Literature DB >> 31766890

QSAR/QSPR models based on quantum chemistry for risk assessment of pesticides according to current European legislation.

J J Villaverde1, B Sevilla-Morán1, C López-Goti1, J L Alonso-Prados1, P Sandín-España1.   

Abstract

In Europe, agencies and official organizations involved in the pesticide control such as the EFSA, ECHA, JRC and ECETOC or even the OECD are pointing out that the software tools based on quantitative structure relationship models, i.e. QSAR and QSPR, have a huge potential to improve the pesticide risk assessment process. In this sense, these non-animal test methods can promote the competitiveness of agriculture in this region: the consumer safety is increased with them due to the possibility of perform an overall better risk assessment of the degradation products and metabolites from pesticides. However, the use of theses computational-based (in silico) tools must be much more systematised and harmonised, improving their validation and including case studies to test them. To open databases, incorporating critical data in an orderly manner for building the models, becomes also necessary. Moreover, quantum chemistry through the Density Functional Theory should be promoted as tool for calculation of quantum descriptors, especially for the study of similar compounds with the same carbon skeleton but differing substitution patterns, e.g. isomers.

Entities:  

Keywords:  Pesticide; density functional theory; quantitative structure-activity relationship; quantitative structure-property relationship; quantum chemistry

Mesh:

Substances:

Year:  2020        PMID: 31766890     DOI: 10.1080/1062936X.2019.1692368

Source DB:  PubMed          Journal:  SAR QSAR Environ Res        ISSN: 1026-776X            Impact factor:   3.000


  2 in total

1.  Molecular Modifications and Control of Processes to Facilitate the Synergistic Degradation of Polybrominated Diphenyl Ethers in Soil by Plants and Microorganisms Based on Queuing Scoring Method.

Authors:  Tong Wu; Yu Li; Hailin Xiao; Mingli Fu
Journal:  Molecules       Date:  2021-06-26       Impact factor: 4.411

2.  Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures.

Authors:  Myungwon Seo; Chong Hak Chae; Yuno Lee; Ha Ryong Kim; Jongwoon Kim
Journal:  Toxics       Date:  2021-03-16
  2 in total

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