Virtual screening stategies in drug discovery – A brief overview

Author affiliations

Authors

  • Pham Quoc Long Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Cau Giay, Ha Noi, VietNam https://orcid.org/0000-0001-6964-2341
  • Pham Minh Quan Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Cau Giay, Ha Noi, VietNam https://orcid.org/0000-0001-6922-1627

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.

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 Molecular Biology, 1997. 267(3): p. 727-748.

Ewing, T.J.A., et al., DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases. Journal of Computer-Aided Molecular Design, 2001. 15(5): p. 411-428.

Friesner, R.A., et al., Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. Journal of Medicinal Chemistry, 2004. 47(7): p. 1739-1749.

Halgren, T.A., et al., Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening. Journal of Medicinal Chemistry, 2004. 47(7): p. 1750-1759.

Kramer, B., M. Rarey, and T. Lengauer, Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins: Structure, Function, and Genetics, 1999. 37(2): p. 228-241.

Buzko, O.V., A.C. Bishop, and K.M. Shokat, Modified AutoDock for accurate docking of protein kinase inhibitors. Journal of Computer-Aided Molecular Design, 2002. 16(2): p. 113-127.

Morris, G.M., et al., Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry, 1998. 19(14): p. 1639-1662.

Warren, G.L., et al., A Critical Assessment of Docking Programs and Scoring Functions. Journal of Medicinal Chemistry, 2006. 49(20): p. 5912-5931.

Verdonk, M.L., et al., Improved protein-ligand docking using GOLD. Proteins: Structure, Function, and Bioinformatics, 2003. 52(4): p. 609-623.

Grosdidier, A., V. Zoete, and O. Michielin, EADock: Docking of small molecules into protein active sites with a multiobjective evolutionary optimization. Proteins: Structure, Function, and Bioinformatics, 2007. 67(4): p. 1010-1025.

Jain, A.N., Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. Journal of Computer-Aided Molecular Design, 2007. 21(5): p. 281-306.

Abagyan, R., M. Totrov, and D. Kuznetsov, ICM-A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. Journal of Computational Chemistry, 1994. 15(5): p. 488-506.

Bohm, H.-J., The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. Journal of Computer-Aided Molecular Design, 1994. 8(3): p. 243-256.

Ravitz, O., Z. Zsoldos, and A. Simon, Improving molecular docking through eHiTS’ tunable scoring function. Journal of Computer-Aided Molecular Design, 2011. 25(11): p. 1033-1051.

Zavodszky, M.I., et al., Scoring ligand similarity in structure-based virtual screening. Journal of Molecular Recognition, 2009. 22(4): p. 280-292.

Gray, J.J., et al., Protein–Protein Docking with Simultaneous Optimization of Rigid-body Displacement and Side-chain Conformations. Journal of Molecular Biology, 2003. 331(1): p. 281-299.

Thomsen, R. and M.H. Christensen, MolDock: A New Technique for High-Accuracy Molecular Docking. Journal of Medicinal Chemistry, 2006. 49(11): p. 3315-3321.

McGann, M., FRED and HYBRID docking performance on standardized datasets. Journal of Computer-Aided Molecular Design, 2012. 26(8): p. 897-906.

Chen, R., L. Li, and Z. Weng, ZDOCK: An initial-stage protein-docking algorithm. Proteins: Structure, Function, and Genetics, 2003. 52(1): p. 80-87.

Kitchen, D.B., et al., Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Reviews Drug Discovery, 2004. 3(11): p. 935-949.

Brooijmans, N. and I.D. Kuntz, Molecular Recognition and Docking Algorithms. Annual Review of Biophysics and Biomolecular Structure, 2003. 32(1): p. 335-373.

Moitessier, N., et al., Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. British Journal of Pharmacology, 2008. 153(S1): p. S7-S26.

Huang, S.-Y., S.Z. Grinter, and X. Zou, Scoring functions and their evaluation methods for protein–ligand docking: recent advances and future directions. Physical Chemistry Chemical Physics, 2010. 12(40).

Krovat, E., T. Steindl, and T. Langer, Recent Advances in Docking and Scoring. Current Computer Aided-Drug Design, 2005. 1(1): p. 93-102.

