[KDD 2020] Molecular Inverse-Design Platform for Material Industries

[KDD 2020] Molecular Inverse-Design Platform for Material Industries

Dec 16, 2020
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,Th,e d,iscovery of new material,s, ,has been the essential force which ,brings ,a, ,discontinuous improvement to industrial products’ ,performance., ,However, ,the ,extra,vast combinatorial ,design space, ,of material structures exceeds human experts’ capability to ,explore all,, ,thereby hampering material development. ,In this ,paper, we present ,a, ,material industry,oriented web platform of ,an ,AI,driven molecular inverse,design system, ,w,hich automatically ,designs brand new molecular structures rapidly and diversely. ,Di,ff,erent from existing inverse,design ,solutions,, i,n this system, ,the combination of substructure,based feature encoding and ,molecular graph generation algorithms allows a use,r to gain high,speed, interpretable, and customizable design process. Also, ,a ,hierarchical data structure and user,oriented UI provide ,a ,fl,exible ,and intuitive work,fl,ow. ,Th,e system is deployed on IBM’s and our ,client’s cloud servers, ,and has been used by 5 ,partner companies. ,To illustrate, ,actual industrial use cases, we exhibit inverse,design ,of sugar and dye molecules, that were carried out by, ,experimental ,chemists in those client companies. Compared to a general human ,chemist’s standard performance, the mo,lecular design speed was ,accelerated more than 10 times, and greatly increased variety was ,observed in the inverse,designed molecules without loss of ,chemical realism., ,CCS CONCEPT, ,•, ,Applied computing, ,→,Physical science and engineering ,→,Chemistry,;, ,•, ,Computing methodologies, ,→, ,Machine learning ,→, ,Machine ,learning ,algorithms ,→,Feature ,selection,;, ,•, ,Mathematics of computing, ,→, ,Discrete mathematics ,→,Graph theory ,→, ,Graph enumeration,;, , , KEYWORDS , ,Cheminformatics;, ,Bioinformatics; ,Feature ,engineering; ,Generative models, ,ACM Reference format:, ,Seiji Takeda, Toshiyuki Hama, Hsiang,Han Hsu, Victoria A. ,Piunova, Dmitry Zubarev, Daniel P. Sanders, Jed W. Pitera, ,Makoto Kogoh, Takumi Hongo, Yenwei Cheng, Wolf Bocane,tt,, ,Hideaki Nakashika, Akihiro Fujita, Yuta Ts,uchiya, Katsuhiko ,Hino, Kentaro Yano, Shuichi Hirose, Hiroki Toda, Yasumitsu Orii, ,Daiju Nakano. ,Molecular Inverse,Design Platform for Material ,Industries. ,Th,e 26th ACM SIGKDD International Conference on ,Knowledge Discovery & Data Mining (KDD’20,), USA. 9 p,ages., ,https://doi.org/10.1145/3394486.3403346, , ,1.,

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