Literature DB >> 24599647

QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS.

Paola Gramatica1, Stefano Cassani, Nicola Chirico.   

Abstract

A database of environmentally hazardous chemicals, collected and modeled by QSAR by the Insubria group, is included in the updated version of QSARINS, software recently proposed for the development and validation of QSAR models by the genetic algorithm-ordinary least squares method. In this version, a module, named QSARINS-Chem, includes several datasets of chemical structures and their corresponding endpoints (physicochemical properties and biological activities). The chemicals are accessible in different ways (CAS, SMILES, names and so forth) and their three-dimensional structure can be visualized. Some of the QSAR models, previously published by our group, have been redeveloped using the free online software for molecular descriptor calculation, PaDEL-Descriptor. The new models can be easily applied for future predictions on chemicals without experimental data, also verifying the applicability domain to new chemicals. The QSAR model reporting format (QMRF) of these models is also here downloadable. Additional chemometric analyses can be done by principal component analysis and multicriteria decision making for screening and ranking chemicals to prioritize the most dangerous.
Copyright © 2014 Wiley Periodicals, Inc.

Entities:  

Keywords:  PaDEL-Descriptor models; QMRF; datasets; environmental chemicals; ranking

Year:  2014        PMID: 24599647     DOI: 10.1002/jcc.23576

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  37 in total

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4.  In silico Modeling and Toxicity Profiling of a Set of Quinoline Derivatives as c-MET Inhibitors in the treatment of Human Tumors.

Authors:  Gülçin Tuğcu; Filiz Esra Önen Bayram; Hande Sipahi
Journal:  Turk J Pharm Sci       Date:  2021-12-31

5.  A novel approach for assessment of antitrypanosomal activity of sesquiterpene lactones through additive and non-additive molecular structure parameters.

Authors:  Mohammad Hossein Keshavarz; Zeinab Shirazi; Faezeh Sayehvand
Journal:  Mol Divers       Date:  2022-07-17       Impact factor: 3.364

6.  Identification of potent aldose reductase inhibitors as antidiabetic (Anti-hyperglycemic) agents using QSAR based virtual Screening, molecular Docking, MD simulation and MMGBSA approaches.

Authors:  Ravindra L Bakal; Rahul D Jawarkar; J V Manwar; Minal S Jaiswal; Arabinda Ghosh; Ajaykumar Gandhi; Magdi E A Zaki; Sami Al-Hussain; Abdul Samad; Vijay H Masand; Nobendu Mukerjee; Syed Nasir Abbas Bukhari; Praveen Sharma; Israa Lewaa
Journal:  Saudi Pharm J       Date:  2022-04-07       Impact factor: 4.562

7.  In silico local QSAR modeling of bioconcentration factor of organophosphate pesticides.

Authors:  Purusottam Banjare; Balaji Matore; Jagadish Singh; Partha Pratim Roy
Journal:  In Silico Pharmacol       Date:  2021-04-04

8.  QSAR DataBank repository: open and linked qualitative and quantitative structure-activity relationship models.

Authors:  V Ruusmann; S Sild; U Maran
Journal:  J Cheminform       Date:  2015-06-25       Impact factor: 5.514

9.  COVID-19: In silico identification of potent α-ketoamide inhibitors targeting the main protease of the SARS-CoV-2.

Authors:  Mehdi Oubahmane; Ismail Hdoufane; Imane Bjij; Carola Jerves; Didier Villemin; Driss Cherqaoui
Journal:  J Mol Struct       Date:  2021-06-16       Impact factor: 3.196

10.  ILPC: simple chemometric tool supporting the design of ionic liquids.

Authors:  Maciej Barycki; Anita Sosnowska; Magdalena Piotrowska; Piotr Urbaszek; Anna Rybinska; Monika Grzonkowska; Tomasz Puzyn
Journal:  J Cheminform       Date:  2016-08-19       Impact factor: 5.514

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