In silico machine learning methods in drug development.

Abstract:

:Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, "noisy" and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. A variety of studies have demonstrated the potential of machine-learning methods for predicting compounds as potential drug candidates. The present review is intended to give an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. We also regard a number of applications of the machine learning algorithms based on common classes of diseases.

journal_name

Curr Top Med Chem

authors

Dobchev DA,Pillai GG,Karelson M

doi

10.2174/1568026614666140929124203

subject

Has Abstract

pub_date

2014-01-01 00:00:00

pages

1913-22

issue

16

eissn

1568-0266

issn

1873-4294

pii

CTMC-EPUB-62571

journal_volume

14

pub_type

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