In silico pharmacology for drug discovery: applications to targets and beyond

By Sean Ekins, Jordi Mestres, Bernard Testa. British Journal of Pharmacology, 2007

363 citations recorded by the source corpus.

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure‐activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target‐based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

Subfields: Computational Theory and Mathematics, Pharmacology

View Paper: https://www.semanticscholar.org/paper/ec024a8b9fd12921e71b8c384aba25365a623804

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Citations in the corpus

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling (2007, methodology): “Developing the optimal QSAR model for the chemical property or biological effect of interest, as well as defining its applicability domain within chemical space, is still very much an active area of investigation (Dimitrov et al ., 2005) as described later (Ekins et al ., 2007).”
Designing antimicrobial peptides: form follows function (2011, background): “Computerassisted peptide design and peptidemimetics design coupled with in silico pharmacology will undoubt edly benefit from these methodological advances 173,174 .”
I-TASSER: a unified platform for automated protein structure and function prediction (2010, background): “For example, high-resolution models with root mean square deviation (RMSD) values in the range of 1–2 A, typically generated by CM using close homologous templates, usually meet the highest structural requirements and are sometimes suitable for computational ligand-binding studies and virtual compou”
Protein Structure Prediction: Is It Useful? (2009, methodology): “Current Opinion in Structural Biology 2009, 19:145–155 examples in which computer-predicted models were used to guide the design of a new drug [9].”