Deep Learning in Chemistry

• Published in: Journal of chemical information and modeling Vol. 59; no. 6; pp. 2545 - 2559
• Main Authors: Mater, Adam C, Coote, Michelle L
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 24.06.2019
• Subjects: Artificial intelligence; Chemistry; Chemists; Computational chemistry; Deep learning; Feature extraction; Machine learning; Organic chemistry; Representation learning; Deep learning; Computational chemistry; Cheminformatics; Synthesis planning; Materials design; Machine learning; Drug design; Open sourcing; Quantum mechanical calculations
• ISSN: 1549-9596; 1549-960X; 1549-960X
• DOI: 10.1021/acs.jcim.9b00266

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.