Literature DB >> 24875906

A new in silico classification model for ready biodegradability, based on molecular fragments.

Anna Lombardo1, Fabiola Pizzo1, Emilio Benfenati2, Alberto Manganaro1, Thomas Ferrari3, Giuseppina Gini3.   

Abstract

Regulations such as the European REACH (Registration, Evaluation, Authorization and restriction of Chemicals) often require chemicals to be evaluated for ready biodegradability, to assess the potential risk for environmental and human health. Because not all chemicals can be tested, there is an increasing demand for tools for quick and inexpensive biodegradability screening, such as computer-based (in silico) theoretical models. We developed an in silico model starting from a dataset of 728 chemicals with ready biodegradability data (MITI-test Ministry of International Trade and Industry). We used the novel software SARpy to automatically extract, through a structural fragmentation process, a set of substructures statistically related to ready biodegradability. Then, we analysed these substructures in order to build some general rules. The model consists of a rule-set made up of the combination of the statistically relevant fragments and of the expert-based rules. The model gives good statistical performance with 92%, 82% and 76% accuracy on the training, test and external set respectively. These results are comparable with other in silico models like BIOWIN developed by the United States Environmental Protection Agency (EPA); moreover this new model includes an easily understandable explanation.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Fragment-based model; QSAR; REACH; Ready biodegradability; SARpy

Mesh:

Substances:

Year:  2014        PMID: 24875906     DOI: 10.1016/j.chemosphere.2014.02.073

Source DB:  PubMed          Journal:  Chemosphere        ISSN: 0045-6535            Impact factor:   7.086


  5 in total

1.  Classification of biodegradable materials using QSAR modelling with uncertainty estimation.

Authors:  W F C Rocha; D A Sheen
Journal:  SAR QSAR Environ Res       Date:  2016-10-06       Impact factor: 3.000

2.  Identification of structural alerts for liver and kidney toxicity using repeated dose toxicity data.

Authors:  Fabiola Pizzo; Domenico Gadaleta; Anna Lombardo; Orazio Nicolotti; Emilio Benfenati
Journal:  Chem Cent J       Date:  2015-11-05       Impact factor: 4.215

3.  A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts.

Authors:  Fabiola Pizzo; Anna Lombardo; Alberto Manganaro; Emilio Benfenati
Journal:  Front Pharmacol       Date:  2016-11-22       Impact factor: 5.810

4.  Unmasking of crucial structural fragments for coronavirus protease inhibitors and its implications in COVID-19 drug discovery.

Authors:  Kalyan Ghosh; Sk Abdul Amin; Shovanlal Gayen; Tarun Jha
Journal:  J Mol Struct       Date:  2021-03-26       Impact factor: 3.196

5.  Predicting Primary Biodegradation of Petroleum Hydrocarbons in Aquatic Systems: Integrating System and Molecular Structure Parameters using a Novel Machine-Learning Framework.

Authors:  Craig Warren Davis; Louise Camenzuli; Aaron D Redman
Journal:  Environ Toxicol Chem       Date:  2022-04-29       Impact factor: 4.218

  5 in total

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