In Silico Machine Learning Methods in Drug Development

Title:In Silico Machine Learning Methods in Drug Development

Volume: 14 Issue: 16

Author(s): Dimitar A. Dobchev, Girinath G. Pillai and Mati Karelson

Affiliation:

Keywords: Machine learning, support vector machine, genetic programming, artificial neural network, QSAR.

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.

Cite this article as:

Dobchev A. Dimitar, Pillai G. Girinath and Karelson Mati, In Silico Machine Learning Methods in Drug Development, Current Topics in Medicinal Chemistry 2014; 14 (16) . https://dx.doi.org/10.2174/1568026614666140929124203