Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds

Deep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties. Many deep learning approaches employ reinforcement learning for optimizing the target properties of the generated molecules. However, the success of this approach is often hampered by the problem of sparse rewards as the majority of the generated molecules are expectedly predicted as inactives. We propose several technical innovations to address this problem and improve the balance between exploration and exploitation modes in reinforcement learning. In a proof-of-concept study, we demonstrate the application of the deep generative recurrent neural network architecture enhanced by several proposed technical tricks to design inhibitors of the epidermal growth factor (EGFR) and further experimentally validate their potency. The proposed technical solutions are expected to substantially improve the success rate of finding novel bioactive compounds for specific biological targets using generative and reinforcement learning approaches. Deep generative neural networks are increasingly exploited for drug discovery, but often the majority of generated molecules are predicted to be inactive. Here, an optimized protocol for generative models with reinforcement learning is derived and applied to design potent epidermal growth factor inhibitors.

Automated summary

This study proposes an optimized protocol for generative models and reinforcement learning to design novel bioactive compounds for specific biological targets, aiming to improve the accuracy of activity prediction for generated molecules.