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Poster
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
Sai Krishna Gottipati · Boris Sattarov · Sufeng Niu · Yashaswi Pathak · Haoran Wei · Shengchao Liu · Shengchao Liu · Simon Blackburn · Karam Thomas · Connor Coley · Jian Tang · Sarath Chandar · Yoshua Bengio

Thu Jul 16 09:00 AM -- 09:45 AM & Thu Jul 16 08:00 PM -- 08:45 PM (PDT) @
in Poster Session 44 »

Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in generative modeling of novel chemical structures. However, current generative approaches exhibit a significant challenge: they do not ensure that the proposed molecular structures can be feasibly synthesized nor do they provide the synthesis routes of the proposed small molecules, thereby seriously limiting their practical applicability. In this work, we propose a novel reinforcement learning (RL) setup for de novo drug design: Policy Gradient for Forward Synthesis (PGFS), that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo drug 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. PGFS achieves state-of-the-art performance in generating structures with high QED and logP. Moreover, we put to test PGFS in an in-silico proof-of-concept associated with three HIV targets, and the candidates generated with PGFS outperformed the existing benchmarks in optimizing the activity against the biological targets. 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.

Keywords: Algorithms · Deep Reinforcement Learning · Healthcare · Reinforcement Learning · Applications - Neuroscience, Cognitive Science, Biology and Health

Author Information

Sai Krishna Gottipati (99andBeyond)

Reinforcement learning, multi agents, human-in-the-loop etc.. makers of cogment.ai

Boris Sattarov (99andBeyond)
Sufeng Niu (Linkedin)
Yashaswi Pathak (International Institute of Information Technology,Hyderabad)
Haoran Wei (University of Delaware)
Shengchao Liu (MILA-UdeM)
Shengchao Liu (Mila, Université de Montréal)
Simon Blackburn (Mila)
Karam Thomas (99andBeyond)
Connor Coley (MIT)
Jian Tang (HEC Montreal & MILA)
Sarath Chandar (Mila / École Polytechnique de Montréal)
Yoshua Bengio (Montreal Institute for Learning Algorithms)

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