ArticleJournal of chemical information and modeling2026
Robustness of Protein-Ligand Binding Affinity Prediction Models to Docked and Predicted Structures.
Article in Journal of chemical information and modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Critical Artifacts Improve Reproducibility of Protein-Ligand Binding Affinity Prediction Models on CASF-2016.Journal of chemical information and modeling · 2026Article
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2 authors.
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Abstract
Structure-based deep learning models for protein-ligand binding affinity prediction (PLBAP) are commonly benchmarked using experimentally resolved co-crystal structures, but real use cases often rely on computed inputs (docked or predicted complexes). To quantify this benchmark-to-deployment mismatch, we compared the CASF-2016 performance of five reproducible PLBAP pipelines across crystal structures, GNINA docking into holo/apo/AlphaFold3-predicted receptors, and AlphaFold3 co-folding. Critically, access to an experimentally resolved apo receptor conformer provided only marginal benefit over AlphaFold3-predicted receptor structures. AlphaFold3 co-folding was competitive with, and for some models significantly better than, rigid-receptor docking into the apo conformer (Holm
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