identifier (RGI). bioRxiv. https://doi.org/10.1101/2021.11.15.468576

Li, H., Li, P. Y., & Yeung, P. (2013). A simple HPLC assay for ginsenoside-Rh2 in plasma and its application for pharmacokinetic study in rats. Natural Products Chemistry & Research, 1(103), 1-5. https://doi.org/10.4172/npcr.1000103

Lindahl, E., Hess, B., & van der Spoel, D. (2001). GROMACS 3.0: A package for molecular simulation and trajectory analysis. Journal of Molecular Modeling, 7(8), 306-317. https://doi.org/10.1007/s008940100045

López-López, E., Bajorath, J., & Medina-Franco, J. L. (2020). Informatics for chemistry, biology, and biomedical sciences. Journal of Chemical Information and Modeling, 61(1), 26-35. https://doi.org/10.1021/acs.jcim.0c01234

Ma, X., Wu, H., Liu, W., & Zhang, J. (2020). Bioinformatics-assisted, integrated omics studies on medicinal plants. Briefings in Bioinformatics, 21(6), 1857-1874. https://doi.org/10.1093/bib/bbaa122

Mahieu, N. G., Genenbacher, J. L., & Patti, G. J. (2016). A roadmap for the XCMS family of software solutions in metabolomics. Current Opinion in Chemical Biology, 30, 87-93. https://doi.org/10.1016/j.cbpa.2016.01.014

Mawalagedera, S. M. U. P., Tan, D. W. K., Toh, H. T., Ho, W. S., & Ng, S. H. (2019). Combining evolutionary inference and metabolomics to identify plants with medicinal potential. Frontiers in Ecology and Evolution, 7, 267. https://doi.org/10.3389/fevo.2019.00267

McCullagh, J., & Probert, F. (2024). New analytical methods focusing on polar metabolite analysis in mass spectrometry and NMR-based metabolomics. Current Opinion in Chemical Biology, 80, 102466. https://doi.org/10.1016/j.cbpa.2023.102466

Mensa, S., Sahin, E., Tacchino, F., Barkoutsos, P. K., & Tavernelli, I. (2023). Quantum machine learning framework for virtual screening in drug discovery: A prospective quantum advantage. Machine Learning: Science and Technology, 4(1), 015023. https://doi.org/10.1088/2632-2153/ac9d80

Michel, J., Abd Rani, N. Z., & Husain, K. (2020). A review on the potential use of medicinal plants from Asteraceae and Lamiaceae plant family in cardiovascular diseases. Frontiers in Pharmacology, 11, 852. https://doi.org/10.3389/fphar.2020.00852

Mukherjee, S., Stamatis, D., Bertsch, J., Ovchinnikova, G., Verezemska, O., Isbandi, M., Thomas, A. D., Ali, R., Sharma, K., Kyrpides, N. C., & Reddy, T. B. K. (2016). Genomes OnLine Database (GOLD) v. 6: Data updates and feature enhancements. Nucleic Acids Research. https://doi.org/10.1093/nar/gkw992

Naqvi, A. T., Zahoor, M., Ahmad, A., Ali, A., & Hussain, S. (2018). Advancements in docking and molecular dynamics simulations towards ligand-receptor interactions and structure-function relationships. Current Topics in Medicinal Chemistry, 18(20), 1755-1768. https://doi.org/10.2174/1568026618666180724121745

Nasim, N., Sandeep, I. S., & Mohanty, S. (2022). Plant-derived natural products for drug discovery: Current approaches and prospects. The Nucleus, 65(4), 399-411. https://doi.org/10.1007/s13237-022-00397-0

Nurisso, A., Bravo, J., Carrupt, P.-A., & Daina, A. (2012). Molecular docking using the molecular lipophilicity potential as hydrophobic descriptor: Impact on GOLD docking performance. Journal of Chemical Information and Modeling, 52(6), 1319-1327. https://doi.org/10.1021/ci200553j

Ogbe, R.J., Ochalefu, D.O., & Olaniru, O.B. (2016). Bioinformatics advances in genomics:A review. International Journal of Current Research and Review, 8(10), 5-15.

