Geom-SAC: Geometric multi-discrete soft actor critic with applications in de novo drug design
Document Type
Article
Publication Date
Winter 3-18-2024
Abstract
Finding new molecules with desirable properties has high computational and overhead costs. Much research has focused on generating candidate molecules in one- and two-dimensional spaces, which has produced some favorable results. However, extending these approaches to molecules in three-dimensional space would be far more useful because the representation of molecules is more realistic, although three-dimensional methods have much higher computational costs. In this work, we developed a geometric deep reinforcement learning agent that generates and optimizes molecules that could interact with a biochemical target. The agent can be used for generating molecules from scratch or for lead optimization when it enhances the properties of a given molecule, whether by enhancing its drug-likeness or increasing its activity toward the target via implicit learning. Thus, the agent works with molecules in three-dimensional space without high computational costs.
Recommended Citation
A. Abdallah et al., "Geom-SAC: Geometric Multi-Discrete Soft Actor Critic With Applications in De Novo Drug Design," in IEEE Access, vol. 12, pp. 45519-45529, 2024, doi: 10.1109/ACCESS.2024.3377289