Top four molecules generated by Geom-SAC with QED of 0.948 embedded in 3D space via the MMFF94 algorithm.
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-dim...Show MoreMetadata
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.
Top four molecules generated by Geom-SAC with QED of 0.948 embedded in 3D space via the MMFF94 algorithm.
Published in: IEEE Access ( Volume: 12)