Bentham Science Publisher, B.S.P., Scoring Functions for Protein-Ligand Docking. Current Protein & Peptide Science, 2006. 7(5): p. 407-420.

Meng, E.C., B.K. Shoichet, and I.D. Kuntz, Automated docking with grid-based energy evaluation. Journal of Computational Chemistry, 1992. 13(4): p. 505-524.

Goodsell, D.S. and A.J. Olson, Automated docking of substrates to proteins by simulated annealing. Proteins: Structure, Function, and Genetics, 1990. 8(3): p. 195-202.

Shoichet, B.K., A.R. Leach, and I.D. Kuntz, Ligand solvation in molecular docking. Proteins: Structure, Function, and Genetics, 1999. 34(1): p. 4-16.

Zou, X., Yaxiong, and I.D. Kuntz, Inclusion of Solvation in Ligand Binding Free Energy Calculations Using the Generalized-Born Model. Journal of the American Chemical Society, 1999. 121(35): p. 8033-8043.

Wang, J., et al., Use of MM-PBSA in Reproducing the Binding Free Energies to HIV-1 RT of TIBO Derivatives and Predicting the Binding Mode to HIV-1 RT of Efavirenz by Docking and MM-PBSA. Journal of the American Chemical Society, 2001. 123(22): p. 5221-5230.

Weng, Z., S. Vajda, and C. Delisi, Prediction of protein complexes using empirical free energy functions. Protein Science, 1996. 5(4): p. 614-626.

Schulz-Gasch, T. and M. Stahl, Scoring functions for protein–ligand interactions: a critical perspective. Drug Discovery Today: Technologies, 2004. 1(3): p. 231-239.

Kulharia, M., R.S. Goody, and R.M. Jackson, Information Theory-Based Scoring Function for the Structure-Based Prediction of Protein−Ligand Binding Affinity. Journal of Chemical Information and Modeling, 2008. 48(10): p. 1990-1998.

Huang, S.-Y. and X. Zou, Inclusion of Solvation and Entropy in the Knowledge-Based Scoring Function for Protein−Ligand Interactions. Journal of Chemical Information and Modeling, 2010. 50(2): p. 262-273.

Leach, A.R., B.K. Shoichet, and C.E. Peishoff, Prediction of Protein−Ligand Interactions. Docking and Scoring: Successes and Gaps. Journal of Medicinal Chemistry, 2006. 49(20): p. 5851-5855.

O’Boyle, N.M., J.W. Liebeschuetz, and J.C. Cole, Testing Assumptions and Hypotheses for Rescoring Success in Protein−Ligand Docking. Journal of Chemical Information and Modeling, 2009. 49(8): p. 1871-1878.

Raub, S., et al., AIScore — Chemically Diverse Empirical Scoring Function Employing Quantum Chemical Binding Energies of Hydrogen-Bonded Complexes. Journal of Chemical Information and Modeling, 2008. 48(7): p. 1492-1510.

Seifert, M.H.J., Robust optimization of scoring functions for a target class. Journal of Computer-Aided Molecular Design, 2009. 23(9): p. 633-644.

Charifson, P.S., et al., Consensus Scoring: A Method for Obtaining Improved Hit Rates from Docking Databases of Three-Dimensional Structures into Proteins. Journal of Medicinal Chemistry, 1999. 42(25): p. 5100-5109.

Feher, M., Consensus scoring for protein–ligand interactions. Drug Discovery Today, 2006. 11(9-10): p. 421-428.

Evers, A., et al., Virtual Screening of Biogenic Amine-Binding G-Protein Coupled Receptors: Comparative Evaluation of Protein- and Ligand-Based Virtual Screening Protocols. Journal of Medicinal Chemistry, 2005. 48(17): p. 5448-5465.

Jiang, F. and S.-H. Kim, “Soft docking”: Matching of molecular surface cubes. Journal of Molecular Biology, 1991. 219(1): p. 79-102.

Perez, A., et al., FlexE: Using Elastic Network Models to Compare Models of Protein Structure. Journal of Chemical Theory and Computation, 2012. 8(10): p. 3985-3991.

