4-Thiazolidinone is a promising scaffold for the search of new potential antibacterial, antiviral, antidiabetic and anticancer agents etc. SAR analysis of the most potent compounds and different activities evaluation provide a solid background for de novo design of novel drugs. Current review summarizes recent QSAR studies on the 4-thiazolidinones making the emphasis on both technical and interpretative sides of reported models. Several papers among them are devoted to the anticancer activity of 4-thiazolidinone derivatives and are reporting QSAR models that were obtained via multiple linear regressions (MLR). Additionally, a non-linear approach, namely Gaussian processes, has been applied to identify the relationships between 4-thiazolidinones structure and tumor cell growth inhibition. The interpretation of the reported model highlights the core template for the design of new highly-potent anticancer agents and proposes a hypothesis about key role of Hydrogen at the N-atom three bonds away from thiazolidine in the interaction with biotarget.

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.