22 citations · 28 across the 4 of their papers we have counts for
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physics.chem-ph2018
Population-based de novo molecule generation, using grammatical evolution
Naruki Yoshikawa, Kei Terayama, Teruki Honma +2
Automatic design with machine learning and molecular simulations has shown a remarkable ability to generate new and promising drug candidates. Current models, however, still have p…
physics.chem-ph2017
ChemTS: An Efficient Python Library for de novo Molecular Generation
Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe +2
Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of…