Pharmaceutical Research

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

The Next Era: Deep Learning in Pharmaceutical Research

Perspective

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.

KEY WORDS

artificial intelligence deep Learning drug discovery machine learning pharmaceutics 

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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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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