Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future
Abstract
This paper is an overview of the most significant and impactful interpretation approaches of quantitative structure-activity relationship (QSAR) models, their development, and application. The evolution of the interpretation paradigm from "model → descriptors → (structure)" to "model → structure" is indicated. The latter makes all models interpretable regardless of machine learning methods or descriptors used for modeling. This opens wide prospects for application of corresponding interpretation approaches to retrieve structure-property relationships captured by any models. Issues of separate approaches are discussed as well as general issues and prospects of QSAR model interpretation.
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For some PFAS, the only available values for physical and chemical properties are estimates derived from mathematical models such as QSAR models.
“In fact, for some PFAS, the only available values for physical and chemical properties are estimates derived from mathematical models, such as quantitative structure–activity relationship (QSAR) models [18], which can approximate the chemical and physical properties of compounds based on their chemical structure, rather values from direct measurements [19,20].”
From The EU’s Per- and Polyfluoroalkyl Substances (PFAS) Ban: A Case of Policy over Science (2023) · cites this paper as [18]
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Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
Rodríguez-Pérez, R., & Bajorath, J. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00314-0 · Cited by 718 · see citing works
Jiang, D., Wu, Z., Hsieh, C.-Y., Chen, G., Liao, B., Wang, Z., Shen, C., Cao, D., Wu, J., & Hou, T. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-020-00479-8 · Cited by 664 · see citing works
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Sheridan, R. P. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00825 · Cited by 84 · see citing works
The EU’s Per- and Polyfluoroalkyl Substances (PFAS) Ban: A Case of Policy over Science
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Toward Interpretable Machine Learning Models for Materials Discovery
Mikulskis, P., Alexander, M. R., & Winkler, D. A. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900045 · Cited by 43 · see citing works
- Authors
- Polishchuk, P.
- Affiliations
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital in Olomouc, Hněvotínská 1333/5, 779 00 Olomouc, Czech Republic
- ORCID
- Polishchuk, Pavel
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-10-13
- Volume
- 57
- Issue
- 11
- Pages
- 2618-2639
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2760085125
- Cited by
- 236
- References
- 116
- Citations by year
- 2026: 23 · 2025: 22 · 2024: 30 · 2023: 26 · 2022: 39 · 2021: 33 · 2020: 23 · 2019: 25 · 2018: 14 · 2017: 1
- FWCI
- 1.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Russian Science Foundation (14-43-00024)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- structure-activity relationship, model interpretability, structure-property relationships, machine learning models, molecular descriptors
- MeSH terms
- Machine Learning, Models, Theoretical, Quantitative Structure-Activity Relationship
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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