CASF-2016

The CASF-2016 dataset is a benchmark used to evaluate protein-ligand binding affinity prediction models, containing a collection of protein-ligand complexes with known binding affinities.

Papers using CASF-2016 (8)

Harnessing angular geometry in deep learning for protein-ligand binding affinity prediction2026 · 1 citesSurGBSA: Learning Representations From Molecular Dynamics Simulations2025 · 1 citesBioLM-Score: Language-Prior Conditioned Probabilistic Geometric Potentials for Protein-Ligand Scoring2026PLANET v2.0: A comprehensive Protein-Ligand Affinity Prediction Model Based on Mixture Density Network2026A Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction2025Geometric Graph Learning with Extended Atom-Types Features for Protein-Ligand Binding Affinity Prediction2023 · 16 citesFrom Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning2022 · 7 citesSPIN: SE(3)-Invariant Physics Informed Network for Binding Affinity Prediction2024 · 2 cites