a Department of Organic Chemistry, University of Santiago de Compostela, 15782, Spain. b Chemical Bioactives Center and Department of Veterinary Medicine, Central University of ‘Las Villas’, 54830, Cuba. c Department of Ultrasound Medicine, Calixto, Las Tunas, 77400, Cuba. ______________________________________________________________________________ Abstract. The general belief is that quantit...

BACKGROUND Quantitative structure-activity relationship (QSAR) models have become popular tools to help identify promising lead compounds in anticancer drug development. Few QSAR studies have investigated multitask learning, however. Multitask learning is an approach that allows distinct but related data sets to be used in training. In this paper, a suite of three QSAR models is developed to id...

Quantitative structure-activity relationship (QSAR) methods have been widely applied in drug discovery, lead optimization, toxicity prediction, and regulatory decisions. Despite major advances in algorithms and software, QSAR models have inherent limitations associated with a size and chemical-structure diversity of the training set, experimental error, and many characteristics of structure rep...

A structurally diverse dataset of 530 polo-like kinase-1 (PLK1) inhibitors is compiled from the ChEMBL database and studied by means of a conformation-independent quantitative structure-activity relationship (QSAR) approach. A large number (26,761) of molecular descriptors are explored with the main intention of capturing the most relevant structural characteristics affecting the bioactivity. T...

The Topoisomerase I enzyme has become an attractive target for the treatment of cancer. In this paper molecular dynamics, 2D and 3D QSAR and molecular docking studies were performed on 90 naphthoquinone derivatives as Topoisomerase I inhibitors by using the human Topo I-DNA cleavable complex. This model has the drug intercalated with its planar pharmacophore between +1 and -1 bp flanking cleava...

Derivation of quantitative structure-activity relationships (QSAR) usually involves computational models that relate a set of input variables describing the structural properties of the molecules for which the activity has been measured to the output variable representing activity. Many of the input variables may be correlated, and it is therefore often desirable to select an optimal subset of ...

A quantitative structure-activity relationship model was developed on a series of compounds containing oxadiazole-ligated pyrrole pharmacophore to identify key structural fragments required for anti-tubercular activity. Two-dimensional (2D) and three-dimensional (3D) QSAR studies were performed using multiple linear regression (MLR) analysis and k-nearest neighbour molecular field analysis (kNN...

Antitubercular activity of 5-nitrofuran-2-yl Derivatives series were subjected to Quantitative Structure Activity Relationship (QSAR) Analysis with an effort to derive and understand a correlation between the biological activity as response variable and different molecular descriptors as independent variables. QSAR models are built using 40 molecular descriptor dataset. Different statistical re...

Antitubercular activity of Sulfathiazole Derivitives series were subjected to Quantitative Structure Activity Relationship (QSAR) Analysis with an attempt to derive and understand a correlation between the Biologically Activity as dependent variable and various descriptors as independent variables. QSAR models generated using 28 compounds. Several statistical regression expressions were obtaine...

histamine h3 receptor subtype has been the target of several recent drug development programs. quantitative structure-activity relationship (qsar) methods are used to predict the pharmaceutically relevant properties of drug candidates whenever it is applicable. the aim of this study was to compare the predictive powers of three different qsar techniques, namely, multiple linear regression (mlr)...