Exploiting Machine Learning for End-to-end Drug Discovery and Development

Overview

Journal Nat Mater

Date 2019 Apr 20

PMID 31000803

Citations 146

Authors

Sean Ekins

Ana C Puhl

Kimberley M Zorn

Thomas R Lane

Daniel P Russo

Jennifer J Klein

Anthony J Hickey

Alex M Clark

Affiliations

Soon will be listed here.

Abstract

A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.

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