Oliveira, S. G. D., Piva, E., & Lund, R. G. (2015). The possibility of interactions between medicinal herbs and allopathic medicines used by patients attended at basic care units of the Brazilian unified health system. Natural Products Chemistry & Research, 3, 171. https://doi.org/10.4172/npcr.1000171

Parihar, A., Sonia, Z. F., Choudhary, N. K., Sharma, P., Mahdi, I., Hakim, F. T. H., Ali, M. A., Khan, R., Alqahtan, M. S., & Abbas, M. (2022). Identification of plant-based drug-like molecules as potential inhibitors against hACE2 and S-RBD of SARS-CoV-2 using multi-step molecular docking and dynamic simulation approach. Europe PMC. https://doi.org/10.21203/rs.3.rs-1517448/v1

Petrovska, B. B. (2012). Historical review of medicinal plants’ usage. Pharmacognosy Reviews, 6(11), 1–5. https://doi.org/10.4103/0973-7847.95849

Poole, R. L. (2007). The TAIR database. In Plant Bioinformatics: Methods and Protocols (pp. 179-212). Springer. https://doi.org/10.1007/978-1-59745-535-0_9

Raafat, K. M. (2013). Exploration of the protective effects of some natural compounds against neurodegeneration exploiting glycine receptors in vivo model. Natural Products Chemistry & Research, 1(3), 1–6. https://doi.org/10.4172/2329-6836.1000122

Rai, A., Saito, K., & Yamazaki, M. (2017). Integrated omics analysis of specialized metabolism in medicinal plants. The Plant Journal, 90(4), 764-787. https://doi.org/10.1111/tpj.13591

Rakshit, G., Komal, & Dagur, P. (2023). Multi-omics approaches in drug discovery. In M. Rudrapal & J. Khan (Eds.), CADD and informatics in drug discovery (pp. 79–98). Springer Nature, Singapore. https://doi.org/10.1007/978-981-19-6285-3_5

Rallabandi, H. R., Mekapogu, M., Natesan, K., Saindane, M., Dhupal, M., Swamy, M. K., & Vasamsetti, B. M. K. (2020). Computational methods used in phytocompound-based drug discovery. In M. K. Swamy (Ed.), Plant-derived bioactives: Chemistry and mode of action (pp. 549–573). Springer, Singapore. https://doi.org/10.1007/978-981-15-1761-7_24

Reich, M., Liefeld, T., Gould, J., Lerner, J., Tamayo, P., & Mesirov, J. P. (2006). GenePattern 2.0. Nature Genetics, 38(5), 500-501. https://doi.org/10.1038/ng0506-500

Repasky, M. P., Shelley, M., & Friesner, R. A. (2007). Flexible ligand docking with Glide. Current Protocols in Bioinformatics, 18(1), 8.12.1-8.12.36.

https://doi.org/10.1002/0471250953.bi0812s18

Rodosy, F. B., Azad, M. A. K., Halder, S. K., Limon, M. B. H., Jaman, S., Lata, N. A., Sarker, M., & Riya, A. I. (2024). The potential of phytochemicals against epidermal growth factor receptor tyrosine kinase (EGFRK): An insight from molecular dynamic simulations. Journal of Biomolecular Structure and Dynamics, 42(9), 2482–2493. https://doi.org/10.1080/07391102.2022.2105834

Romero, P. (2012). The HumanCyc pathway-genome database and pathway tools software as tools for imaging and analyzing metabolomics data. In The Handbook of Metabolomics (pp. 419-438). Springer. https://doi.org/10.1007/978-1-61779-618-0_21

Saito, K., & Matsuda, F. (2010). Metabolomics for functional genomics, systems biology, and biotechnology. Annual Review of Plant Biology, 61(1), 463-489. https://doi.org/10.1146/annurev-arplant-042809-112244

Santamaria, G., & Pinto, F. R. (2024). Bioinformatic analysis of metabolomic data: From raw spectra to biological insight. BioChem, 4(2), 90-114. https://doi.org/[DOI]

Satpathy, R. (2001). Application of bioinformatics techniques to screen and characterize the plant-based anti-cancer compounds [WWW document]. Httpsservicesigi-Glob. https://www.igi-global.com/gateway/chapter/www.igi-global.com/gateway/chapter/299817 (accessed 7.3.24).

