Prediction for Biodegradability of Chemicals by Kernel Partial Least Squares

DOI Web Site 1 Citations 13 References
  • Hiromatsu Koichi
    Kurume Laboratory, Chemicals Evaluation and Research Institute
  • Takahara Jun-ichi
    Graduate School of Pharmaceutical Sciences, Osaka University Drug Development Research Laboratories, Kyoto R&D Center, Maruho Co., Ltd.
  • Nishihara Tsutomu
    School of Pharmacy, Hyogo University of Health Sciences Graduate School of Engineering, Osaka University
  • Okamoto Kousuke
    Graduate School of Pharmaceutical Sciences, Osaka University
  • Yasunaga Teruo
    Research Collaboration Center on Emerging and Re-emerging Infections Genome Information Research Center, Research Institute for Microbial Diseases
  • Ohmayu Yoshihiro
    Graduate School of Pharmaceutical Sciences, Osaka University
  • Takagi Tatsuya
    Graduate School of Pharmaceutical Sciences, Osaka University Research Collaboration Center on Emerging and Re-emerging Infections Genome Information Research Center, Research Institute for Microbial Diseases
  • Nakazono Kingo
    Kurume Laboratory, Chemicals Evaluation and Research Institute

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Other Title
  • Kernel Partial Least Squaresによる化学物質の生分解性予測

Description

We predicted the biodegradability of 554 chemicals by using a nonlinear partial least squares (PLS) method, called kernel PLS (KPLS), and compared the predictive performance of KPLS and that of linear PLS, which is widely used for modeling structure-activity relationships. Moreover, prediction using support vector machine (SVM) was performed to confirm the utility of KPLS. The KPLS models correctly categorized 429 (77.4%), 443 (80.0%) and 454 (81.9%) chemicals out of 554, whereas the PLS models were correct for 419 (75.6%), 434 (78.3%) and 439 (79.2%) in cases of using 6, 50 and 89 descriptors, respectively, based on the chemical structures of chemicals analyzed in this study. By properly tuning the necessary parameters, KPLS showed better predictive performance for the biodegradability of chemicals than PLS and SVM did, which showed 79.6% accuracy with 89 descriptors.

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