A general feeling of disillusionment with QSAR has settled across the modeling community in recent years. Most practitioners seem to agree that QSAR has not fulfilled the expectations set for its ability to predict biological activity. Among the possible reasons that have been proposed recently for this disappointment are chance correlation, rough response surfaces, incorrect functional forms, ...

Quantitative structure activity relationships (QSARs) are functions that predict bioactivity from compound structure. Although almost every form of statistical and machine learning method has been applied to learning QSARs, there is no single best way of learning QSARs. Therefore, currently the QSAR scientist has little to guide her/him on which QSAR approach to choose for a specific problem. T...

QSAR models of 22 benzamidine derivatives reported as inhibitors of thrombin have been developed using the descriptors heat of formation, valence connectivity index, shape index, solvent accessibility surface area, molar refractivity, log P and molecular weight. QSAR models, in which either heat of formation or shape index or molar refractivity is present, have good predictive powers as correla...

Antioxidants are important defenders of the human body against nocive free radicals, which are the causative agents of most life-threatening diseases. The immense biomedicinal utility of antioxidants necessitates the development and design of new synthetic antioxidant molecules. The present report deals with the modeling of a series of chromone derivatives, which was done to provide detailed in...

QSAR models for four skin penetration enhancer data sets of 61, 44, 42, and 17 compounds were constructed using classic QSAR descriptors and 4D-fingerprints. Three data sets involved skin penetration enhancement of hydrocortisone and hydrocortisone acetate. The other data set involved skin penetration enhancement of fluorouracil. The measure of penetration enhancement is the ratio of the net pe...