VIRTUAL SCREENING STATEGIES IN DRUG DISCOVERY – A BRIEF OVERVIEW

Authors

DOI:

https://doi.org/10.15625/2525-2518/59/4/16003

Keywords:

virtual screening, drug discovery, molecular docking, drug-likeness, ADME

Abstract

Computer-aided drug design has now become a compulsory tool in the drug discovery and development process which uses computational approaches to discover potential compounds with expected biological activities. Firstly, this review provides a comprehensive introduction of the virtual screening technique, knowledge and advances in both SBVS and LBVS strategies also presented. Secondly, recent database of compounds provided worldwide and drug-like parameters which are helpful in supporting the VS process will be discussed. These information will provides a good platform to estimate the advance of applying these techniques in the new drug-lead identification and optimization.

Downloads

Download data is not yet available.

Author Biographies

Quan Minh PHAM, Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology

Biochemistry

Long Quoc PHAM, Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology

Biochemistry

References

Do Tat, L. and D. Nguyen Xuan, Native drugs of Vietnam: which traditional and scientific approaches? Journal of Ethnopharmacology, 1991. 32(1-3): p. 51-56.

Dias, D.A., S. Urban, and U. Roessner, A Historical Overview of Natural Products in Drug Discovery. Metabolites, 2012. 2(2): p. 303-336.

Newman, D.J., G.M. Cragg, and K.M. Snader, Natural Products as Sources of New Drugs over the Period 1981−2002. Journal of Natural Products, 2003. 66(7): p. 1022-1037.

Pan, S.-Y., et al., New Perspectives on How to Discover Drugs from Herbal Medicines: CAM's Outstanding Contribution to Modern Therapeutics. Evidence-Based Complementary and Alternative Medicine, 2013. 2013: p. 1-25.

Sliwoski, G., et al., Computational Methods in Drug Discovery. Pharmacological Reviews, 2013. 66(1): p. 334-395.

Réda, C., E. Kaufmann, and A. Delahaye-Duriez, Machine learning applications in drug development. Computational and Structural Biotechnology Journal, 2020. 18: p. 241-252.

Ooms, F., Molecular Modeling and Computer Aided Drug Design. Examples of their Applications in Medicinal Chemistry. Current Medicinal Chemistry, 2000. 7(2): p. 141-158.

Gasteiger, J., Chemoinformatics: a new field with a long tradition. Analytical and Bioanalytical Chemistry, 2005. 384(1): p. 57-64.

Downs, G.M. and J.M. Barnard, Clustering Methods and Their Uses in Computational Chemistry, in Reviews in Computational Chemistry, Volume 18. 2002. p. 1-40.

Song, C.M., S.J. Lim, and J.C. Tong, Recent advances in computer-aided drug design. Briefings in Bioinformatics, 2009. 10(5): p. 579-591.

Yu, W. and A.D. MacKerell, Computer-Aided Drug Design Methods, in Antibiotics. 2017. p. 85-106.

Singh, B.K. and S. Surabhi, Computer Aided Drug Design: An Overview. Journal of Drug Delivery and Therapeutics, 2018. 8(5): p. 504-509.

da Silva Rocha, S.F.L., et al., Virtual Screening Techniques in Drug Discovery: Review and Recent Applications. Current Topics in Medicinal Chemistry, 2019. 19(19): p. 1751-1767.

Slater, O. and M. Kontoyianni, The compromise of virtual screening and its impact on drug discovery. Expert Opinion on Drug Discovery, 2019. 14(7): p. 619-637.

Kontoyianni, M., Docking and Virtual Screening in Drug Discovery, in Proteomics for Drug Discovery. 2017. p. 255-266.

Paul, S.M., et al., How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature Reviews Drug Discovery, 2010. 9(3): p. 203-214.

Kale, V.P., et al., Old drugs, new uses: Drug repurposing in hematological malignancies. Seminars in Cancer Biology, 2021. 68: p. 242-248.

