본문 바로가기
HOME> 논문 > 논문 검색상세

논문 상세정보

해당자료는 외국학술지지원센터(FRIC)에서 무료 원문복사신청서비스를 제공합니다.
Chemosphere v.172, 2017년, pp.249 - 259   SCI SCIE SCOPUS
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach

Abbasitabar, Fatemeh (Department of Chemistry, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran ); Zare-Shahabadi, Vahid (Department of Chemistry, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran );
  • 초록  

    Abstract Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure–toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis . A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for R t r a i n i n g 2 and R t e s t 2 , respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for R t r a i n i n g 2 and R t e s t 2 , respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets ( R t r a i n i n g 2 and R t e s t 2 were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615. Highlights A new algorithm that combines GA and CART is introduced. The proposed algorithm inherits the advantages from both local and global approaches and was used to predict phenol toxicity. The algorithm partitioned the phenols to several subsets regardless of their MOAs to obtain statistically sound QSARs. Compared to the QSAR models reported in the literature, our model had better statistical efficiencies.


  • 주제어

    Toxicity .   Phenol .   Decision tree .   Genetic algorithm .   Tetrahymena pyriformis.  

 활용도 분석

  • 상세보기

    amChart 영역
  • 원문보기

    amChart 영역

원문보기

무료다운로드
  • 원문이 없습니다.

유료 다운로드의 경우 해당 사이트의 정책에 따라 신규 회원가입, 로그인, 유료 구매 등이 필요할 수 있습니다. 해당 사이트에서 발생하는 귀하의 모든 정보활동은 NDSL의 서비스 정책과 무관합니다.

원문복사신청을 하시면, 일부 해외 인쇄학술지의 경우 외국학술지지원센터(FRIC)에서
무료 원문복사 서비스를 제공합니다.

NDSL에서는 해당 원문을 복사서비스하고 있습니다. 위의 원문복사신청 또는 장바구니 담기를 통하여 원문복사서비스 이용이 가능합니다.

이 논문과 함께 출판된 논문 + 더보기