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Jesse Weller, Remo Rohs (University of Southern California, 1050 Childs Way, Los Angeles, CA 90089)
Rapid advancement in the computational methods of structure-based drug design has led to their widespread adoption as key tools in the early drug development process. Recently, the remarkable growth of available crystal structure data and libraries of commercially available or readily synthesizable molecules have unlocked previously inaccessible regions of chemical space for drug development. Paired with the improvement in virtual ligand screening methods, these libraries are having a significant impact on the success of early drug design. However, screening-based approaches are limited in their scalability due to computational limits and the sheer scale of drug-like space. Significant advances in deep generative modeling are extending the reach of molecular design beyond classical methods by learning the fundamental intra- and inter-molecular relationships in drug-target systems from existing data. We introduce a deep structure-based generative model that enables fine-grained control over molecular generation. We demonstrate the capacity to accelerate a wide range of common drug design tasks, improving over previous methods on de novo generation, molecular optimization, scaffold