Cited by 236Computational Drug Discovery MethodsMachine Learning in Materials ScienceNeural Networks and Applications

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A renaissance of neural networks in drug discovery

Baskin, Igor I., Winkler, David, Tetko, Igor V.·2016·Expert Opinion on Drug Discovery

DOI 10.1080/17460441.2016.1201262

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Abstract

INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach. AREAS COVERED: In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets; predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties; characteristics of drug-delivery systems and virtual screening. EXPERT OPINION: Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It's anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine.

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Authors: Baskin, I. I., Winkler, D., & Tetko, I. V.
Affiliations: Lomonosov Moscow State University (RU)
Flinders University (AU)
Helmholtz Munich (DE)
Corresponding author: Tetko, Igor V.
ORCID: Baskin, Igor I.
Winkler, David
Tetko, Igor V.
Venue: Expert Opinion on Drug Discovery
Publisher: Informa UK Limited
Published: 2016-07-04
Volume: 11
Issue: 8
Pages: 785-795
ISSN: 1746-0441 (ISSN-L)
1746-045X
DOI: 10.1080/17460441.2016.1201262
OpenAlex: W2436108096
Cited by: 235
References: 118
Citations by year: 2026: 12 · 2025: 18 · 2024: 25 · 2023: 24 · 2022: 41 · 2021: 26 · 2020: 26 · 2019: 21 · 2018: 27 · 2017: 8 · 2016: 7
FWCI: 1.63
Type: journal article
Language: en
Open access: closed
Locations: Expert Opinion on Drug Discovery (publishedVersion)
PubMed (publishedVersion)
Site cant be reached (submittedVersion)
PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords: artificial neural networks, drug discovery, BP neural network, self-organizing maps, deep learning, overfitting, regularization, ensemble learning, model interpretability, applicability domain, structure-activity relationship, toxicity prediction, ADMET prediction, physicochemical property prediction, drug delivery systems, virtual screening
MeSH terms: Pharmaceutical Preparations, Humans, Models, Biological, Structure-Activity Relationship, Drug Design, Drug Delivery Systems, Neural Networks, Computer, Computer-Aided Design, Drug Discovery

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A renaissance of neural networks in drug discovery

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