Research Area:  Machine Learning

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.

Keywords:  
Diagnostics
Drug delivery
Drug discovery
Drug regulation
Drug safety
Naive Bayesian
Support vector machines
Machine learning

Author(s) Name:  Sean Ekins, Ana C. Puhl, Kimberley M. Zorn, Thomas R. Lane, Daniel P. Russo, Jennifer J. Klein, Anthony J. Hickey & Alex M. Clark

Publisher name:  Springer

DOI:  10.1038/s41563-019-0338-z

Volume Information:  volume 18, pages: 435–441 (2019)

Paper Link:   https://www.nature.com/articles/s41563-019-0338-z