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. 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. 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.

This paper presents the experimental protein-ligand datasets used as benchmarks in the CASP 16 blind prediction challenge—the first CASP competition to incorporate targets from pharmaceutical discovery projects. We have assembled and characterized protein-ligand complexes for four proteins that are known or candidate drug targets: human chymase, human cathepsin G, human autotaxin, and the SARS-CoV-2 main protease. The collection encompasses over 200 co-crystal structures at resolutions better than 2.7 Å, paired with binding measurements for approximately 160 compounds covering a broad affinity range (nanomolar to high micromolar). Many systems feature potentially challenging characteristics, including chymase’s electropositive surface and acidic ligands, which require proper handling of titratable ligand groups; autotaxin complexes with and without zinc coordination; and a SARS-CoV-2 protease crystal form exhibiting an unusually open active site conformation. We describe the experimental approaches—from protein production and crystallization to binding assay development—that yielded these reference data. Contributed by scientists at Hoffman LaRoche and Idorsia Pharmaceuticals, these datasets represent actual drug discovery projects and therefore represent a realistic testbed for assessing how computational methods perform on pharmaceutically relevant targets.