Deep learning for molecular design—a review of the state of the art
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We review a recent groundswell of work which uses deep learning techniques to generate and optimize molecules.
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GNNs are commonly used to generate protein representations from chemical and geometric features to predict protein pocket-ligand and protein-protein interaction sites, intra- and inter-molecular energies, binding affinities, complex quality measures, and interface residues.
“GNNs (as described in Section 4.1) are commonly used to generate representations of proteins based on chemical (e.g., free electrons’ and protons donors’ locations) and geometric (e.g., distance-dependent curvature) features to predict protein pocket-ligand and protein-protein interactions sites [116]; intra- and inter-molecule residue contact graphs to predict intra- and inter-molecular energies,”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [111]
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“Although these models vary greatly in method [5], they can usually be categorised by four common architectures/approaches: (1) Language models (such as [6–8]) which require a chemical language (e.”
Method
- Authors
- Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W.
- Affiliations
- University of Maryland, College Park (US)
- American University (US)
- Corresponding author
- Elton, Daniel C.
- Venue
- Molecular Systems Design & Engineering
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2019
- Volume
- 4
- Issue
- 4
- Pages
- 828-849
- ISSN
- 2058-9689 (ISSN-L)
- OpenAlex
- W2945551948
- Cited by
- 683
- References
- 172
- Citations by year
- 2026: 86 · 2025: 77 · 2024: 84 · 2023: 90 · 2022: 88 · 2021: 131 · 2020: 102 · 2019: 23 · 2018: 1 · 2017: 1
- FWCI
- 58.50
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Molecular Systems Design & Engineering (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Office of Naval Research (N00014-17-1-2108)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- deep learning, molecular design, generative models, molecular optimization, drug discovery, machine learning in chemistry
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