Published August 21, 2022
| Version v1
Journal article
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RJT-RL: De novo molecular design using a Reversible Junction Tree and Reinforcement Learning
Description
Datasets and model weights used in the paper, "Molecular design method using a reversible tree representation of chemical compounds and deep reinforcement learning" (https://doi.org/10.1021/acs.jcim.2c00366), by Ryuichiro Ishitani, Toshiki Kataoka, Kentaro Rikimaru.
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