1993 Fiscal Year Final Research Report Summary
Prediction of Toxicity of Organic Chemicals Using Fuzzy Adaptive Least-Squares
| Project/Area Number |
03671030
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Physical pharmacy
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| Research Institution | Kitasato University |
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Principal Investigator |
MORIGUCHI Ikuo Kitasto University, School of Pharm. Sci.Professor, 薬学部, 教授 (90050343)
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| Co-Investigator(Kenkyū-buntansha) |
NAKAGOME Izumi Kitasato University, School of Pharm.Sci.Research Associate, 薬学部, 助手 (30237242)
YAMAOTSU Noriyuki Kitasato University, School of Pharm.Sci.Research Associate, 薬学部, 助手 (60230322)
MATSUSHITA Yasuo Kitasato University, School of Pharm.Sci.Assistant Professor, 薬学部, 講師 (40050653)
HIRONO Shuichi Kitasato University, Schol of Pharm.Sci.Assoociate Professor, 薬学部, 助教授 (30146328)
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| Project Period (FY) |
1991 – 1993
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| Keywords | Quantitative structure-toxicity relationship / Toxicity prediction / Aquatic toxicity / Human acute toxicity / Carcinogenicity / Mutagenicity / Biodegradability / 生分解性 |
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| Research Abstract |
Fuzzy adatve lest-squares (FAS), a pattern recognition method for analysing structure-activity rating data to generate QSAR models, was developed. A novel feature of FALS is that the degree to which each sample belongs to its activity class is given by a fuzzy membership funciton. Using FALS, non-congeneric QSAR anayses of data of miscellaneous organic chemicals were performed to construct predictive models of toxicity and safety : aquatic acute toxicity (394 compunds), human oral acute toxicity (504 compounds), rodent carcinogenicity (246 compunds), Ames Salmonella mutagenicity (244 compounds), and biodegradability (1259 compounds). In these QSAR analyzes, the values of og P (partition coefficient in octanol/water) caculated by a newly-developed method were used as the descriptor for hydrophobicity, together with diverse substructural descriptors automatically generated using extended chemical graphs by a computer. Satisfactory QSAR models, reliable in both recognition and leave-one-out prediction, were obtained by the FALS analyzes.
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