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The Next Era: Deep Learning in Pharmaceutical Research

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Abstract

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule’s properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

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Abbreviations

ADME/Tox:

Absorption, distribution, metabolism, excretion/toxicology

AUC:

Area under the curve

DILI:

Drug induced liver injury

hERG:

Human ether a-go-go related gene

PLGA:

Poly-lactide-co-glycolide

QSAR:

Quantitative structure activity relationships

SVM:

Support vector machines

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ACKNOWLEDGMENTS AND DISCLOSURES

Dr. Alex M. Clark and Dr. Peter W. Swaan are kindly acknowledged for useful discussions on this topic. SE is founder and owner of Collaborations Pharmaceuticals, Inc. he was a consultant for Collaborative Drug Discovery, Inc.

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  1. Collaborations Pharmaceuticals, Inc, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina, 27526, USA

    Sean Ekins

  2. Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California, 94010, USA

    Sean Ekins

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Correspondence to Sean Ekins.

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Funding

This work was partially supported by Award Number 9R44TR000942-02 “Biocomputation across distributed private datasets to enhance drug discovery” from the NIH National Center for Advancing Translational Sciences.

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Ekins, S. The Next Era: Deep Learning in Pharmaceutical Research. Pharm Res 33, 2594–2603 (2016). https://doi.org/10.1007/s11095-016-2029-7

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