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, whose 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. CACHE challenges are designed to attract state-of-the-art computational methods for both hit-finding and lead optimization of small molecule inhibitors to challenging protein targets. A unique advantage of the CACHE challenge is that the predicted molecules are experimentally validated in-house. Here we report on our winning submission of experimentally confirmed LRRK2 WDR inhibitor molecules, predicted from thermodynamics integration (TI) calculations performed on only 672 compounds within a chemical space of 25,171 molecules. We used a free energy molecular dynamics (MD) -based active learning (AL) workflow to optimize our two previously confirmed hit molecules. We identified 8 experimentally verified novel inhibitors out of 35 tested (23% hit rate) with a maximum affinity increase of almost 35-fold. These results demonstrate the efficacy of free energy-based active learning workflow to quickly and efficiently explore large chemical spaces while minimizing the number and length of computational simulations. This workflow is widely applicable to the screening of any chemical space for small molecule analogs with increased affinity, subject to the general constraints of RBFE calculations. The mean absolute error of TI MD calculations was 1.30 kcal/mol with respect to measured KD of hit compounds.