Open AccessMolecular Design Method Using a Reversible Tree Representation of Chemical Compounds and Deep Reinforcement Learning
Author(s) -
Ryuichiro Ishitani,
Toshiki Kataoka,
Kentaro Rikimaru
Publication year - 2022
Publication title -
journal of chemical information and modeling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 160
eISSN - 1549-960X
pISSN - 1549-9596
DOI - 10.1021/acs.jcim.2c00366
Subject(s) - reinforcement learning , computer science , tree (set theory) , benchmark (surveying) , representation (politics) , process (computing) , artificial intelligence , drug discovery , tree structure , machine learning , chemistry , data structure , mathematics , politics , political science , law , mathematical analysis , biochemistry , geodesy , programming language , geography , operating system
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