ChemFlowFrom 2D Chemical Libraries to Protein–Ligand Binding Free Energies

The accurate prediction of protein–ligand binding
affinities
is a fundamental problem for the rational design of new drug entities.
Current computational approaches are either too expensive or inaccurate
to be effectively used in virtual high-throughput screening campaigns.
In addition, the most sophisticated methods, e.g., those based on
configurational sampling by molecular dynamics, require significant
pre- and postprocessing to provide a final ranking, which hinders
straightforward applications by nonexpert users. We present a novel
computational platform named ChemFlow to bridge the gap between 2D
chemical libraries and estimated protein–ligand binding affinities.
The software is designed to prepare a library of compounds provided
in SMILES or SDF format, dock them into the protein binding site,
and rescore the poses by simplified free energy calculations. Using
a data set of 626 protein–ligand complexes and GPU computing,
we demonstrate that ChemFlow provides relative binding free energies
with an RMSE < 2 kcal/mol at a rate of 1000 ligands per day on
a midsize computer cluster. The software is publicly available at https://github.com/IFMlab/ChemFlow.