Sherman, W., et al., Novel Procedure for Modeling Ligand/Receptor Induced Fit Effects. Journal of Medicinal Chemistry, 2006. 49(2): p. 534-553.

Lin, J.-H., et al., The relaxed complex method: Accommodating receptor flexibility for drug design with an improved scoring scheme. Biopolymers, 2003. 68(1): p. 47-62.

Amaro, R.E., R. Baron, and J.A. McCammon, An improved relaxed complex scheme for receptor flexibility in computer-aided drug design. Journal of Computer-Aided Molecular Design, 2008. 22(9): p. 693-705.

Case, D.A., et al., The Amber biomolecular simulation programs. Journal of Computational Chemistry, 2005. 26(16): p. 1668-1688.

Phillips, J.C., et al., Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 2005. 26(16): p. 1781-1802.

Van Der Spoel, D., et al., GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, 2005. 26(16): p. 1701-1718.

Dudek, A., T. Arodz, and J. Galvez, Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review. Combinatorial Chemistry & High Throughput Screening, 2006. 9(3): p. 213-228.

Zou, J., et al., Towards more accurate pharmacophore modeling: Multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2. Journal of Molecular Graphics and Modelling, 2008. 27(4): p. 430-438.

Verma, J., V. Khedkar, and E. Coutinho, 3D-QSAR in Drug Design - A Review. Current Topics in Medicinal Chemistry, 2010. 10(1): p. 95-115.

Lavecchia, A. and C. Giovanni, Virtual Screening Strategies in Drug Discovery: A Critical Review. Current Medicinal Chemistry, 2013. 20(23): p. 2839-2860.

Dobson, C.M., Chemical space and biology. Nature, 2004. 432(7019): p. 824-828.

Bohacek, R.S., C. McMartin, and W.C. Guida, The art and practice of structure-based drug design: A molecular modeling perspective. Medicinal Research Reviews, 1996. 16(1): p. 3-50.

Ertl, P., Cheminformatics Analysis of Organic Substituents: Identification of the Most Common Substituents, Calculation of Substituent Properties, and Automatic Identification of Drug-like Bioisosteric Groups. Journal of Chemical Information and Computer Sciences, 2002. 43(2): p. 374-380.

Selzer, P., et al., Complex molecules: do they add value? Current Opinion in Chemical Biology, 2005. 9(3): p. 310-316.

Feher, M. and J.M. Schmidt, Property Distributions: Differences between Drugs, Natural Products, and Molecules from Combinatorial Chemistry. Journal of Chemical Information and Computer Sciences, 2002. 43(1): p. 218-227.

Hann, M.M. and T.I. Oprea, Pursuing the leadlikeness concept in pharmaceutical research. Current Opinion in Chemical Biology, 2004. 8(3): p. 255-263.

Lipinski, C.A., Drug-like properties and the causes of poor solubility and poor permeability. Journal of Pharmacological and Toxicological Methods, 2000. 44(1): p. 235-249.

Ridder, L., et al., Revisiting the Rule of Five on the Basis of Pharmacokinetic Data from Rat. ChemMedChem, 2011. 6(11): p. 1967-1970.

Congreve, M., et al., A ‘Rule of Three’ for fragment-based lead discovery? Drug Discovery Today, 2003. 8(19): p. 876-877.

Hughes, J.D., et al., Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorganic & Medicinal Chemistry Letters, 2008. 18(17): p. 4872-4875.

Axerio-Cilies, P., et al., Investigation of the incidence of “undesirable” molecular moieties for high-throughput screening compound libraries in marketed drug compounds. European Journal of Medicinal Chemistry, 2009. 44(3): p. 1128-1134.

Benigni, R. and C. Bossa, Mechanisms of Chemical Carcinogenicity and Mutagenicity: A Review with Implications for Predictive Toxicology. Chemical Reviews, 2011. 111(4): p. 2507-2536.

Enoch, S.J., et al., A review of the electrophilic reaction chemistry involved in covalent protein binding relevant to toxicity. Critical Reviews in Toxicology, 2011. 41(9): p. 783-802.

Erve, J.C.L., Chemical toxicology: reactive intermediates and their role in pharmacology and toxicology. Expert Opinion on Drug Metabolism & Toxicology, 2006. 2(6): p. 923-946.

