DOX_BDW: Incorporating Solvation and Desolvation Effects of Cavity Water into Nonfitting Protein–Ligand Binding Affinity Prediction

Accurate prediction of the protein–ligand binding
affinity
(PLBA) with an affordable cost is one of the ultimate goals in the
field of structure-based drug design (SBDD), as well as a great challenge
in the computational and theoretical chemistry. Herein, we have systematically
addressed the complicated solvation and desolvation effects on the
PLBA brought by the difference of the explicit water in the protein
cavity before and after ligands bind to the protein-binding site.
Based on the new solvation model, a nonfitting method at the first-principles
level for the PLBA prediction was developed by taking the bridging
and displaced water (BDW) molecules into account simultaneously. The
newly developed method, DOX_BDW, was validated against a total of
765 noncovalent and covalent protein–ligand binding pairs,
including the CASF2016 core set, Cov_2022 covalent binding testing
set, and six testing sets for the hit and lead compound optimization
(HLO) simulation. In all of the testing sets, the DOX_BDW method was
able to produce PLBA predictions that were strongly correlated with
the corresponding experimental data (R = 0.66–0.85).
The overall performance of DOX_BDW is better than the current empirical
scoring functions that are heavily parameterized. DOX_BDW is particularly
outstanding for the covalent binding situation, implying the need
for considering an electronic structure in covalent drug design. Furthermore,
the method is especially recommended to be used in the HLO scenario
of SBDD, where hundreds of similar derivatives need to be screened
and refined. The computational cost of DOX_BDW is affordable, and
its accuracy is remarkable.