Tamimi, N.A.M. and P. Ellis, Drug Development: From Concept to Marketing! Nephron Clinical Practice, 2009. 113(3): p. c125-c131.

Moridani, M. and S. Harirforoosh, Drug development and discovery: challenges and opportunities. Drug Discovery Today, 2014. 19(11): p. 1679-1681.

Jacq, N., et al., Grid-enabled Virtual Screening Against Malaria. Journal of Grid Computing, 2007. 6(1): p. 29-43.

Kasam, V., et al., WISDOM-II: Screening against multiple targets implicated in malaria using computational grid infrastructures. Malaria Journal, 2009. 8(1).

de Beer, T.A.P., et al., Antimalarial Drug Discovery: In Silico Structural Biology and Rational Drug Design. Infectious Disorders - Drug Targets, 2009. 9(3): p. 304-318.

Ton, A.T., et al., Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds. Molecular Informatics, 2020. 39(8).

Rutwick Surya, U. and N. Praveen, A molecular docking study of SARS-CoV-2 main protease against phytochemicals of Boerhavia diffusa Linn. for novel COVID-19 drug discovery. VirusDisease, 2021.

Soumia, M., et al., Towards potential inhibitors of COVID-19 main protease Mpro by virtual screening and molecular docking study. Journal of Taibah University for Science, 2020. 14(1): p. 1626-1636.

Talluri, S., Molecular Docking and Virtual Screening based prediction of drugs for COVID-19. Combinatorial Chemistry & High Throughput Screening, 2020. 23.

Keretsu, S., S.P. Bhujbal, and S.J. Cho, Rational approach toward COVID-19 main protease inhibitors via molecular docking, molecular dynamics simulation and free energy calculation. Scientific Reports, 2020. 10(1).

Ngo, S.T., et al., Assessing potential inhibitors of SARS-CoV-2 main protease from available drugs using free energy perturbation simulations. RSC Advances, 2020. 10(66): p. 40284-40290.

Pham, M.Q., et al., Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations. RSC Advances, 2020. 10(53): p. 31991-31996.

Pranav Kumar, S.K. and V.M. Kulkarni, Molecular dynamics simulations of the three dimensional model of plasmepsin II-peptidic inhibitor complexes. Drug Des Discov, 2001. 17(4): p. 293-313.

Dong, G.Q., et al., Prediction of Substrates for Glutathione Transferases by Covalent Docking. Journal of Chemical Information and Modeling, 2014. 54(6): p. 1687-1699.

Choowongkomon, K., et al., Computational analysis of binding between malarial dihydrofolate reductases and anti-folates. Malaria Journal, 2010. 9(1).

Liu, Z., et al., Molecular Docking of Potential Inhibitors for Influenza H7N9. Computational and Mathematical Methods in Medicine, 2015. 2015: p. 1-8.

Chenafa, H., et al., In silico design of enzyme α-amylase and α-glucosidase inhibitors using molecular docking, molecular dynamic, conceptual DFT investigation and pharmacophore modelling. Journal of Biomolecular Structure and Dynamics, 2021: p. 1-22.

Basu, A., A. Sarkar, and U. Maulik, Molecular docking study of potential phytochemicals and their effects on the complex of SARS-CoV2 spike protein and human ACE2. Scientific Reports, 2020. 10(1).

Du, X., et al., Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods. International Journal of Molecular Sciences, 2016. 17(2).

Muegge, I. and S. Oloff, Advances in virtual screening. Drug Discovery Today: Technologies, 2006. 3(4): p. 405-411.

Reddy, A.S., et al., Virtual Screening in Drug Discovery - A Computational Perspective. Current Protein & Peptide Science, 2007. 8(4): p. 329-351.

Kuntz, I.D., et al., A geometric approach to macromolecule-ligand interactions. Journal of Molecular Biology, 1982. 161(2): p. 269-288.

Jones, G., et al., Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen. Journal of Molecula