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. 2019 May;18(5):435-441.
doi: 10.1038/s41563-019-0338-z. Epub 2019 Apr 18.

Exploiting machine learning for end-to-end drug discovery and development

Sean Ekins  1 , Ana C Puhl  2 , Kimberley M Zorn  2 , Thomas R Lane  2 , Daniel P Russo  2   3 , Jennifer J Klein  2 , Anthony J Hickey  4   5 , Alex M Clark  6
Affiliations

Exploiting machine learning for end-to-end drug discovery and development

Sean Ekins et al. Nat Mater. 2019 May.

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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Conflict of interest statement

Competing Financial Interests

SE is Founder and CEO, ACP, KMZ, TL and JJK are employees, DPR and AMC are consultants of Collaborations Pharmaceuticals, Inc. AMC is also the founder and owner of Molecular Materials Informatics, Inc. AJH has no conflicts of interest.

Figures

Figure 1.
Figure 1.. Implementing end-to-end (E2E) machine learning models at all stages of drug discovery and development illustrating some of the key areas that could be modeled.
A drug discovery and development dashboard for E2E machine learning provides the go-no-go decisions based on inputs of machine learning algorithms (SVM – support vector machine; DL – deep learning; BNB – Naïve Bayesian; KNN – K-nearest neighbors; RF – random forest; ADA-AdaBoost) or a consensus.
Figure 2.
Figure 2.. Demonstrating iterative drug discovery using machine learning.
A. The prospective machine learning approach. B. Demonstration of linkage between disease, target and machine learning model using Pitt Hopkins Syndrome as an example.
Figure 2.
Figure 2.. Demonstrating iterative drug discovery using machine learning.
A. The prospective machine learning approach. B. Demonstration of linkage between disease, target and machine learning model using Pitt Hopkins Syndrome as an example.
See this image and copyright information in PMC

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