This chapter critically reviews some of the important methods being used for building quantitative structure-activity relationship (QSAR) models using the artificial neural networks (ANNs). It attends predominantly to the use of multilayer ANNs in the regression analysis of structure-activity data. The highlighted topics cover the approximating ability of ANNs, the interpretability of the resul...

Quantitative Structure-Activity Relationships (QSAR) is a method to create models that can predict certain properties of compounds. Because of the importance of QSAR in designing new drugs, ability to accelerate this process becomes crucial. One way to achieve that is to be able to quickly explore the QSAR model space in the search for the best models. The cloud computing paradigm very well fit...

Q uantitative structure-activity relationships (QSAR) correlate, within congeneric series of compounds, affinities of ligands to their binding sites, inhibition constants, rate constants, and other biological activities, either with certain structural features (Free Wilson analysis) or with atomic, group or molecular properties, such as lipophilicity, polarizability, electronic and steric prope...

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...

QSAR(Quantitative Structure Activity Relationship) studies were carried out on a set of 72 α-sulfone hydroxamatesas Matrix Metalloproteinase-13 (MMP-13) inhibitors using multiple regression procedure. Outliers were removed based on Relative Error calculation and Extent of Extrapolation. The activity contributions of these compounds were determined from regression equation and the validation pro...

an important property that has been extensively studied in quantitative structure activityrelationship (qsar) is the chromatographic retention index. qsar study is suggested for theprediction of retention index of alkanes and alkenes compounds. modeling of the retention indexof alkanes and alkenes compounds as a function of molecular structures was established bydifferent chemometrics methods. ...

Quantitative Structure–Activity Relationship (QSAR) aims to correlate molecular structure properties with corresponding bioactivity. Chance correlations and multicollinearity are two major problems often encountered when generating QSAR models. Feature selection can significantly improve the accuracy interpretability of by removing redundant or irrelevant descriptors. An artificial bee colony a...

OBJECTIVE Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substruc...

BACKGROUND Integrative testing strategies (ITSs) for potential endocrine activity can use tiered in silico and in vitro models. Each component of an ITS should be thoroughly assessed. OBJECTIVES We used the data from three in vitro ToxCast™ binding assays to assess OASIS, a quantitative structure-activity relationship (QSAR) platform covering both estrogen receptor (ER) and androgen receptor ...