Deep Learning in Chemistry

By Adam C. Mater, Michelle L. Coote. 2019

466 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computational Theory and Mathematics, Materials Chemistry

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Citations in the corpus

Predicting cellular responses to complex perturbations in high‐throughput screens (2023, background): “These could, for example, be the similarity of chemical compounds (Mater & Coote, 2019), or clinical-covariate induced differences of patient IDs.”
Artificial Intelligence For Natural Product Drug Discovery (2023, background): “Deep learning has the flexibility to capture nonlinear relationships and to accept non-tabular input that extends the applicability of AI for natural product computational research to non-Euclidean domains 100,101 .”
Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns (2019, methodology): “…2014; Rupp, 2015; Goh et al., 2017; Li et al., 2017; Butler et al., 2018; Fleming, 2018; Gao et al., 2018; Kishimoto et al., 2018; Popova et al., 2018; Aspuru-Guzik et al., 2019; Elton et al., 2019; Gromski et al., 2019; Mater and Coote, 2019; Schleder et al., 2019; Venkatasubramanian, 2019).”
Milvus: A Purpose-Built Vector Data Management System (2021, background): “For example, YouTube embeds videos to vectors [15]; Airbnb models houses with vectors [25]; Bioscientists describe the molecular structural information of drug compounds using vectors [13, 51].”