A bibliometric analysis of the Cheminformatics/QSAR literature (2000-2023) for predictive modeling in data science using the SCOPUS database

被引：15

作者：

Banerjee, Arkaprava ^{[1
]}

Roy, Kunal ^{[1
]}

Gramatica, Paola ^{[2
]}

机构：

[1] Jadavpur Univ, Dept Pharmaceut Technol, Drug Theoret & Cheminformat Lab, Kolkata 700032, India

[2] Univ Insubria, Dept Theoret & Appl Sci DiSTA, QSAR Res Unit Environm Chem & Ecotoxicol, Varese, Italy

来源：

MOLECULAR DIVERSITY | 2025年 / 29卷 / 04期

关键词：

QSAR; QSPR; QSTR; Cheminformatics; Chemoinformatics; Chemometrics; Bibliometric analysis;

D O I：

10.1007/s11030-024-11056-8

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

A bibliometric analysis of the Cheminformatics/QSAR articles published in the present century (2000-2023) is presented based on a SCOPUS search made in October 2024 using a given set of search criteria. The obtained results of 52,415 documents against the specific query are analyzed based on the number of documents per year, contributions of different countries and Institutes in Cheminformatics/QSAR publications, the contributions of researchers based on the number of documents, appearance in the top-cited articles, h-index, composite c-score (ns), and the newly introduced q-score. Finally, a list of the top 50 Cheminformatics/QSAR researchers is presented. An analysis is also made for the content of the top-cited articles during the period 2000-2023 in comparison to those before 2000 to capture the trend of changes in the Cheminformatics/QSAR research. The limiting factors of any bibliometric analysis are also briefly presented.

引用

页码：3703 / 3715

页数：13

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