ArticleBeilstein journal of organic chemistry2024
Finding the most potent compounds using active learning on molecular pairs.
Article in Beilstein journal of organic chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning.bioRxiv : the preprint server for biology · 2026Article
- ChemScreener: an active learning enabled hit discovery workflow with WDR5 inhibitor case study.Journal of cheminformatics · 2026Article
- Adaptive experimentation and optimization in organic chemistry.Beilstein journal of organic chemistry · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Active learning allows algorithms to steer iterative experimentation to accelerate and de-risk molecular optimizations, but actively trained models might still exhibit poor performance during early project stages where the training data is limited and model exploitation might lead to analog identification with limited scaffold diversity. Here, we present ActiveDelta, an adaptive approach that leverages paired molecular representations to predict improvements from the current best training compound to prioritize further data acquisition. We apply the ActiveDelta concept to both graph-based deep (Chemprop) and tree-based (XGBoost) models during exploitative active learning for 99 K
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