Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning

@article{Horwood2020MolecularDI,
  title={Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning},
  author={Julien Horwood and Emmanuel Noutahi},
  journal={ACS Omega},
  year={2020},
  volume={5},
  pages={32984 - 32994}
}
The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria. Despite recent progress, we argue that existing generative methods are limited in their ability to favorably shift the distributions of molecular properties during optimization. We instead propose a novel Reinforcement Learning framework for molecular design in which an agent learns to directly optimize through a space of synthetically… 

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