Kazius, J., R. McGuire, and R. Bursi, Derivation and Validation of Toxicophores for Mutagenicity Prediction. Journal of Medicinal Chemistry, 2005. 48(1): p. 312-320.

Rishton, G.M., Nonleadlikeness and leadlikeness in biochemical screening. Drug Discovery Today, 2003. 8(2): p. 86-96.

Irwin, J.J. and B.K. Shoichet, ZINC--a free database of commercially available compounds for virtual screening. J Chem Inf Model, 2005. 45(1): p. 177-82.

Chen, J.H., et al., ChemDB update full-text search and virtual chemical space. Bioinformatics, 2007. 23(17): p. 2348-2351.

Seiler, K.P., et al., ChemBank: a small-molecule screening and cheminformatics resource database. Nucleic Acids Research, 2007. 36(Database): p. D351-D359.

Wishart, D.S., DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Research, 2006. 34(90001): p. D668-D672.

Knox, C., et al., DrugBank 3.0: a comprehensive resource for 'Omics' research on drugs. Nucleic Acids Research, 2010. 39(Database): p. D1035-D1041.

Gaulton, A., et al., ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Research, 2011. 40(D1): p. D1100-D1107.

Olah, M., et al., WOMBAT: World of Molecular Bioactivity, in Chemoinformatics in Drug Discovery. 2005. p. 221-239.

Del Rio, A., et al., CoCoCo: a free suite of multiconformational chemical databases for high-throughput virtual screening purposes. Molecular BioSystems, 2010. 6(11).

Amézqueta, S., et al., Octanol-Water Partition Constant, in Liquid-Phase Extraction. 2020. p. 183-208.

Le Fèvre, R.J.W., Molecular Refractivity and Polarizability. 1965. p. 1-90.

Price, D.A., et al., Physicochemical drug properties associated within vivotoxicological outcomes: a review. Expert Opinion on Drug Metabolism & Toxicology, 2009. 5(8): p. 921-931.

Wenlock, M.C., Designing safer oral drugs. MedChemComm, 2017. 8(3): p. 571-577.

Ekins, S. and A.J. Williams, Precompetitive preclinical ADME/Tox data: set it free on the web to facilitate computational model building and assist drug development. Lab Chip, 2010. 10(1): p. 13-22.

Ertl, P. and S. Jelfs, Designing Drugs on the Internet? Free Web Tools and Services Supporting Medicinal Chemistry. Current Topics in Medicinal Chemistry, 2007. 7(15): p. 1491-1501.

Richard, A.M., L.S. Gold, and M.C. Nicklaus, Chemical structure indexing of toxicity data on the internet: moving toward a flat world. Curr Opin Drug Discov Devel, 2006. 9(3): p. 314-25.

Szakács, G., et al., The role of ABC transporters in drug absorption, distribution, metabolism, excretion and toxicity (ADME–Tox). Drug Discovery Today, 2008. 13(9-10): p. 379-393.

Tsaioun, K., Evidence-based absorption, distribution, metabolism, excretion (ADME) and its interplay with alternative toxicity methods. Altex, 2016: p. 343-358.

Li, A.P., Screening for human ADME/Tox drug properties in drug discovery. Drug Discovery Today, 2001. 6(7): p. 357-366.

Guengerich, F.P., Cytochrome P450s and other enzymes in drug metabolism and toxicity. The AAPS Journal, 2006. 8(1): p. E101-E111.

Zanger, U.M. and M. Schwab, Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacology & Therapeutics, 2013. 138(1): p. 103-141.

Caldwell, J., I. Gardner, and N. Swales, An Introduction to Drug Disposition: The Basic Principles of Absorption, Distribution, Metabolism, and Excretion. Toxicologic Pathology, 2016. 23(2): p. 102-114.

Downloads

Published

13-08-2021

How to Cite

[1]
P. Q. Long and P. M. Quan, “Virtual screening stategies in drug discovery – A brief overview”, Vietnam J. Sci. Technol., vol. 59, no. 4, pp. 415–440, Aug. 2021.

Issue

Section

Natural Products

Most read articles by the same author(s)

<< < 1 2