Hit to Lead Design of Novel D-amino-acid Oxidase Inhibitors using a Comprehensive Digital Chemistry Strategy

Computational platform grounded in highly accurate predictive models enables team-based discovery of a novel chemical series engaging a complex CNS target.


Inhibition of D-amino-acid oxidase (DAO) has been hypothesized as a potential therapeutic strategyy for schizophrenia. Schrödinger’s Drug Discovery Team engaged in a discovery effort with a collaborator to identify novel DAO inhibitors with potential best-in-class properties.

Program Challenges

  • Identify novel chemical matter while striving for best-in-class molecules that cross the blood-brain-barrier
  • Simultaneously optimize drug-like properties, improve CNS exposure, and affinity


The Drug Discovery Team deployed a large-scale digital chemistry strategy leveraging:

  • A centralized project data platform to facilitate knowledge-based medicinal chemistry design collaboration (LiveDesign, AutoQSAR)
  • Physics-based methods to predict affinity and prioritize design ideas for synthesis (FEP+)
  • Computationally-driven ideation and scoring workflow to amplify common enumeration strategies and screen hundreds of millions of compounds using machine learning coupled with physics-based free energy methods (FEP+, AutoDesigner)


The team discovered a novel class of DAO inhibitors with desirable drug-like properties by confidently exploring synthetically-challenging chemistry. The team also identified a previously unexplored subpocket for further evaluation. The novelty of the compounds, coupled with well-balanced properties, demonstrates the extraordinary power of the approach to unleash project team creativity. By leveraging a digital platform, the team explored vast chemical space while simultaneously optimizing for drug-like properties in a challenging disease area.

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