Schmid, R., Petras, D., Nothias, L.-F., Wang, M., Aron, A. T., Jagels, A., Tsugawa, H., Rainer, J., Garcia-Aloy, M., Dührkop, K., Korf, A., Pluskal, T., et al. (2021). Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment. Nature Communications, 12(1), 3832. https://doi.org/10.1038/s41467-021-23861-w

Seitzer, P., Bennett, B., & Melamud, E. (2022). MAVEN2: An updated open-source mass spectrometry exploration platform. Metabolites, 12(8), 684. https://doi.org/10.3390/metabo12080684

Semenzato, G., Alonso-Vásquez, T., Del Duca, S., Vassallo, A., Riccardi, C., Zaccaroni, M., Mucci, N., Padula, A., Emiliani, G., Palumbo Piccionello, A., Puglia, A. M., & Fani, R. (2022). Genomic Analysis of Endophytic Bacillus-Related Strains Isolated from the Medicinal Plant Origanum vulgare L. Revealed the Presence of Metabolic Pathways Involved in the Biosynthesis of Bioactive Compounds. Microorganisms, 10(5), 919. https://doi.org/10.3390/microorganisms10050919

Sharma, B., & Yadav, D. K. (2022). Metabolomics and network pharmacology in the exploration of the multi-targeted therapeutic approach of traditional medicinal plants. Plants, 11, 3243. https://doi.org/10.3390/plants11233243

Sharma, V., & Sarkar, I. N. (2013). Bioinformatics opportunities for identification and study of medicinal plants. Briefings in Bioinformatics, 14(2), 238-250. https://doi.org/10.1093/bib/bbs017

Shrestha, P., Kim, M.-S., Elbasani, E., Kim, J.-D., & Oh, T.-J. (2022). Prediction of trehalose-metabolic pathway and comparative analysis of KEGG, MetaCyc, and RAST databases based on complete genome of Variovorax sp. PAMC28711. BMC Genomic Data, 23(1), 4. https://doi.org/10.1186/s12863-021-01025-0

Shreya, S., Shweta, D., Rakshit, G., & Ghosh, M. (2023). Virtual screening of phytochemicals for drug discovery. In C. Egbuna, M. Rudrapal, & H. Tijjani (Eds.), Phytochemistry, computational tools and databases in drug discovery (pp. 149–179). Elsevier. https://doi.org/10.1016/B978-0-12-823649-2.00007-8

Sultana, A., Noushin, F., Ali, M. L., Sultan, M. Z., Chowdhury, J. A., Chowdhury, A. A., Kabir, S., & Amran, M. S. (2023). Virtual Screening of a Series of Phytocompounds from Lagenaria Siceraria for the Identification of Potential Antidiabetic Drug Candidates, in Silico Study and Drug Design Approaches. Preprints. https://doi.org/10.20944/preprints202306.0038.v1

Süntar, I. (2020). Importance of ethnopharmacological studies in drug discovery: Role of medicinal plants. Phytochemistry Reviews, 19(5), 1199-1209. https://doi.org/10.1007/s11101-020-09690-z

Swainston, N., Smallbone, K., Hefzi, H., Dobson, P. D., Brewer, J., Hanscho, M., Zielinski, D. C., Ang, K. S., Gardiner, N. J., Gutierrez, J. M., Kyriakopoulos, S., Lakshmanan, M., Li, S., Liu, J. K., Martínez, V. S., Orellana, C. A., Quek, L.-E., Thomas, A., Zanghellini, J., Borth, N., Lee, D.-Y., Nielsen, L. K., Kell, D. B., Lewis, N. E., & Mendes, P. (2016). Recon 2.2: From reconstruction to model of human metabolism. Metabolomics, 12, 1-7. https://doi.org/10.1007/s11306-016-1051-4

Tolani, P., Joshi, B., Patil, V., & Ranjan, J. (2021). Big data, integrative omics and network biology. Advances in Protein Chemistry and Structural Biology, 127, 127-160. https://doi.org/10.1016/bs.apcsb.2021.03.003

Toole, A. A. (2012). The impact of public basic research on industrial innovation: Evidence from the pharmaceutical industry. Research Policy, 41(1), 1-12. https://doi.org/10.1016/j.respol.2011.06.004

Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334

Tsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., Kanazawa, M., VanderGheynst, J., Fiehn, O., & Arita, M. (2015). MS-DIAL: Data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nature Methods, 12(6), 523-526. https://doi.org/10.1038/nmeth.3393

