We propose a novel computational strategy based on deep and reinforcement learning techniques for <i>de-novo</i> design of molecules with desired properties. This strategy integrates two deep neural networks – generative and predictive – that are trained separately but employed jointly to generate novel chemical structures with the desired properties. Generative models are trained to produce chemically feasible SMILES, and predictive models are derived to forecast the desired compound properties. In the first phase of the method, generative and predictive models are separately trained with supervised learning algorithms. In the second phase, both models are trained jointly with reinforcement learning approach to bias newly generated chemical structures towards those with desired physical and biological properties. In this proof-of-concept study, we have designed novel inhibitors of JAK2 and EGFR. Several top hits were synthesized and tested for kinase activity <i>in vitro</i>. This new approach can find a general use for generating targeted chemical libraries optimized for a single desired property or multiple properties.

AI driven design of novel kinase inhibitors

256th National Meeting (2018)

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