De novo Drug Design using Reinforcement Learning with Multiple GPT
  Agents
Xiuyuan Hu Guoqing Liu Yang Zhao Hao Zhang
De novo drug design is a pivotal issue in pharmacology and a new area of
focus in AI for science research. A central challenge in this field is to
generate molecules with specific properties while also producing a wide range
of diverse candidates. Although advanced technologies such as transformer
models and reinforcement learning have been applied in drug design, their
potential has not been fully realized. Therefore, we propose MolRL-MGPT, a
reinforcement learning algorithm with multiple GPT agents for drug molecular
generation. To promote molecular diversity, we encourage the agents to
collaborate in searching for desirable molecules in diverse directions. Our
algorithm has shown promising results on the GuacaMol benchmark and exhibits
efficacy in designing inhibitors against SARS-CoV-2 protein targets. The codes
are available at: https://github.com/HXYfighter/MolRL-MGPT.