RJT-RL: De novo molecular design using a Reversible Junction Tree and Reinforcement Learning
Ryuichiro Ishitani · Zenodo (CERN European Organization for Nuclear Research) · 2022
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.