FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning

Significant improvements have been made in the past decade
to methods
that rapidly and accurately predict binding affinity through free
energy perturbation (FEP) calculations. This has been driven by recent
advances in small-molecule force fields and sampling algorithms combined
with the availability of low-cost parallel computing. Predictive accuracies
of ∼1 kcal mol–1 have been regularly achieved,
which are sufficient to drive potency optimization in modern drug
discovery campaigns. Despite the robustness of these FEP approaches
across multiple target classes, there are invariably target systems
that do not display expected performance with default FEP settings.
Traditionally, these systems required labor-intensive manual protocol
development to arrive at parameter settings that produce a predictive
FEP model. Due to the (a) relatively large parameter space to be explored,
(b) significant compute requirements, and (c) limited understanding
of how combinations of parameters can affect FEP performance, manual
FEP protocol optimization can take weeks to months to complete, and
often does not involve rigorous train-test set splits, resulting in
potential overfitting. These manual FEP protocol development timelines
do not coincide with tight drug discovery project timelines, essentially
preventing the use of FEP calculations for these target systems. Here,
we describe an automated workflow termed FEP Protocol Builder (FEP-PB)
to rapidly generate accurate FEP protocols for systems that do not
perform well with default settings. FEP-PB uses an active-learning
workflow to iteratively search the protocol parameter space to develop
accurate FEP protocols. To validate this approach, we applied it to
pharmaceutically relevant systems where default FEP settings could
not produce predictive models. We demonstrate that FEP-PB can rapidly
generate accurate FEP protocols for the previously challenging MCL1
system with limited human intervention. We also apply FEP-PB in a
real-world drug discovery setting to generate an accurate FEP protocol
for the p97 system. FEP-PB is able to generate a more accurate protocol
than the expert user, rapidly validating p97 as amenable to free energy
calculations. Additionally, through the active-learning workflow,
we are able to gain insight into which parameters are most important
for a given system. These results suggest that FEP-PB is a robust
tool that can aid in rapidly developing accurate FEP protocols and
increasing the number of targets that are amenable to the technology.