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The viability of a new drug molecule is a time and resource intensive taskthat makes computer-aided assessments a vital approach to rapid drug discovery.Here we develop a machine learning algorithm, iMiner, that generates novelinhibitor molecules for target proteins by combining deep reinforcementlearning with real-time 3D molecular docking using AutoDock Vina, therebysimultaneously creating chemical novelty while constraining molecules for shapeand molecular compatibility with target active sites. Moreover, through the useof various types of reward functions, we can generate new molecules that arechemically similar to a target ligand, which can be grown from known proteinbound fragments, as well as to create molecules that enforce interactions withtarget residues in the protein active site. The iMiner algorithm is embedded ina composite workflow that filters out Pan-assay interference compounds,Lipinski rule violations, and poor synthetic accessibility, with options forcross-validation against other docking scoring functions and automation of amolecular dynamics simulation to measure pose stability. Because our approachonly relies on the structure of the target protein, iMiner can be easilyadapted for future development of other inhibitors or small moleculetherapeutics of any target protein.

Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design