New Methods and Tools for Computational Drug Discovery

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Project Summary My goal is to develop effective and efficient computational methods for drug discovery, apply these methods to find new and efficacious drugs to treat diseases, and deploy these methods in easy-to-use open source tools. My research group pioneered the development and integration of deep neural networks in user-friendly molecular docking software for structure-based drug design to predict poses and potency of small molecules binding to their molecular targets. We will build on our foundational work by using deep learning to simultaneously solving the scoring and sampling problems, which will overcome scalability limitations inherent in current approaches. We propose to develop the first deep generative models for structure-based drug design. Unlike tra- ditional screening, generative modeling is not limited to a predefined chemical space. In generative mod- eling, a deep neural network learns an underlying distribution of molecular structures and properties represented as a latent space. New structures can be extracted from this learned latent space to have desirable properties. Ideally, a generative model will produce novel, near-optimal molecular structures almost instantaneously. We hypothesize that training generative models using existing 3D protein and ligand structures will allow us to create general models that can be productively applied to new, struc- turally enabled targets due to the richness and universality of protein-ligand interactions. We will further develop these methods to support the generation of optimized lead candidates, where the generative process is updated to include results from experimental assays as the drug discovery process progresses. We will continually apply our methods to identify small molecule modulators of molecular interac- tions relevant to normal physiology and disease. For example, using our current tools, we identified the first inhibitors of the profilin-actin interaction, an anti-angiogenesis target with relevance to cancer and diabetic retinopathy, and we plan to further improve these compounds with the goal of identifying candi- dates for clinical testing. We will apply our methods to address other under-explored molecular targets, such as NFATc2, which is implicated in cancer and autoimmune diseases. These prospective applications of our methods will provide unbiased and realistic evaluations that further inform their development. Finally, all of our code and trained deep neural network models will be deployed either as new tools for generative modeling or as enhancements to our widely used open source tools for computational drug discovery: (1) PHARMIT, an interactive web application for structure-based drug discovery; (2) GNINA, a C/C++ deep learning framework for molecular docking; and (3) the newly released LIBMOLGRID, a Python library for accelerated molecular gridding that integrates with popular deep learning toolkits. These tools and methods will make the drug discovery process more accessible and efficient.