Pharmaceutical Research

, Volume 33, Issue 11, pp 2594–2603 | Cite as

The Next Era: Deep Learning in Pharmaceutical Research

  • Sean EkinsEmail author


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.


artificial intelligence deep Learning drug discovery machine learning pharmaceutics 



Absorption, distribution, metabolism, excretion/toxicology


Area under the curve


Drug induced liver injury


Human ether a-go-go related gene




Quantitative structure activity relationships


Support vector machines



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.

Compliance with Ethical Standards


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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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Collaborations Pharmaceuticals, IncFuquay-VarinaUSA
  2. 2.Collaborative Drug DiscoveryBurlingameUSA

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