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Geom-SAC: Geometric Multi-Discrete Soft Actor Critic With Applications in De Novo Drug Design | IEEE Journals & Magazine | IEEE Xplore

Geom-SAC: Geometric Multi-Discrete Soft Actor Critic With Applications in De Novo Drug Design


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 More
Society Section: IEEE Engineering in Medicine and Biology Society Section

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.
Society Section: IEEE Engineering in Medicine and Biology Society Section
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)
Page(s): 45519 - 45529
Date of Publication: 18 March 2024
Electronic ISSN: 2169-3536

Funding Agency:


References

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