{"title":"Solvatochromism of 4-(diethylamino)-4’-nitroazobenzene: explanation based on CNDO/S calculation results","authors":"Tomoya Takada, H. Tachikawa","doi":"10.2751/jcac.22.8","DOIUrl":"https://doi.org/10.2751/jcac.22.8","url":null,"abstract":"","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69256513","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}
Eri Maeyama, Toru Yamaguchi, Michinori Sumimoto, K. Hori
The development of synthetic routes for functional chemicals has been heavily depending on experience and intuition of synthetic organic chemists. In case that target molecules have complex structures, there are many possible synthetic routes, and it is often difficult to determine which one should be adopted. In order to decrease synthesis routes for experiments, we introduced “in silico screening” which requires to search TSs for synthesis routes, we have proposed a method to locate the new TS structure of a target reaction by using TS structures in TSDB. However, this method seldom gives the most stable TS structure within possible conformers. That is, the stability of transition states (TS), reactants and products is highly dependent on initial structures used for optimization. Therefore, this method is likely to give inadequate data to compare calculated and measured values of other synthetic reactions. For these purposes, we have to find reaction mechanisms with the most stable TS and molecules involved in the reactions. In this paper, we proposed a method to search the most stable reaction pathway and applied it to the Pinner Pyrimidine reaction of ethyl 3-oxobutanoate and 3-ethoxypropanimidamide.
{"title":"A method to search the most stable reaction pathway and its application to the Pinner Pyrimidine Synthesis reaction","authors":"Eri Maeyama, Toru Yamaguchi, Michinori Sumimoto, K. Hori","doi":"10.2751/jcac.22.1","DOIUrl":"https://doi.org/10.2751/jcac.22.1","url":null,"abstract":"The development of synthetic routes for functional chemicals has been heavily depending on experience and intuition of synthetic organic chemists. In case that target molecules have complex structures, there are many possible synthetic routes, and it is often difficult to determine which one should be adopted. In order to decrease synthesis routes for experiments, we introduced “in silico screening” which requires to search TSs for synthesis routes, we have proposed a method to locate the new TS structure of a target reaction by using TS structures in TSDB. However, this method seldom gives the most stable TS structure within possible conformers. That is, the stability of transition states (TS), reactants and products is highly dependent on initial structures used for optimization. Therefore, this method is likely to give inadequate data to compare calculated and measured values of other synthetic reactions. For these purposes, we have to find reaction mechanisms with the most stable TS and molecules involved in the reactions. In this paper, we proposed a method to search the most stable reaction pathway and applied it to the Pinner Pyrimidine reaction of ethyl 3-oxobutanoate and 3-ethoxypropanimidamide.","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69256450","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":"Extended Regression Modeling of the Toxicity of Phenol Derivatives to Tetrahymena pyriformis Using the Electronic-Structure Informatics Descriptor","authors":"Algafari Bakti Manggara, M. Sugimoto","doi":"10.2751/jcac.22.17","DOIUrl":"https://doi.org/10.2751/jcac.22.17","url":null,"abstract":"","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69256468","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}
In parallel to developments in Next-Generation Sequencing for cancer patient therapy decision making, personalized approaches to chemotherapy selection are also becoming desired. In an ideal situation, an individual's genomic, transcriptomic, and tumor-specific in-vitro response to chemical perturbation would be combined, and the US National Cancer Institute NCI-60 project has systematically screened a large chemical library against a variety of cell lines from various tumor types. Therefore, chemoinformatics approaches to make effective use of this data and identify the chemical and biological factors are of value. In this work, we investigate the impact of both chemical and biological descriptions of tumor response to chemical inhibition, and assess how well modeling approaches can predict tumor inhibition response on external datasets. We find that external datasets in both the classification and regression problems are reasonably well addressed, with the impact of chemical description outweighing the contribution from transcriptome or genome descriptions of tumors.