Veeramohan, R., Zamani, A. Z., Aziza, K. A., Goh, H.-H., Aizat, W. M., Razak, M. F. A., Yusof, N. S. M., Mansor, S. M., Baharum, S. N., & Ng, C. L. (2023). Comparative metabolomics analysis reveals alkaloid repertoires in young and mature Mitragyna speciosa (Korth.) Havil. leaves. PLOS ONE, 18(3), e0283147. https://doi.org/10.1371/journal.pone.0283147

Verpoorte, R. (1998). Exploration of nature’s chemodiversity: The role of secondary metabolites as leads in drug development. Drug Discovery Today, 3(5), 232–238. https://doi.org/10.1016/S1359-6446(97)01154-7

Vilar, S., Cozza, G., & Moro, S. (2008). Medicinal chemistry and the molecular operating environment (MOE): Application of QSAR and molecular docking to drug discovery. Current Topics in Medicinal Chemistry, 8(18), 1555–1572.

https://doi.org/10.2174/156802608786786624

Vong, A. D.-Y., Hwang, S.-S., Chee, X. W., & Sim, E. U.-H. (2022). Computational ligand–receptor docking simulation of piperine with apoptosis-associated factors. Journal of Applied Biology & Biotechnology, 10(1), 38–44.

https://doi.org/10.7324/JABB.2022.100105

Vuorela, P., Leinonen, M., Saikku, P., Tammela, P., Rauha, J.-P., Wennberg, T., & Vuorela, H. (2004). Natural products in the process of finding new drug candidates. Current Medicinal Chemistry, 11(11), 1375-1389.

https://doi.org/10.2174/0929867043365107

Walker, A. S., & Clardy, J. (2021). A machine learning bioinformatics method to predict biological activity from biosynthetic gene clusters. Journal of Chemical Information and Modeling, 61(6), 2560-2571. https://doi.org/10.1021/acs.jcim.1c00214

Waman, V. P., Pujol, A., Fernández-Fuentes, N., & Oliva, B. (2021). The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies. Briefings in Bioinformatics, 22(2), 742-768.

https://doi.org/10.1093/bib/bbaa408

Wanichthanarak, K., In-on, A., Fan, S., Fiehn, O., Wangwiwatsin, A., & Khoomrung, S. (2024). Data processing solutions to render metabolomics more quantitative: Case studies in food and clinical metabolomics using Metabox 2.0. GigaScience, 13, giae005. https://doi.org/10.1093/gigascience/giae005

Willighagen, E. L., Waagmeester, A., Spjuth, O., Ansell, P., Williams, A. J., Tkachenko, V., Hastings, J., Chen, B., & Wild, D. J. (2013). The ChEMBL database as linked open data. Journal of Cheminformatics, 5, 23. https://doi.org/10.1186/1758-2946-5-23

Woldeyes, S., Adane, L., Tariku, Y., Muleta, D., & Begashaw, T. (2012). Evaluation of antibacterial activities of compounds isolated from Sida rhombifolia Linn. (Malvaceae). Natural Products Chemistry & Research, 1, 101.

https://doi.org/10.4172/2329-6836.1000101

Wu, Y., Liu, Q., & Xie, L. (2023). Hierarchical multi-omics data integration and modeling predict cell-specific chemical proteomics and drug responses. Cell Reports Methods, 3. https://doi.org/10.1016/j.crmeth.2023.100521

Yang, L., Li, S., Chen, Y., Chen, Y., Wang, M., Yu, J., Bai, W., & Hong, L. (2024). Combined metabolomics and network pharmacology analysis reveal the effect of rootstocks on anthocyanins, lipids, and potential pharmacological ingredients of Tarocco blood orange (Citrus sinensis L. Osbeck). Plants, 13(16), 2259.

https://doi.org/10.3390/plants13162259

Yuan, H., Ma, Q., Ye, L., & Piao, G. (2016). The traditional medicine and modern medicine from natural products. Molecules, 21(559), 1-18.

https://doi.org/10.3390/molecules21050559

Zhao, X., Ge, W., & Miao, Z. (2024). Integrative metabolomic and transcriptomic analyses reveal the accumulation patterns of key metabolites associated with flavonoids and terpenoids of Gynostemma pentaphyllum (Thunb.) Makino. Scientific Reports, 14(1), 8644. https://doi.org/10.1038/s41598-024-79825-4