  Many physics-based and machine-learned scoring functions (SFs) used to
predict protein-ligand binding free energies have been trained on the PDBBind
dataset. However, it is controversial as to whether new SFs are actually
improving since the general, refined, and core datasets of PDBBind are
cross-contaminated with proteins and ligands with high similarity, and hence
they may not perform comparably well in binding prediction of new
protein-ligand complexes. In this work we have carefully prepared a cleaned
PDBBind data set of non-covalent binders that are split into training,
validation, and test datasets to control for data leakage, defined as proteins
and ligands with high sequence and structural similarity. The resulting
leak-proof (LP)-PDBBind data is used to retrain four popular SFs: AutoDock
Vina, Random Forest (RF)-Score, InteractionGraphNet (IGN), and DeepDTA, to
better test their capabilities when applied to new protein-ligand complexes. In
particular we have formulated a new independent data set, BDB2020+, by matching
high quality binding free energies from BindingDB with co-crystalized
ligand-protein complexes from the PDB that have been deposited since 2020.
Based on all the benchmark results, the retrained models using LP-PDBBind
consistently perform better, with IGN especially being recommended for scoring
and ranking applications for new protein-ligand systems.
