Some findings relevant to the mechanistic interpretation in the case of predictive models for carcinogenicity based on the counter propagation artificial neural network-Reference-Cited by-同舟云学术

Some findings relevant to the mechanistic interpretation in the case of predictive models for carcinogenicity based on the counter propagation artificial neural network

Published:2011-12 Issue:12 Volume:25 Page:1159-1169
ISSN:0920-654X
Container-title:Journal of Computer-Aided Molecular Design
language:en
Short-container-title:J Comput Aided Mol Des

Author:

Fjodorova Natalja,Novič Marjana

Publisher

Springer Science and Business Media LLC

Subject

Physical and Theoretical Chemistry,Computer Science Applications,Drug Discovery

Link

http://link.springer.com/content/pdf/10.1007/s10822-011-9500-7.pdf

Reference17 articles.

1. Worth AP, Bassan A, Gallegos A, Netzeva TI, Patlewicz G, Pavan M, Tsakovska I, Vracko M (2005) The characterisation of (Quantitative) structure-activity relationships: preliminary guidance EUR 21866 EN

2. Benigni R, Bossa C, Netzeva T, Worth A (2007) Collection and evaluation of (Q)SAR models for mutagenicity and carcinogenicity. EUR 22772 EN:119, http://ecb.jrc.ec.europa.eu/documents/QSAR/EUR_22772_EN.pdf

3. Benigni R (2005) Structure-activity relationship studies of chemical mutagens and carcinogens: mechanistic investigations and prediction approaches. Chem Rev 105:1767–1800. doi: 10.1002/chin.200536232

4. Benigni R, Richard AM (1996) QSARS of mutagens and carcinogens: two case studies illustrating problems in the construction of models for noncongeneric chemicals. Mutat Res/Genetic Toxicol 371(1–2):29–46. doi: 10.1016/S0165-1218(96)90092-0

5. Serafimova R, Gatnik MF, Worth A (2010) Review of QSAR models and software tools for predicting genotoxicity and carcinogenicity, EUR 24427 EN:58, http://ecb.jrc.ec.europa.eu/DOCUMENTS/QSAR/EUR_24427_EN.pdf

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Quantitative Structure Activity/Toxicity Relationship through Neural Networks for Drug Discovery or Regulatory Use;Current Topics in Medicinal Chemistry;2023-11

2. Counter-Propagation Artificial Neural Network Models for Prediction of Carcinogenicity of Non-congeneric Chemicals for Regulatory Uses;Challenges and Advances in Computational Chemistry and Physics;2017

3. Impact of Artificial Neural Networks in QSAR and Computational Modeling;Artificial Neural Network for Drug Design, Delivery and Disposition;2016

4. Classification of estrogen receptor selective compounds with benzopyranskeleton using counterpropagation artificial neural networks optimised by genetic algorithms;Chemometrics and Intelligent Laboratory Systems;2015-08

5. Adapting Interrelated Two-Way Clustering Method for Quantitative Structure-Activity Relationship (QSAR) Modeling of Mutagenicity/Non- Mutagenicity of a Diverse Set of Chemicals;Current Computer Aided-Drug Design;2013-12-31