ArticleCommunications biology2024
Deep active learning with high structural discriminability for molecular mutagenicity prediction.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- ASCENT: an active transfer learning paradigm for efficient drug-target interaction prediction.Science China. Life sciences · 2026Article
- AMPred-MFG: Investigating the Mutagenicity of Compounds Using Motif-Based Graph Combined with Molecular Fingerprints and Graph Attention Mechanism.Interdisciplinary sciences, computational life sciences · 2026Article
- Active Stacking-Deep Learning with Strategic Sampling for Small and Imbalanced Chemical Toxicity Prediction.ACS omega · 2025Article
- Evidential deep learning-based drug-target interaction prediction.Nature communications · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- IECata: interpretable bilinear attention network and evidential deep learning improve the catalytic efficiency prediction of enzymes.Briefings in bioinformatics · 2025Article
- A guide for active learning in synergistic drug discovery.Scientific reports · 2025Article
- Recent advances in AI-based toxicity prediction for drug discovery.Frontiers in chemistry · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The assessment of mutagenicity is essential in drug discovery, as it may lead to cancer and germ cells damage. Although in silico methods have been proposed for mutagenicity prediction, their performance is hindered by the scarcity of labeled molecules. However, experimental mutagenicity testing can be time-consuming and costly. One solution to reduce the annotation cost is active learning, where the algorithm actively selects the most valuable molecules from a vast chemical space and presents them to the oracle (e.g., a human expert) for annotation, thereby rapidly improving the model's predictive performance with a smaller annotation cost. In this paper, we propose muTOX-AL, a deep active learning framework, which can actively explore the chemical space and identify the most valuable molecules, resulting in competitive performance with a small number of labeled samples. The experimental results show that, compared to the random sampling strategy, muTOX-AL can reduce the number of training molecules by about 57%. Additionally, muTOX-AL exhibits outstanding molecular structural discriminability, allowing it to pick molecules with high structural similarity but opposite properties.
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