Learning to Navigate the Synthetically Accessible Chemical Space Using Reinforcement Learning

Jul 12, 2020



Over the last decade, there has been significant progress in the field of machine learning-based de novo drug discovery, particularly in generative modeling of chemical structures. However, current generative approaches exhibit a significant challenge: they do not ensure the synthetic accessibility nor provide the synthetic routes of the proposed small molecules which limits their applicability. In this work, we propose a novel reinforcement learning (RL) setup for drug discovery that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo compound design system. In this setup, the agent learns to navigate through the immense synthetically accessible chemical space by subjecting initial commercially available molecules to valid chemical reactions at every time step of the iterative virtual synthesis process. The proposed environment for drug discovery provides a highly challenging test-bed for RL algorithms owing to the large state space and high-dimensional continuous action space with hierarchical actions. Our end-to-end approach achieves state-of-the-art performance when compared against other generative approaches for drug discovery. Moreover, we leverage our approach in a proof-of-concept that mimics the drug discovery process by generating novel HIV drug candidates. Finally, we describe how the end-to-end training conceptualized in this study represents an important paradigm in radically expanding the synthesizable chemical space and automating the drug discovery process.



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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