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Neil, D., Segler, M., Guasch, L., Ahmed, M., Plumbley, D., Sellwood, M. and Brown, N. (2018) Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design. ICLR 2018 Conference, Vancouver, BC, 30 April-3 May 2018.

has been cited by the following article:

• TITLE: Exploring Local Chemical Space in De Novo Molecular Generation Using Multi-Agent Deep Reinforcement Learning

  AUTHORS: Wei Hu

  KEYWORDS: Multi-Agent Reinforcement Learning, Actor-Critic, Molecule Design, SARS-CoV-2, COVID-19

  JOURNAL NAME: Natural Science, Vol.13 No.9, September 16, 2021

  ABSTRACT: Single-agent reinforcement learning (RL) is commonly used to learn how to play computer games, in which the agent makes one move before making the next in a sequential decision process. Recently single agent was also employed in the design of molecules and drugs. While a single agent is a good fit for computer games, it has limitations when used in molecule design. Its sequential learning makes it impossible to modify or improve the previous steps while working on the current step. In this paper, we proposed to apply the multi-agent RL approach to the research of molecules, which can optimize all sites of a molecule simultaneously. To elucidate the validity of our approach, we chose one chemical compound Favipiravir to explore its local chemical space. Favipiravir is a broad-spectrum inhibitor of viral RNA polymerase, and is one of the compounds that are currently being used in SARS-CoV-2 (COVID-19) clinical trials. Our experiments revealed the collaborative learning of a team of deep RL agents as well as the learning of its individual learning agent in the exploration of Favipiravir. In particular, our multi-agents not only discovered the molecules near Favipiravir in chemical space, but also the learnability of each site in the string representation of Favipiravir, critical information for us to understand the underline mechanism that supports machine learning of molecules.