ArticleScientific reports2021
Explainable machine learning predictions of dual-target compounds reveal characteristic structural features.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Computer-aided discovery of CDK16 inhibitors: a docking-augmented machine learning regression modelling approach.Journal of computer-aided molecular design · 2026Article
- Explainable artificial intelligence for molecular design in pharmaceutical research.Chemical science · 2026Review
- The computationally guided design of selective targeters of multiple proteins (STaMPs) as a new opportunity for small molecule drug discovery.Frontiers in pharmacology · 2025Review
- Article
- Knowledge and data-driven prediction of organ failure in critical care patients.Health information science and systems · 2023Article
- Advances in Computational Polypharmacology.Molecular informatics · 2022Review
- Calculation of exact Shapley values for support vector machines with Tanimoto kernel enables model interpretation.iScience · 2022Article
- Differentiating Inhibitors of Closely Related Protein Kinases with Single- or Multi-Target Activity via Explainable Machine Learning and Feature Analysis.Biomolecules · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compounds with defined multi-target activity play an increasingly important role in drug discovery. Structural features that might be signatures of such compounds have mostly remained elusive thus far. We have explored the potential of explainable machine learning to uncover structural motifs that are characteristic of dual-target compounds. For a pharmacologically relevant target pair-based test system designed for our study, accurate prediction models were derived and the influence of molecular representation features of test compounds was quantified to explain the predictions. The analysis revealed small numbers of specific features whose presence in dual-target and absence in single-target compounds determined accurate predictions. These features formed coherent substructures in dual-target compounds. From computational analysis of specific feature contributions, structural motifs emerged that were confirmed to be signatures of different dual-target activities. Our findings demonstrate the ability of explainable machine learning to bridge between predictions and intuitive chemical analysis and reveal characteristic substructures of dual-target compounds.
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Registered trials
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