Partha Pratim Roy

TL;DR: In this article, the optimum variable selection strategy for Partial Least Squares (PLS) regression using a model dataset of cytoprotection data is explored, where the compounds of the dataset were classified using K-means clustering technique applied on standardized descriptor matrix and ten combinations of training and test sets were generated based on the obtained clusters.

TL;DR: A test for these two parameters is suggested to be a more stringent requirement than the traditional validation parameters to decide acceptability of a predictive QSAR model, especially when a regulatory decision is involved.

TL;DR: No general rule can be formulated regarding the impact of training set size on the quality of prediction, but optimum size of the training set should be set based on a particular data set and types of descriptors and statistical analysis being used.

TL;DR: This work summarizes a methodology for a rigorous statistical approach to obtain reliable QSAR predictions, also for a large number of (B)TAZs in the ECHA preregistration list of REACH (even if starting from limited experimental data availability), and has evidenced some ambiguities and discrepancies related to SMILES notations from different databases.

TL;DR: The focused criteria of this review is to highlights the targeted inhibitory activity of 1,3,4-oxadiazole derivatives and their structure activity relationship to generate potential anticancer agents.