Literature DB >> 17101009

Structure-activity relationships for skin sensitization: recent improvements to Derek for Windows.

Kate Langton1, Grace Y Patlewicz, Anthony Long, Carol A Marchant, David A Basketter.   

Abstract

Derek for Windows (DfW) is a knowledge-based expert system that predicts the toxicity of a chemical from its structure. Its predictions are based in part on alerts that describe structural features or toxicophores associated with toxicity. Recently, improvements have been made to skin sensitization alerts within the DfW knowledge base in collaboration with Unilever. These include modifications to the alerts describing the skin sensitization potential of aldehydes, 1,2-diketones, and isothiazolinones and consist of enhancements to the toxicophore definition, the mechanistic classification, and the extent of supporting evidence provided. The outcomes from this collaboration demonstrate the importance of updating and refining computer models for the prediction of skin sensitization as new information from experimental and theoretical studies becomes available.

Entities:  

Mesh:

Substances:

Year:  2006        PMID: 17101009     DOI: 10.1111/j.1600-0536.2006.00969.x

Source DB:  PubMed          Journal:  Contact Dermatitis        ISSN: 0105-1873            Impact factor:   6.600


  5 in total

1.  Fragment-based prediction of skin sensitization using recursive partitioning.

Authors:  Jing Lu; Mingyue Zheng; Yong Wang; Qiancheng Shen; Xiaomin Luo; Hualiang Jiang; Kaixian Chen
Journal:  J Comput Aided Mol Des       Date:  2011-09-20       Impact factor: 3.686

2.  An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential.

Authors:  J M Fitzpatrick; D W Roberts; G Patlewicz
Journal:  SAR QSAR Environ Res       Date:  2018-04-20       Impact factor: 3.000

3.  In-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining.

Authors:  Feng Zhang; Kumar Ganesan; Yan Li; Jianping Chen
Journal:  Int J Mol Sci       Date:  2022-09-02       Impact factor: 6.208

4.  Prediction of skin sensitization with a particle swarm optimized support vector machine.

Authors:  Hua Yuan; Jianping Huang; Chenzhong Cao
Journal:  Int J Mol Sci       Date:  2009-07-17       Impact factor: 6.208

5.  Interpretation of murine local lymph node assay (LLNA) data for skin sensitization: Overload effects, danger signals and chemistry-based read-across.

Authors:  David W Roberts
Journal:  Curr Res Toxicol       Date:  2021-01-21
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.