ArticleJournal of chemical information and modeling2025
Can AI-Predicted Complexes Teach Machine Learning to Compute Drug Binding Affinity?
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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Who cites it
3 citing papers in PubMed.
- 1,5-Disubstituted Tetrazoles as Promising Anticancer Resistance Agents: A Chemoinformatic Characterization.ACS omega · 2026Article
- Developments and challenges in hit progression within fragment-based drug discovery.Nature communications · 2026Review
- Integrating AI-Based Protein Modeling and Fragment Molecular Orbital Analysis for Structure-Based Drug Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We evaluate the feasibility of using co-folding models for synthetic data augmentation in training machine learning-based scoring functions (MLSFs) for binding affinity prediction. Our results show that performance gains depend critically on the structural quality of augmented data. In light of this, we established simple heuristics for identifying high-quality co-folding predictions without reference structures, enabling them to substitute for experimental structures in MLSF training. Our study informs future data augmentation strategies based on co-folding models.
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Registered trials
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