Machine Learning in Virtual Screening

Title: Machine Learning in Virtual Screening

Volume: 12 Issue: 4

Author(s): James L. Melville, Edmund K. Burke and Jonathan D. Hirst

Affiliation:

Keywords: Machine learning, virtual screening, data mining, drug discovery

Abstract: In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naïve Bayesian classifiers, support vector machines, neural networks, and decision trees, as well as more traditional regression techniques. Effective application of these methodologies requires an appreciation of data preparation, validation, optimization, and search methodologies, and we also survey developments in these areas.

Cite this article as:

Melville L. James, Burke K. Edmund and Hirst D. Jonathan, Machine Learning in Virtual Screening, Combinatorial Chemistry & High Throughput Screening 2009; 12 (4) . https://dx.doi.org/10.2174/138620709788167980