ArticleACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine2025
Physics-Guided Active Learning for New Ligand Discovery.
Article in ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
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0 citing papers in PubMed.
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Authors and funding
3 authors.
Funding
Abstract
Structure-based drug discovery seeks to identify small molecules that bind to protein targets and modulate their function, but current workflows remain slow, costly, and data-limited. While generative models can design ligands from protein structures, they often lack physical grounding and fail to generalize beyond static training data. We propose an active learning framework that generates 3D ligands conditioned on protein pocket geometry and iteratively retrains on the top 10% of candidates, evaluated using a composite score integrating binding affinity and solvation energy. On the refined PDBBind v2019 dataset, our approach yields consistent dataset-wide improvements: median binding affinity increases from -9.3 to -9.7 kcal/mol (compared to -8.7 kcal/mol for reference ligands), median solvation free energy decreases from 1.32 to 0.94 kcal/mol, and ASKCOS scores improve from -2.17e
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.