Literature DB >> 28988138

MouseTox: An online toxicity assessment tool for small molecules through Enalos Cloud platform.

Dimitra-Danai Varsou1, Georgia Melagraki2, Haralambos Sarimveis3, Antreas Afantitis4.   

Abstract

Advances in the drug discovery research substantially depend on in silico methods and techniques that capitalize on experimental data to enable the accurate property/activity assessment by employing a variety of computational techniques. These in silico tools can significantly reduce expensive and time consuming experimental procedures required and are strongly recommended to avoid animal testing, especially as far as toxicity evaluation and risk assessment is concerned. In this context, in the present work we aim to develop a predictive model for the cytotoxic effects of a wide range of compounds based solely on calculated molecular descriptors that account for their topological, geometric and structural characteristics. The developed model was fully validated and was released online via Enalos Cloud platform accessible through http://enalos.insilicotox.com/MouseTox/. This ready-to-use web service offers, through a user-friendly interface, free access to the model results and therefore can act as a toxicity prediction tool for the risk assessment of novel compounds, without any special requirements or prior programming skills.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cytotoxicity; Enalos cloud platform; Enalos+ KNIME nodes; KNIME workflow; Predictive model; Random forest

Mesh:

Substances:

Year:  2017        PMID: 28988138     DOI: 10.1016/j.fct.2017.09.058

Source DB:  PubMed          Journal:  Food Chem Toxicol        ISSN: 0278-6915            Impact factor:   6.023


  4 in total

1.  Filling data gap for nicotinic acid, nicotinate esters and nicotinamide for the determination of permitted daily exposure by a category approach.

Authors:  Mohammad Charehsaz; Gulcin Tugcu; Ahmet Aydin
Journal:  Toxicol Res       Date:  2020-11-05

2.  e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods.

Authors:  Suqing Zheng; Mengying Jiang; Chengwei Zhao; Rui Zhu; Zhicheng Hu; Yong Xu; Fu Lin
Journal:  Front Chem       Date:  2018-03-29       Impact factor: 5.221

Review 3.  Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment.

Authors:  Angela Serra; Michele Fratello; Luca Cattelani; Irene Liampa; Georgia Melagraki; Pekka Kohonen; Penny Nymark; Antonio Federico; Pia Anneli Sofia Kinaret; Karolina Jagiello; My Kieu Ha; Jang-Sik Choi; Natasha Sanabria; Mary Gulumian; Tomasz Puzyn; Tae-Hyun Yoon; Haralambos Sarimveis; Roland Grafström; Antreas Afantitis; Dario Greco
Journal:  Nanomaterials (Basel)       Date:  2020-04-08       Impact factor: 5.076

Review 4.  In silico prediction of toxicity and its applications for chemicals at work.

Authors:  Kyung-Taek Rim
Journal:  Toxicol Environ Health Sci       Date:  2020-05-14
  4 in total

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