Synergy and Complementarity between Focused Machine Learning and Physics-Based Simulation in Affinity Prediction

We present results
on the extent to which physics-based simulation
(exemplified by FEP+) and focused machine learning (exemplified
by QuanSA) are complementary for ligand affinity prediction. For both
methods, predictions of activity for LFA-1 inhibitors from a medicinal
chemistry lead optimization project were accurate within the applicable
domain of each approach. A hybrid model that combined predictions
by both approaches by simple averaging performed better than either
method, with respect to both ranking and absolute pKi values. Two publicly available FEP+ benchmarks,
covering 16 diverse biological targets, were used to test the generality
of the synergy. By identifying training data specifically focused
on relevant ligands, accurate QuanSA models were derived using ligand
activity data known at the time of the original series publications.
Results across the 16 benchmark targets demonstrated significant improvements
both for ranking and for absolute pKi values
using hybrid predictions that combined the FEP+ and QuanSA
predicted affinity values. The results argue for a combined approach
for affinity prediction that makes use of physics-driven methods as
well as those driven by machine learning, each applied carefully on
appropriate compounds, with hybrid prediction strategies being employed
where possible.