Farnesyltranseferase inhibitors (FTIs) are one of the most promising classes of anticancer agents, but though some compounds in this category are in clinical trials there are no marketed drugs in this class yet. Quantitative structure activity relationship (QSAR) models can be used for predicting the activity of FTI candidates in early stages of drug discovery. In this study 192 imidazole-conta...

Nonsteroidal anti-inflammatory drugs (NSAIDs) are widely used for the treatment of pain, fever and inflammatory diseases such as rheumatoid arthritis and osteoarthritis. A series of direct analogues of flurbriprofen [4’-methylbiphenyl-2-(substituted phenyl) carboxamide derivatives] have been reported as novel anti-inflammatory agent. In order to gain further insights into the structural require...

The age old paradigm of quantitative structure-activity relationship (QSAR) is the congenericity principle which states that similar structures usually have similar properties. But these days a lot of large and structurally diverse data sets of chemicals with the same experimental data (dependent variable) are available. Starting with the same classes of descriptors we extracted the two subsets...

HDAC8 inhibitors have become an attractive treatment for cancer. This study aimed to facilitate the identification of potential chemical scaffolds for the selective inhibition of histone deacetylase 8 (HDAC8) using in silico approaches. Non-linear QSAR classification and regression models of HDAC8 inhibitors were developed with support vector machine. Mean impact value-based sequential forward ...

The comparative molecular field analysis (CoMFA) based on three dimensional quantitative structure–activity relationship (3D-QSAR) studies were carried out employing, natural terpenes as potent antimycobacterial agents. The best prediction were obtained with a CoMFA standard model (q2 = 0.569, r2 = 0.999) using steric, electrostatic, hydrophobic and hydrogen bond donor fields. In the current st...

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)...

This study concentrates on the external validation of an existing Quantitative Structure-Activity Relationship (QSAR) model widely used for long-term aquatic toxicity to fish. In the context of the REACH legislation, QSARs are used as an alternative for experimental data to achieve a complete environmental assessment without the need for animal testing. The predictivity of the model was evaluat...

Quantitative structure activity relationship (QSAR) models can be used to predict the activity of new drug candidates in early stages of drug discovery. In the present study, the information of the ninety two 2,5-diaminobenzophenone-containing farnesyltranaferase inhibitors (FTIs) were taken from the literature. Subsequently, the structures of the molecules were optimized using Hyperchem softwa...

The Quantitative Structure Activity Relationship (QSAR) study is performed over a set of 15, 4-alkyl/aryl-substituted 1- [benzofuran-2-yl-phenylmethyl]-1 H-triazoles derivatives. This study is based on the application of physicochemical parameters in QSAR. The parameters include (MR (molar refractivity), MW (molecular weight), Pc (parachor), St (surface tension), D (density), Ir (index of refra...

A common step in drug design is the formation of a quantitative structure-activity relationship (QSAR) to model an exploratory series of compounds. A QSAR generalizes how the structure of a compound relates to its biological activity. There is growing interest in the application of machine learning techniques in QSAR modeling research. However, no single technique can claim to be uniformly supe...