Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling

In silico identification of potent protein
inhibitors
commonly requires prediction of a ligand binding free energy (BFE).
Thermodynamics integration (TI) based on molecular dynamics (MD) simulations
is a BFE calculation method capable of acquiring accurate BFE, but
it is computationally expensive and time-consuming. In this work,
we have developed an efficient automated workflow for identifying
compounds with the lowest BFE among thousands of congeneric ligands,
which requires only hundreds of TI calculations. Automated machine
learning (AutoML) orchestrated by active learning (AL) in an AL–AutoML
workflow allows unbiased and efficient search for a small set of best-performing
molecules. We have applied this workflow to select inhibitors of the
SARS-CoV-2 papain-like protease and were able to find 133 compounds
with improved binding affinity, including 16 compounds with better
than 100-fold binding affinity improvement. We obtained a hit rate
that outperforms that expected of traditional expert medicinal chemist-guided
campaigns. Thus, we demonstrate that the combination of AL and AutoML
with free energy simulations provides at least 20× speedup relative
to the naïve brute force approaches.