ArticleProteins2026
Assessment of Pharmaceutical Protein-Ligand Pose and Affinity Predictions in CASP16.
Article in Proteins, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Docking-based virtual screening: Past, present, and future.Biophysical journal · 2026Review
- ModelCIF Update: Supporting Emerging Classes of Computational Macromolecular Models.Journal of molecular biology · 2026Article
- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- On the generalization and usability of cofolding models for GPCR drug discovery.npj drug discovery · 2026Article
- More protein-ligand data are needed for AlphaFold-like models to enable drug discovery.Current opinion in structural biology · 2026Review
- Mapping the avoid-ome: a systematic open-science approach to predictive ADMET.Nature communications · 2026Review
- The latest AI breakthroughs in structural biology: protein binder design and conformational state prediction.Communications biology · 2026Article
- Evaluating generalization in protein-ligand cofolding methods.Nature structural & molecular biology · 2026Article
- BA-Pred and RMSD-Pred: Integrated Graph Neural Network Models for Accurate Protein-Ligand Binding Affinity and Binding Pose Prediction.Journal of chemical information and modeling · 2026Article
- Practical Outcomes From CASP16 for Users in Need of Biomolecular Structure Prediction.Proteins · 2026Review
- Updates to the CASP Infrastructure in 2024.Proteins · 2026Article
- Progress and Bottlenecks for Deep Learning in Computational Structure Biology: CASP Round XVI.Proteins · 2026Article
- Article
- Assessment of Nucleic Acid Structure Prediction in CASP16.Proteins · 2026Article
- MPBind: a multitask protein binding site predictor using protein language models and equivariant GNNs.Bioinformatics (Oxford, England) · 2025Article
- TEMPL: A Template-Based Protein-Ligand Pose Prediction Baseline.Journal of chemical information and modeling · 2025Article
- Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction.bioRxiv : the preprint server for biology · 2025Article
- Assessment of nucleic acid structure prediction in CASP16.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
The protein-ligand component of the 16th Critical Assessment of Structure Prediction (CASP16) challenged participants to predict both binding poses and affinities of small molecules to protein targets, with a focus on drug-like compounds from pharmaceutical discovery projects. Thirty research groups submitted predictions for 229 protein-ligand pose targets and 140 affinity targets across five protein systems. Among the submitted predictions, template-based pose-prediction methods did particularly well, with the best groups achieving mean LDDT-PLI values of 0.69 (scale of 0-1 with 1 best). For comparison, we also ran a set of automated baseline pose-prediction methods, including ones using deep neural networks. Of these, AlphaFold 3 did particularly well, with a mean LDDT-PLI of 0.8, thus outscoring the best CASP16 predictor. The CASP affinity predictions showed modest correlation with experimental data (maximum Kendall's τ = 0.42), well below the theoretical maximum possible given experimental uncertainty (~0.73). As seen in prior challenges, providing experimental structures did not improve affinity predictions in the second stage of the challenge, suggesting that the scoring functions used here are a key limiting factor. Overall, the accuracy achieved by CASP participants is similar to that observed in the prior Drug Design Data Resource (D3R) blinded prediction challenges. The present results highlight the progress and persistent challenges in computational protein-ligand modeling and provide valuable benchmarks for the field of computer-aided drug design.
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