Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors-Reference-Cited by-同舟云学术

Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors

Published:2022 Issue:39 Volume:46 Page:19062-19072
ISSN:1144-0546
Container-title:New Journal of Chemistry
language:en
Short-container-title:New J. Chem.

Author:

Singh Rahul¹,Kumar Parvin¹^ORCID,Devi Meena¹,Lal Sohan¹^ORCID,Kumar Ashwani²,Sindhu Jayant³^ORCID,Toropova Alla P.⁴^ORCID,Toropov Andrey A.⁴^ORCID,Singh Devender⁵^ORCID

Affiliation:

1. Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India

2. Department of Pharmaceutical Sciences, GJUS&T, Hisar, 125001, India

3. Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, 125004, India

4. Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy

5. Department of Chemistry, Maharshi Dayanand University, Rohtak, 124001, India

Abstract

Monte Carlo optimization based QSGFEAR model development using CII results in the formation of more reliable, robust and predictive models.

Funder

Council of Scientific and Industrial Research, India

Department of Science and Technology, Ministry of Science and Technology, India

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,General Chemistry,Catalysis

Link

http://pubs.rsc.org/en/content/articlepdf/2022/NJ/D2NJ03515D

Reference60 articles.

1. Prediction reliability of QSAR models: an overview of various validation tools

2. QSAR Modeling: Where Have You Been? Where Are You Going To?

3. The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR

4. The study of the index of ideality of correlation as a new criterion of predictive potential of QSPR/QSAR-models

5. Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling

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