Computational Intelligence Methods for Docking Scores

Authors: Hecht, David; Fogel, Gary B.

Source: Current Computer - Aided Drug Design, Volume 5, Number 1, March 2009 , pp. 56-68(13)

Publisher: Bentham Science Publishers

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Abstract:

Computer-aided drug design (CADD) methodologies have proven to be very effective, greatly enhancing the efficiency of small molecule drug discovery and development processes. These methods include quantitative structureactivity relationship and pharmacophore models, quantitative structure-property relationship models, as well as in silico docking studies. While docking studies very often correctly identify the binding mode of a ligand, they have reduced success in predicting binding affinities. Development of improved and more efficient strategies for scoring binding affinity is a very active area of research. Here we review the utility of computational intelligence approaches such as artificial neural networks, fuzzy logic, and evolutionary computation to the calculation of improved docking scores.

Keywords: Computational intelligence; evolutionary algorithms; artificial neural networks; fuzzy logic; docking scores; in silico docking; high-throughput screening; virtual screening

Document Type: Research article

DOI: http://dx.doi.org/10.2174/157340909787580863

Publication date: 2009-03-01

More about this publication?

Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design. Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, etc., providing excellent rationales for drug development.