NGT: Generative AI with Synthesizability Guarantees Identifies Potent Inhibitors for a G-protein Associated Melanocortin Receptor in a Tera-scale vHTS Screen

Commercially available, synthesis-on-demand virtual libraries contain upwards of trillions of readily synthesizable compounds for drug discovery campaigns. These libraries are a critical resource for rapid cycles of in silico discovery, property optimization and in vitro validation. However, as these libraries continue to grow exponentially in size, traditional search strategies that scale linearly with the number of compounds encounter significant limitations. Here we present NeuralGenThesis (NGT), an efficient reinforcement learning approach to retrieving compounds from ultra-large libraries that satisfy a set of user-specified constraints. Our method first trains a generative model over a virtual library and subsequently trains a normalizing flow to learn a distribution over latent space that decodes constraint-satisfying compounds. NGT allows multiple constraints without dictating how molecular properties are calculated, enabling versatile searches within virtual libraries. When NGT learned a policy reporting on compound bioactivity for the melanocortin- 2 receptor (MC2R) it prospectively identified potent and selective inhibitors in a three trillion compound library. NGT offers a powerful and scalable solution for navigating ultra-large virtual libraries, accelerating drug discovery efforts.