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De novo generation of optically active small organic molecules using Monte Carlo tree search combined with recurrent neural network
Author(s) -
Tashiro Motomichi,
Imamura Yutaka,
Katouda Michio
Publication year - 2021
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26441
Subject(s) - monte carlo method , monte carlo tree search , python (programming language) , computer science , tree (set theory) , artificial neural network , organic molecules , dipole , statistical physics , recurrent neural network , molecule , artificial intelligence , algorithm , physics , mathematics , statistics , quantum mechanics , combinatorics , operating system
Optically active small organic molecules are computationally designed using the ChemTS python library developed by Tsuda and collaborators, which utilizes a combined Monte Carlo tree search (MCTS) and recurrent neural network model. Geometry optimization and excited‐state calculations are performed for each generated molecule, following which the excitation energy and dissymmetry factors are computed to evaluate the score function in the MCTS process. Using this procedure, molecules not contained in existing databases are generated. Molecules having either high dissymmetry factors or high transition dipole strengths can be generated depending on the choice of the score function. In a single trajectory with 100,000 trials, mutually similar high‐scoring molecules are generated frequently after the initial 15,000–20,000 trials. This indicates that it is better to sample high‐scoring molecules from several trajectories having a modest number of trials each than from a single trajectory having a large number of trials.