Deep Learning in Chemistry

Adam C. Mater, Michelle L. Coote

Research output: Contribution to journalArticlepeer-review

171 Citations (Scopus)

Abstract

Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns represented in the data. Over the last eight years, its abilities have increasingly been applied to a wide variety of chemical challenges, from improving computational chemistry to drug and materials design and even synthesis planning. This review aims to explain the concepts of deep learning to chemists from any background and follows this with an overview of the diverse applications demonstrated in the literature. We hope that this will empower the broader chemical community to engage with this burgeoning field and foster the growing movement of deep learning accelerated chemistry.

Original languageEnglish
Pages (from-to)2545–2559
Number of pages15
JournalJournal of Chemical Information and Modeling
Volume59
Issue number6
DOIs
Publication statusPublished - 24 Jun 2019
Externally publishedYes

Keywords

  • Cheminformatics
  • Computational chemistry
  • Deep learning
  • Drug design
  • Machine learning
  • Materials design
  • Open sourcing
  • Quantum mechanical calculations
  • Representation learning
  • Synthesis planning

Fingerprint

Dive into the research topics of 'Deep Learning in Chemistry'. Together they form a unique fingerprint.

Cite this