Batch or semi-batch processes have been of great use in various industrial chemical plants. For efficiently monitoring such processes, soft-sensor models can be employed. Many of previously proposed soft-sensor models assumed that objective variable values for model construction can be available at any time during process operation. However, in many chemical plants, it is difficult to sample product from the ongoing process due to such extreme reaction conditions as high pressure and temperature. Therefore, understanding the relationship between time-series soft-sensor model’s predictability and the number of sampling points is important. In the present work, we clarified this relationship using simulation datasets, which can be easily reproduced. When sampling points were scarce, data augmentation strategy was also found to be effective. Soft-sensor models can be effectively built using sampling points in the early phase of the process. These findings were applied to build a soft-sensor model of an industrial semi-batch process.
{"title":"Soft-Sensor Modeling for Semi-Batch Chemical Process Using Limited Number of Sampling","authors":"S. Aoshima, Tomoyuki Miyao, K. Funatsu","doi":"10.2751/jcac.20.119","DOIUrl":"https://doi.org/10.2751/jcac.20.119","url":null,"abstract":"Batch or semi-batch processes have been of great use in various industrial chemical plants. For efficiently monitoring such processes, soft-sensor models can be employed. Many of previously proposed soft-sensor models assumed that objective variable values for model construction can be available at any time during process operation. However, in many chemical plants, it is difficult to sample product from the ongoing process due to such extreme reaction conditions as high pressure and temperature. Therefore, understanding the relationship between time-series soft-sensor model’s predictability and the number of sampling points is important. In the present work, we clarified this relationship using simulation datasets, which can be easily reproduced. When sampling points were scarce, data augmentation strategy was also found to be effective. Soft-sensor models can be effectively built using sampling points in the early phase of the process. These findings were applied to build a soft-sensor model of an industrial semi-batch process.","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69255946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prof. Kimito Funatsu received the Honor Award in Division of Chemoinformatics, the Chemical Society of Japan in the 42th Annual Meeting of Chemoinformatics held on Nov. 28 th 2019. The awarding recognizes his significant contributions in the development of the cheminformatics discipline in the world as well as in Japan. His research efforts extend over multiple domains such as (i) system development including elucidation of chemical structures and prediction of organic reactions, (ii) quantitative structure activity relationship (QSAR), (iii) quantitative structure property relationship (QSPR), and (iv) international collaborations in chemoinformatics. In the present review, we focus on chemoinformatics in the world as well as in Japan based on “Special issue dedicating to Honor Award: Prof. Kimito Funatsu”, which consists of five invited papers by the world-famous distinguished foreign researchers, and six papers from domestic researchers. Taking these papers into consideration, we try to discuss the meanings of the Honor Award dedicating to Prof. Kimito Funatsu.
{"title":"[Special Issue for Honor Award dedicating to Prof Kimito Funatsu](Mini-review)Meanings of the Honor Award for Prof Kimito Funatsu","authors":"M. Sugimoto, K. Hori, S. Kanaya","doi":"10.2751/jcac.20.23","DOIUrl":"https://doi.org/10.2751/jcac.20.23","url":null,"abstract":"Prof. Kimito Funatsu received the Honor Award in Division of Chemoinformatics, the Chemical Society of Japan in the 42th Annual Meeting of Chemoinformatics held on Nov. 28 th 2019. The awarding recognizes his significant contributions in the development of the cheminformatics discipline in the world as well as in Japan. His research efforts extend over multiple domains such as (i) system development including elucidation of chemical structures and prediction of organic reactions, (ii) quantitative structure activity relationship (QSAR), (iii) quantitative structure property relationship (QSPR), and (iv) international collaborations in chemoinformatics. In the present review, we focus on chemoinformatics in the world as well as in Japan based on “Special issue dedicating to Honor Award: Prof. Kimito Funatsu”, which consists of five invited papers by the world-famous distinguished foreign researchers, and six papers from domestic researchers. Taking these papers into consideration, we try to discuss the meanings of the Honor Award dedicating to Prof. Kimito Funatsu.","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2751/jcac.20.23","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69255969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]Molecular Design With Long Short-Term Memory Networks","authors":"F. Grisoni, G. Schneider","doi":"10.2751/jcac.20.35","DOIUrl":"https://doi.org/10.2751/jcac.20.35","url":null,"abstract":"","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69256020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]Exploring Polypharmacology and Molecular Promiscuity","authors":"J. Bajorath","doi":"10.2751/jcac.20.43","DOIUrl":"https://doi.org/10.2751/jcac.20.43","url":null,"abstract":"","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69256050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuki Sugawara, Masaaki Kotera, Kenichi Tanaka, K. Funatsu
Fluorescent substances are used in a wide range of applications, and the method that effectively design molecules having desirable absorption and emission wavelength is required. In this study, we used boron-dipyrromethene (BODIPY) compounds as a case study, and constructed high precision wavelength prediction model using ensemble learning. Prediction accuracy improved in stacking model using RDKit descriptors and Morgan fingerprint. The variables related to the molecular skeleton and the conjugation length were shown to be important. We also proposed an applicability domain (AD) estimation model that directly use the descriptors based on Tanimoto distance. The performance of the AD models was shown better than the OCSVM-based model. Using our proposed stacking model and AD model, newly generated compounds were screened and we obtained 602 compounds which were estimated inside the AD in both absorption wavelength and emission wavelength.
