De novo Drug Design using Reinforcement Learning with Multiple GPT Agents
This work addresses the problem of generating diverse and property-specific drug molecules for pharmacology, representing an incremental advancement in AI-driven drug design.
The paper tackles the challenge of generating diverse molecules with specific properties in de novo drug design by proposing MolRL-MGPT, a reinforcement learning algorithm with multiple GPT agents that encourages collaboration for diverse searches, showing promising results on the GuacaMol benchmark and efficacy in designing SARS-CoV-2 inhibitors.
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.