From atoms to mechanisms - Artificial Intelligence augmented molecular simulations for mechanistic ligand design

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While there have been numerous advances in computational methods aiding in rational drug design, most approaches so far take a static view of the drug target, ignoring the complexities associated with the dynamics and thermodynamics of conformational transitions. There is thus a pressing need for new computational methods that are accurate, tractable and automatable for large scale drug discovery and chemical biology studies that account for the changing nature of a generic drug target. Built on strong theoretical and computational preliminary results, this program seeks to understand and guide design of new inhibitors of tyrosine kinases and model riboswitches in a mechanistically guided paradigm. Our research program is driven by the central hypotheses that (a) mechanistically aware ligand design strategies can outperform traditional strategies guided only by structure, and (b) artificial intelligence (AI)- integrated molecular dynamics (MD) simulation methods can help learn mechanisms in a high-throughput fashion. Our program can be split into two overarching yet complementary thematic areas. In the first area, we will develop sampling algorithms at the interface of statistical mechanics, MD simulations and AI to probe mechanisms for rare event processes, such as drug unbinding and conformational change. Specifically we will build on our preliminary work in using ideas from neural information processing and natural language processing, and adapt them for advanced sampling methods that learn reaction coordinate, thermodynamics and kinetics on-the-fly as the simulation progresses. In addition, we will also be interacting closely with other leading computational groups to integrate our sampling methods with theirs and to facilitate efficient, accurate sampling for polarizable force-field development. In the second area, we will use our algorithms to guide mechanistically driven design of inhibitors of Src, Abl kinases and PreQ1 riboswitches. We will take a mechanistically driven perspective wherein we will map out the different conformations of a given target and understand how an existing ligand interacts with these, and then propose ligand modifications based upon this understanding. We will use our AI-augmented MD methods to understand the dissociation mechanisms of ligands in one uninterrupted “atoms to mechanism” workflow with minimal human intervention. All our predictions will be validated in different ways by our experimental collaborators at Stony Brook University and the National Cancer Institute