Active Learning-Guided Hit Optimization for the Leucine-Rich Repeat Kinase 2 WDR Domain Based on In Silico Ligand-Binding Affinities

The leucine-rich repeat kinase 2 (LRRK2) is the most
mutated gene
in familial Parkinson’s disease, and its mutations lead to
pathogenic hallmarks of the disease. The LRRK2 WDR domain is an understudied
drug target for Parkinson’s disease, with no known inhibitors
prior to the first phase of the Critical Assessment of Computational
Hit-Finding Experiments (CACHE) Challenge. A unique advantage of the
CACHE Challenge is that the predicted molecules are experimentally
validated in-house. Here, we report the design and experimental confirmation
of LRRK2 WDR inhibitor molecules. We used an active learning (AL)
machine learning (ML) workflow based on optimized free-energy molecular
dynamics (MD) simulations utilizing the thermodynamic integration
(TI) framework to expand a chemical series around two of our previously
confirmed hit molecules. We identified 8 experimentally verified novel
inhibitors out of 35 experimentally tested (23% hit rate). These results
demonstrate the efficacy of our free-energy-based active learning
workflow to explore large chemical spaces quickly and efficiently
while minimizing the number and length of expensive simulations. This
workflow is widely applicable to screening any chemical space for
small-molecule analogs with increased affinity, subject to the general
constraints of RBFE calculations. The mean absolute error of the TI
MD calculations was 2.69 kcal/mol, with respect to the measured KD of hit compounds.