ArticleMolecules (Basel, Switzerland)2021
Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Accurate prediction of activity cliff compounds based on bioactivity profiles depends on assay nearest neighbor relationships.Journal of cheminformatics · 2026Article
- ACtriplet: An improved deep learning model for activity cliffs prediction by in tegrating triplet loss and pre-training.Journal of pharmaceutical analysis · 2025Article
- Non-Nucleoside Lycorine-Based Analogs as Potential DENV/ZIKV NS5 Dual Inhibitors: Structure-Based Virtual Screening and Chemoinformatic Analysis.Metabolites · 2024Article
- Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure-Activity Relationships.ACS omega · 2024Article
- Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity.Journal of cheminformatics · 2023Article
- On QSAR-based cardiotoxicity modeling with the expressiveness-enhanced graph learning model and dual-threshold scheme.Frontiers in physiology · 2023Article
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Authors and funding
4 authors.
Funding
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Abstract
Activity cliffs (ACs) are formed by two structurally similar compounds with a large difference in potency. Accurate AC prediction is expected to help researchers' decisions in the early stages of drug discovery. Previously, predictive models based on matched molecular pair (MMP) cliffs have been proposed. However, the proposed methods face a challenge of interpretability due to the black-box character of the predictive models. In this study, we developed interpretable MMP fingerprints and modified a model-specific interpretation approach for models based on a support vector machine (SVM) and MMP kernel. We compared important features highlighted by this SVM-based interpretation approach and the SHapley Additive exPlanations (SHAP) as a major model-independent approach. The model-specific approach could capture the difference between AC and non-AC, while SHAP assigned high weights to the features not present in the test instances. For specific MMPs, the feature weights mapped by the SVM-based interpretation method were in agreement with the previously confirmed binding knowledge from X-ray co-crystal structures, indicating that this method is able to interpret the AC prediction model in a chemically intuitive manner.
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