Deep Reinforcement Learning for De-Novo Drug Design
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We propose a novel computational strategy based on deep and reinforcement learning techniques for de-novo 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 employed this integrative strategy to design chemical libraries biased toward compounds with either maximal, minimal, or specific range of physical properties, such as melting point and hydrophobicity, as well as to develop novel putative inhibitors of JAK2. This new approach can find a general use for generating targeted chemical libraries optimized for a single desired property or multiple properties.
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