ChemTS: an efficient python library for de novo molecular generation

Science and Technology of Advanced Materials

Volumn 18, Issue 1, 2017, Pages 972-976

  Yang, Xiufeng ^a   Zhang, Jinzhe ^b   Yoshizoe, Kazuki ^c   Terayama, Kei ^a   Tsuda, Koji ^a,c,d  

^a UNIVERSITY OF TOKYO   (Japan)

^b UNIVERSITÉ DE LYON   (France)

^c Disaster Resilience Science Team   (Japan)

^d NATIONAL INSTITUTE FOR MATERIALS SCIENCE   (Japan)

Author keywords

Molecular design; Monte Carlo tree search; python library; recurrent neural network

Indexed keywords

ARTIFICIAL INTELLIGENCE; DEEP NEURAL NETWORKS; HIGH LEVEL LANGUAGES; MOLECULES; MONTE CARLO METHODS; OPTIMIZATION;

BENCHMARKING PROBLEMS; BLACK-BOX OPTIMIZATION; MOLECULAR DESIGN; MONTE-CARLO TREE SEARCHES; NEURAL NETWORK MODEL; OCTANOL-WATER PARTITION COEFFICIENT; ORGANIC MATERIALS; RECURRENT NEURAL NETWORK (RNNS);

RECURRENT NEURAL NETWORKS;

EID: 85035780298     PISSN: 14686996     EISSN: 18785514     Source Type: Journal    
DOI: 10.1080/14686996.2017.1401424     Document Type: Article

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