We have leveraged Schrödinger's computational modeling technology extensively to accelerate the program execution. Free energy perturbation (FEP+) technology was applied to prioritize compounds based on prospective binding potency predictions. Overall, the FEP+ models have performed well in predicting compounds binding potency to the hDAO enzyme. As shown in Figure , compound experimental hDAO inhibitory potency correlates well with prospectively predicted potency across the three chemical series. 24 Of the ~11000 ideas designed and profiled in silico, we synthesized 208 compounds and only 20 of these were unexpectedly inactive (>10 uM), demonstrating that the physics-based methods allowed us to quickly prioritize compounds of interest and deprioritize compounds that did not meet project objectives. In addition to structure-based design by seasoned medicinal chemists and modelers, we have also applied computational enumeration with our AutoDesigner algorithm to generate novel design ideas. Most notably, this effort has helped to identify a novel subpocket for further SAR development on the project. As is common for CNS programs, the challenge is to balance compound potency with desirable PK/brain penetration properties. While a working model to predict PK/brain penetration has been elusive on this project, we will continue to apply the Schrödinger computational modeling technology along with drug-likeness and CNS MPO filters to prioritize compounds for synthesis. Further optimization work toward a development candidate will be reported in due course.