ArticleNature communications2024
Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Interpretable machine learning framework for peptide lead optimization in ultralarge design spaces.npj drug discovery · 2026Article
- An arithmetic method algorithm optimizing k-nearest neighbors compared to regression algorithms and evaluated on real world data sources.Scientific reports · 2026Article
- Structure-guided discovery and validation of a potent RamR inhibitor targeting efflux-mediated multidrug resistance in Salmonella typhimurium.Folia microbiologica · 2025Article
- AdapTor: Adaptive Topological Regression for quantitative structure-activity relationship modeling.Journal of cheminformatics · 2025Article
- Cationic alkyl chain length and nanoaggregate form of ionic liquids dominate biocompatibility and toxicity.Nature communications · 2025Article
- Advances in medical devices using nanomaterials and nanotechnology: Innovation and regulatory science.Bioactive materials · 2025Review
- Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling.Nature communications · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application in molecular design. We propose a similarity-based regression framework, topological regression (TR), that offers a statistically grounded, computationally fast, and interpretable technique to predict drug responses. We compare the predictive performance of TR on 530 ChEMBL human target activity datasets against the predictive performance of deep-learning-based QSAR models. Our results suggest that our sparse TR model can achieve equal, if not better, performance than the deep learning-based QSAR models and provide better intuitive interpretation by extracting an approximate isometry between the chemical space of the drugs and their activity space.
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