D-Serine is a co-agonist of the N-methyl D-aspartate (NMDA) receptor, a key excitatory neurotransmitter receptor. In the brain, D-Serine is synthesized from its L-isomer by serine racemase and is metabolized by the D-amino acid oxidase (DAO, DAAO), a flavoenzyme that catalyzes the oxidative degradation of D-amino acids including D-serine to the corresponding α-keto acids. Many studies have linked decreased D-serine concentration and/or increased DAO expression and enzyme activity to NMDA dysfunction and schizophrenia. Thus, many companies have explored the possibility of employing DAO inhibitors for the treatment of schizophrenia and other indications. Powered by the Schrödinger computational modeling platform, we initiated a research program to identify novel DAO inhibitors with best-in-class properties. The program execution leveraged an hDAO FEP+ model to prospectively predict compound hDAO inhibitory potency and prioritize design ideas from both human design and computer enumeration by our AutoDesigner algorithm. A novel class of DAO inhibitors with desirable pharmacokinetic and brain penetration properties was discovered from this effort. In an in vivo mouse PK/PD model, tool compound 37 demonstrated modulation of D-serine concentrations in the plasma and brain through inhibition of DAO function. Continued SAR work has led to significant potency improvement in both DAO biochemical and cell assays. Our modeling technology on this program has not only enhanced the efficiency of medicinal chemistry execution, it has also helped to identify a previously unexplored subpocket for further SAR development.