Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation

Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation

Abstract A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is using reinforcement learning to update a recurrent neural network or language-based...

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Bibliographic Details
Main Authors: Morgan Thomas, Noel M. O’Boyle, Andreas Bender, Chris de Graaf
Format: Article
Language:English
Published: BMC 2022-10-01
Series:Journal of Cheminformatics
Subjects:
Artificial intelligence
AI
Structure-based drug design
SBDD
Deep learning
Generative models
Online Access:https://doi.org/10.1186/s13321-022-00646-z

Internet

https://doi.org/10.1186/s13321-022-00646-z

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