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5-Year Trends in QSAR and its Machine Learning Methods

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Background: Quantitative Structure-Activity Relationships (QSAR) is a well-established branch of computational chemistry. The presence of QSAR papers is decreasing for the last few years.

Objective: To highlight recent trends of QSAR in general and trends of machine learning methods in particular.

Method: A bibliometric analysis of articles published in top ten molecular modeling and medicinal chemistry journals was carried out. The bibliometric statistics was collected for papers published in 2009 and 2015 and compared.

Results: During 5-year span studied, the fraction of QSAR studies underwent a twofold decrease. Top journals of both categories became less likely to publish Multiple Linear Regression models and increased the presence of Random forest and Naïve Bayes methods. 3D-QSAR remains the most popular method of studying structure-activity relationships with a slight decrease of its presence in molecular modeling journals but a relative increase in medicinal chemistry.

Conclusion: The downward QSAR trend might have several reasons: more stringent criteria for QSAR studies acceptance by journals, transformation of QSAR studies into routine work due to wider availability of QSAR methods and the overall maturation of QSAR field, and possible disappointment in QSAR. We expect that the progress in machine learning methods being adopted by chem(o)informaticians finally will help QSAR to find its place in drug design and to move to the Plateau of Productivity.
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Keywords: QSAR trends; bibliometric study; machine learning methods; medicinal chemistry; molecular modeling; random forest

Document Type: Research Article

Publication date: December 1, 2016

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  • Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design. Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, etc., providing excellent rationales for drug development.
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