Molecular representations in AI-driven drug discovery: a review and practical guide
Open accessAbstract
The technological advances of the past century, marked by the computer revolution and the advent of high-throughput screening technologies in drug discovery, opened the path to the computational analysis and visualization of bioactive molecules. For this purpose, it became necessary to represent molecules in a syntax that would be readable by computers and understandable by scientists of various fields. A large number of chemical representations have been developed over the years, their numerosity being due to the fast development of computers and the complexity of producing a representation that encompasses all structural and chemical characteristics. We present here some of the most popular electronic molecular and macromolecular representations used in drug discovery, many of which are based on graph representations. Furthermore, we describe applications of these representations in AI-driven drug discovery. Our aim is to provide a brief guide on structural representations that are essential to the practice of AI in drug discovery. This review serves as a guide for researchers who have little experience with the handling of chemical representations and plan to work on applications at the interface of these fields.
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Many studies have integrated chemical structure, binding affinities, physical and chemical principles, and amino acid sequences to improve protein interaction quantification.
“Many have integrated various data modalities, including chemical structure, binding affinities, physical and chemical principles, and amino acid sequences, to improve protein interaction quantification [33].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [33]
Experiments required to measure drug-drug interactions and toxicity face a combinatorial explosion problem.
“But, in addition to high financial cost, the experiments required to measure drug-drug interactions and toxicity face a combinatorial explosion problem [33].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [33]
Minimizing adverse drug events is part of the drug discovery pipeline.
“Part of the drug discovery pipeline is minimizing adverse drug events [33, 34].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [33]
Quantifying binding affinity and specificity requires labor- and cost-intensive experiments.
“However, quantifying these metrics requires labor- and cost-intensive experiments [33, 34].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [33]
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GlycanFormatConverter: a conversion tool for translating the complexities of glycans
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A Branch-and-Bound approach for tautomer enumeration
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Markov Logic Networks for Optical Chemical Structure Recognition
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CHORTLES: A Method for Representing Oligomeric and Template-Based Mixtures
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Self-Contained Sequence Representation: Bridging the Gap between Bioinformatics and Cheminformatics
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Using Natural Language Processing to Learn the Grammar of GlycansPreprint
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Flagged first, then by citations
Research works printed but not linked: 1
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Sorted by citations
Why 90% of clinical drug development fails and how to improve it?
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Applications and potential of in silico approaches for psychedelic chemistry
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- Authors
- David, L., Thakkar, A., Mercado, R., & Engkvist, O.
- Affiliations
- AstraZeneca (Sweden) (SE)
- University of Bern (CH)
- Corresponding author
- David, Laurianne
- Venue
- Journal of Cheminformatics
- Published
- 2020-09-17
- Volume
- 12
- Issue
- 1
- Pages
- 56-56
- ISSN
- 1758-2946 (ISSN-L)
- PMC
- PMC7495975
- OpenAlex
- W3087318293
- Cited by
- 587
- References
- 140
- Citations by year
- 2026: 103 · 2025: 113 · 2024: 131 · 2023: 98 · 2022: 79 · 2021: 57 · 2020: 4
- FWCI
- 5.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Cheminformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- H2020 Marie Skłodowska-Curie Actions (676434)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Mathematics › Geometry and Topology › Graph theory and applications
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- molecular representation, chemical graph representation, artificial intelligence in drug discovery, structural representation, cheminformatics, molecular visualization, high-throughput screening
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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