Active Learning FEP: Impact on Performance of AL Protocol and Chemical Diversity

Active learning using models built on binding potency
predictions
from free energy perturbation (AL-FEP) has been proposed as a method
for generating machine learning models capable of predicting biochemical
potency for early-stage lead optimization where limited measured data
are available. Two applications of AL-FEP are described here for different
bromodomain inhibitor series that were developed in historic GSK projects:
one where the core is kept constant and the other where core changes
are included in the pool of compound ideas. Measured biochemical potency
data have been used to assess the performance of the final models
and demonstrate that well-performing models can be generated within
several rounds of active learning, especially when the core is kept
constant. To apply this method routinely to drug discovery projects,
a retrospective evaluation of the AL-FEP workflow has been conducted
covering parameters including the compound selection strategy, explore–exploit
ratios, and number of compounds selected per cycle. Significant differences
in performance in terms of model enrichment and R2 are observed and rationalized. Recommendations are made
as to when specific parameters should be employed for AL-FEP depending
on the context (maximizing potency or broad-range prediction accuracy)
in which the final model is to be deployed.