Deep Learning in Chemistry
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.
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DNN methods have attractively widespread application across many disciplines.
“DNN methods are attractingly widespread application across many disciplines .50”
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Cited by
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A review of large language models and autonomous agents in chemistry
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Bioactivity descriptors for uncharacterized chemical compounds
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- Authors
- Mater, A. C., & Coote, M. L.
- Affiliations
- Australian National University (AU)
- Corresponding author
- Coote, Michelle L.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-06-13
- Volume
- 59
- Issue
- 6
- Pages
- 2545-2559
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2950128007
- Cited by
- 565
- References
- 155
- Citations by year
- 2026: 51 · 2025: 55 · 2024: 87 · 2023: 100 · 2022: 107 · 2021: 88 · 2020: 54 · 2019: 23
- FWCI
- 3.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- Funders
- Australian Research Council (FL170100041)
- Australian National University
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- deep learning, machine learning in chemistry, computational chemistry, drug design, materials design, synthesis planning, hierarchical feature extraction
- MeSH terms
- Deep Learning, Cheminformatics, Humans, Drug Design, Neural Networks, Computer, Quantitative Structure-Activity Relationship, Chemical Phenomena, Drug Discovery, Chemistry Techniques, Synthetic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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