De Novo Drug Design by Iterative Multi-Objective Deep Reinforcement Learning with Graph-based Molecular Quality Assessment
Generating molecules of high quality and drug-likeness in the vast chemical space is a big challenge in the drug discovery. Most existing molecule generative methods focus on diversity and novelty of molecules, but ignoring drug potentials of the generated molecules during the generation process.
In this study, we present a novel de novo multi-objective quality assessment-based drug design approach QADD, which integrates an iterative refinement framework with a novel graph-based molecule quality assessment model on drug potentials. QADD designs a multi-objective deep reinforcement learning pipeline to generate molecules with multiple desired properties iteratively, where a graph neural network-based model for accurate molecular quality assessment on drug potentials is introduced to guide molecule generation. Experimental results show that QADD can jointly optimize multiple molecular properties with a superior performance to the state-of-the-art methods and the quality assessment module guides the generated molecules with high drug potentials. Furthermore, we successfully apply QADD to generate novel molecules binding to a biological target protein DRD2.
Figure. The pipeline of the proposed method QADD. The multi-objective deep reinforcement learning model estimates the value function of the generated molecules and chooses the most appropriate action at each step to maximize the discounted return. The QAscore scored by the molecule quality assessment model serves as one of the reward functions of the multi-objective deep reinforcement learning model, whose generated molecules are fed back to retrain the GNN-based QA model iteratively. Finally, the generated molecules are further modified using the functional group modification.
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