{"title":"Prediction of Compound Cytotoxicity Based on Compound Structures and Cell Line Molecular Characteristics","authors":"T. Nakano, J. B. Brown","doi":"10.2751/jcac.21.1","DOIUrl":"https://doi.org/10.2751/jcac.21.1","url":null,"abstract":"In parallel to developments in Next-Generation Sequencing for cancer patient therapy decision making, personalized approaches to chemotherapy selection are also becoming desired. In an ideal situation, an individual's genomic, transcriptomic, and tumor-specific in-vitro response to chemical perturbation would be combined, and the US National Cancer Institute NCI-60 project has systematically screened a large chemical library against a variety of cell lines from various tumor types. Therefore, chemoinformatics approaches to make effective use of this data and identify the chemical and biological factors are of value. In this work, we investigate the impact of both chemical and biological descriptions of tumor response to chemical inhibition, and assess how well modeling approaches can predict tumor inhibition response on external datasets. We find that external datasets in both the classification and regression problems are reasonably well addressed, with the impact of chemical description outweighing the contribution from transcriptome or genome descriptions of tumors.","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"21 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2751/jcac.21.1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69256407","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":"Prediction of Fish Acute Ecotoxicity of Inorganic and Ionized Chemical Substances by Machine Learning","authors":"Michiyoshi Takata, B. Lin, A. Terada, M. Hosomi","doi":"10.2751/jcac.20.104","DOIUrl":"https://doi.org/10.2751/jcac.20.104","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":"69255900","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}
Finding direct correlations between electronic structures of molecules and their properties, which we call “electronic-structure informatics”, is one of the challenging issues in chemoinformatics because the electronic degree of freedom is an essential factor determining the chemical characteristics. Herein we develop computational methods to automatically draw two types of orbital correlation diagrams. They are expected useful to perform machine learning including electronic degrees of freedom. In the present approach, we focus on electronic similarity called orbital similarity whose score is defined as spatial overlap between two molecular orbitals (MOs) enclosed with their iso-value surfaces. The similarity scores are also used to derive another orbital correlation diagram called “orbital interaction diagram”. This diagram is to relate MOs of a target molecule with those of its fragments. Through applications to benzene derivatives, these diagrams are shown to be reasonable, indicating potential usefulness of the present method in machine learning for quantitative predictions of molecular properties and chemical reactivities.
{"title":"[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]Automatic Drawing of Orbital Correlation Diagrams. A Computational Tool for Electronic-Structure Informatics","authors":"M. Sugimoto, Takafumi Inoue","doi":"10.2751/jcac.20.56","DOIUrl":"https://doi.org/10.2751/jcac.20.56","url":null,"abstract":"Finding direct correlations between electronic structures of molecules and their properties, which we call “electronic-structure informatics”, is one of the challenging issues in chemoinformatics because the electronic degree of freedom is an essential factor determining the chemical characteristics. Herein we develop computational methods to automatically draw two types of orbital correlation diagrams. They are expected useful to perform machine learning including electronic degrees of freedom. In the present approach, we focus on electronic similarity called orbital similarity whose score is defined as spatial overlap between two molecular orbitals (MOs) enclosed with their iso-value surfaces. The similarity scores are also used to derive another orbital correlation diagram called “orbital interaction diagram”. This diagram is to relate MOs of a target molecule with those of its fragments. Through applications to benzene derivatives, these diagrams are shown to be reasonable, indicating potential usefulness of the present method in machine learning for quantitative predictions of molecular properties and chemical reactivities.","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":"69255935","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 – The Driving Force of Chemoinformatics in Japan","authors":"J. Gasteiger","doi":"10.2751/jcac.20.32","DOIUrl":"https://doi.org/10.2751/jcac.20.32","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":"https://sci-hub-pdf.com/10.2751/jcac.20.32","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69255987","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}
Michiyoshi Takata, B. Lin, A. Terada, Masaaki Hosomia
{"title":"Predicting the Fish Chronic Ecotoxicity of Chemical Substance with New Ecotoxicity Fingerprint and Stacked Ensemble Method on Machine Learning","authors":"Michiyoshi Takata, B. Lin, A. Terada, Masaaki Hosomia","doi":"10.2751/jcac.20.111","DOIUrl":"https://doi.org/10.2751/jcac.20.111","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":"69255913","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}
T. Serino, Yoshizumi Takigawa, Sadao Nakamura, Ming Huang, N. Ono, Altaf-Ul-Amin, S. Kanaya
{"title":"[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]Chemoinformatics Approach for Estimating Recovery Rates of Pesticides in Fruits and Vegetables","authors":"T. Serino, Yoshizumi Takigawa, Sadao Nakamura, Ming Huang, N. Ono, Altaf-Ul-Amin, S. Kanaya","doi":"10.2751/jcac.20.92","DOIUrl":"https://doi.org/10.2751/jcac.20.92","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":"69256401","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}
T. Nakano, Yuji Mochidzuki, Kaori Fukuzawa, Yoshio Okiyama, C. Watanabe
{"title":"A Radical Correction for Inter Fragment Interaction Energy (IFIE) between Fragments Sharing Bond Detached Atom (BDA)","authors":"T. Nakano, Yuji Mochidzuki, Kaori Fukuzawa, Yoshio Okiyama, C. Watanabe","doi":"10.2751/JCAC.20.1","DOIUrl":"https://doi.org/10.2751/JCAC.20.1","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":"https://sci-hub-pdf.com/10.2751/JCAC.20.1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69255840","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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