{"title":"Ensemble Machine Learning and Applicability Domain Estimation for Fluorescence Properties and its Application to Structural Design","authors":"Yuki Sugawara, Masaaki Kotera, Kenichi Tanaka, K. Funatsu","doi":"10.2751/JCAC.20.7","DOIUrl":"https://doi.org/10.2751/JCAC.20.7","url":null,"abstract":"Fluorescent substances are used in a wide range of applications, and the method that effectively design molecules having desirable absorption and emission wavelength is required. In this study, we used boron-dipyrromethene (BODIPY) compounds as a case study, and constructed high precision wavelength prediction model using ensemble learning. Prediction accuracy improved in stacking model using RDKit descriptors and Morgan fingerprint. The variables related to the molecular skeleton and the conjugation length were shown to be important. We also proposed an applicability domain (AD) estimation model that directly use the descriptors based on Tanimoto distance. The performance of the AD models was shown better than the OCSVM-based model. Using our proposed stacking model and AD model, newly generated compounds were screened and we obtained 602 compounds which were estimated inside the AD in both absorption wavelength and emission wavelength.","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2751/JCAC.20.7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69256337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]Similarity, Diversity - Chemoinformatics","authors":"K. Varmuza","doi":"10.2751/jcac.20.29","DOIUrl":"https://doi.org/10.2751/jcac.20.29","url":null,"abstract":"","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69255981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]Kimito Funatsu – Driving Force of Japanese-French Collaboration in Chemoinformatics","authors":"A. Varnek","doi":"10.2751/jcac.20.47","DOIUrl":"https://doi.org/10.2751/jcac.20.47","url":null,"abstract":"","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69255810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
a Division of Materials Science, Graduate School of Science and Technology for Innovation, Yamaguchi University, Tokiwadai, Ube 755-8611, Japan. Laboratory for Computational Molecular Design, RIKEN Center for Biosystems Dynamics Research (BDR), 6-2-4 Furuedai, Suita, Osaka, 565-0874, Japan Division of Earth Science, Biology, and Chemistry, Graduate School of Science and Technology for Innovation, Yamaguchi University, Yamaguchi, 753-8512, Japan
{"title":"[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]Fast Evaluation of Potential Synthesis Routes Using Transition State Database(TSDB)","authors":"K. Hori, A. Hasegawa, N. Okimoto, S. Yamazaki","doi":"10.2751/jcac.20.50","DOIUrl":"https://doi.org/10.2751/jcac.20.50","url":null,"abstract":"a Division of Materials Science, Graduate School of Science and Technology for Innovation, Yamaguchi University, Tokiwadai, Ube 755-8611, Japan. Laboratory for Computational Molecular Design, RIKEN Center for Biosystems Dynamics Research (BDR), 6-2-4 Furuedai, Suita, Osaka, 565-0874, Japan Division of Earth Science, Biology, and Chemistry, Graduate School of Science and Technology for Innovation, Yamaguchi University, Yamaguchi, 753-8512, Japan","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69255872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nobutaka Wakamatsu, Ming Huang, N. Ono, M. Altaf-Ul-Amin, S. Kanaya
{"title":"[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]Prediction of Metabolite Activities by Repetitive Clustering of the Structural Similarity Based Networks","authors":"Nobutaka Wakamatsu, Ming Huang, N. Ono, M. Altaf-Ul-Amin, S. Kanaya","doi":"10.2751/jcac.20.76","DOIUrl":"https://doi.org/10.2751/jcac.20.76","url":null,"abstract":"","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69256387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sugimoto Manabu, Ide Toshihiro, Algafari Bakti Manggara, Y. Kazuki, Inoue Takafumi
{"title":"[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]An Electronic-Structure Informatics Study on Inhibitory Activity of Natural Products against Fatty Acid Synthase","authors":"Sugimoto Manabu, Ide Toshihiro, Algafari Bakti Manggara, Y. Kazuki, Inoue Takafumi","doi":"10.2751/jcac.20.65","DOIUrl":"https://doi.org/10.2751/jcac.20.65","url":null,"abstract":"","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69256326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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