Literature DB >> 27218604

A Historical Excursus on the Statistical Validation Parameters for QSAR Models: A Clarification Concerning Metrics and Terminology.

Paola Gramatica1, Alessandro Sangion1.   

Abstract

In the last years, external validation of QSAR models was the subject of intensive debate in the scientific literature. Different groups have proposed different metrics to find "the best" parameter to characterize the external predictivity of a QSAR model. This editorial summarizes the history of parameter development for the external QSAR model validation and suggests, once again, the concurrent use of several different metrics to assess the real predictive capability of QSAR models.

Mesh:

Year:  2016        PMID: 27218604     DOI: 10.1021/acs.jcim.6b00088

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  33 in total

1.  QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes.

Authors:  Nikita Basant; Shikha Gupta
Journal:  Environ Sci Pollut Res Int       Date:  2017-04-24       Impact factor: 4.223

2.  Modeling the pH and temperature dependence of aqueousphase hydroxyl radical reaction rate constants of organic micropollutants using QSPR approach.

Authors:  Shikha Gupta; Nikita Basant
Journal:  Environ Sci Pollut Res Int       Date:  2017-09-16       Impact factor: 4.223

3.  QSPR modeling of optical rotation of amino acids using specific quantum chemical descriptors.

Authors:  Karina Kapusta; Natalia Sizochenko; Sedat Karabulut; Sergiy Okovytyy; Eugene Voronkov; Jerzy Leszczynski
Journal:  J Mol Model       Date:  2018-02-17       Impact factor: 1.810

4.  Norm index for predicting the rate constants of organic contaminants oxygenated with sulfate radical.

Authors:  Yajuan Shi; Fangyou Yan; Qingzhu Jia; Qiang Wang
Journal:  Environ Sci Pollut Res Int       Date:  2019-12-09       Impact factor: 4.223

5.  Partition coefficients for the SAMPL5 challenge using transfer free energies.

Authors:  Michael R Jones; Bernard R Brooks; Angela K Wilson
Journal:  J Comput Aided Mol Des       Date:  2016-09-19       Impact factor: 3.686

6.  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

7.  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

8.  First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability.

Authors:  Arkaprava Banerjee; Kunal Roy
Journal:  Mol Divers       Date:  2022-06-29       Impact factor: 3.364

9.  Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model.

Authors:  Hui Zhang; Chen Shen; Hong-Rui Zhang; Wen-Xuan Chen; Qing-Qing Luo; Lan Ding
Journal:  Mol Divers       Date:  2021-06-23       Impact factor: 3.364

10.  Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates.

Authors:  Liangliang Wang; Junjie Ding; Peichang Shi; Li Fu; Li Pan; Jiahao Tian; Dongsheng Cao; Hui Jiang; Xiaoqin Ding
Journal:  Arch Toxicol       Date:  2021-05-01       Impact factor: 5.153

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