Computational Methods in Drug Discovery

Overview

Journal Beilstein J Org Chem

Specialty Chemistry

Date 2017 Feb 2

PMID 28144341

Citations 157

Authors

Sumudu P Leelananda

Steffen Lindert

Affiliations

Soon will be listed here.

Abstract

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

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References

Michel J, Foloppe N, Essex J . Rigorous Free Energy Calculations in Structure-Based Drug Design. Mol Inform. 2016; 29(8-9):570-8. DOI: 10.1002/minf.201000051. View

Kim S, Thiessen P, Bolton E, Chen J, Fu G, Gindulyte A . PubChem Substance and Compound databases. Nucleic Acids Res. 2015; 44(D1):D1202-13. PMC: 4702940. DOI: 10.1093/nar/gkv951. View

Koga H, Itoh A, Murayama S, SUZUE S, IRIKURA T . Structure-activity relationships of antibacterial 6,7- and 7,8-disubstituted 1-alkyl-1,4-dihydro-4-oxoquinoline-3-carboxylic acids. J Med Chem. 1980; 23(12):1358-63. DOI: 10.1021/jm00186a014. View

Lindert S, Bucher D, Eastman P, Pande V, McCammon J . Accelerated Molecular Dynamics Simulations with the AMOEBA Polarizable Force Field on Graphics Processing Units. J Chem Theory Comput. 2014; 9(11):4684-4691. PMC: 3948463. DOI: 10.1021/ct400514p. View

Cross J, Thompson D, Rai B, Baber J, Fan K, Hu Y . Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J Chem Inf Model. 2009; 49(6):1455-74. DOI: 10.1021/ci900056c. View

Sturzebecher J, Prasa D, Hauptmann J, Vieweg H, Wikstrom P . Synthesis and structure-activity relationships of potent thrombin inhibitors: piperazides of 3-amidinophenylalanine. J Med Chem. 1997; 40(19):3091-9. DOI: 10.1021/jm960668h. View

Gupta S, Kumaran S . A quantitative structure-activity relationship study on some aromatic/heterocyclic sulfonamides and their charged derivatives acting as carbonic anhydrase inhibitors. J Enzyme Inhib Med Chem. 2005; 20(3):251-9. DOI: 10.1080/14756360500067439. View

Anderson A . The process of structure-based drug design. Chem Biol. 2003; 10(9):787-97. DOI: 10.1016/j.chembiol.2003.09.002. View

Byvatov E, Fechner U, Sadowski J, Schneider G . Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J Chem Inf Comput Sci. 2003; 43(6):1882-9. DOI: 10.1021/ci0341161. View

10.

Amaro R, Baron R, McCammon J . An improved relaxed complex scheme for receptor flexibility in computer-aided drug design. J Comput Aided Mol Des. 2008; 22(9):693-705. PMC: 2516539. DOI: 10.1007/s10822-007-9159-2. View

11.

Lindert S, Durrant J, McCammon J . LigMerge: a fast algorithm to generate models of novel potential ligands from sets of known binders. Chem Biol Drug Des. 2012; 80(3):358-65. PMC: 3462068. DOI: 10.1111/j.1747-0285.2012.01414.x. View

12.

Jorgensen W . Efficient drug lead discovery and optimization. Acc Chem Res. 2009; 42(6):724-33. PMC: 2727934. DOI: 10.1021/ar800236t. View

13.

Glaab E . Building a virtual ligand screening pipeline using free software: a survey. Brief Bioinform. 2015; 17(2):352-66. PMC: 4793892. DOI: 10.1093/bib/bbv037. View

14.

Halgren T . Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model. 2009; 49(2):377-89. DOI: 10.1021/ci800324m. View

15.

Yan R, Si J, Wang C, Zhang Z . DescFold: a web server for protein fold recognition. BMC Bioinformatics. 2009; 10:416. PMC: 2803855. DOI: 10.1186/1471-2105-10-416. View

16.

Sova M, Cadez G, Turk S, Majce V, Polanc S, Batson S . Design and synthesis of new hydroxyethylamines as inhibitors of D-alanyl-D-lactate ligase (VanA) and D-alanyl-D-alanine ligase (DdlB). Bioorg Med Chem Lett. 2009; 19(5):1376-9. DOI: 10.1016/j.bmcl.2009.01.034. View

17.

Jones D . GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences. J Mol Biol. 1999; 287(4):797-815. DOI: 10.1006/jmbi.1999.2583. View

18.

Mueller R, Rodriguez A, Dawson E, Butkiewicz M, Nguyen T, Oleszkiewicz S . Identification of Metabotropic Glutamate Receptor Subtype 5 Potentiators Using Virtual High-Throughput Screening. ACS Chem Neurosci. 2010; 1(4):288-305. PMC: 2857954. DOI: 10.1021/cn9000389. View

19.

Hirst S, Alexander N, Mchaourab H, Meiler J . RosettaEPR: an integrated tool for protein structure determination from sparse EPR data. J Struct Biol. 2010; 173(3):506-14. PMC: 3040274. DOI: 10.1016/j.jsb.2010.10.013. View

20.

Bowie J, Luthy R, Eisenberg D . A method to identify protein sequences that fold into a known three-dimensional structure. Science. 1991; 253(5016):164-70. DOI: 10.1126/science.1853201. View