Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints.

Background: Quantitative and qualitative structure–activity relationships (QSARs) have been used to understand chemical behavior for almost a century. The main source of QSAR models is the scientific literature, but the open question is how well these models are documented.

Objectives: The main aim of this study was to critically analyze the publication practices of QSARs with regard to transparency, potential reproducibility, and independent verification.

Topological Fingerprints as an Aid in Finding Structural Patterns for LRRK2 Inhibition.

Multiplet-based fingerprint mapping has been used to analyse the relationship between the structural features of potential drug candidates and the enzyme LRRK2 inhibition expressed as the inhibition constant (pKi ). For 198 structurally diverse compounds 4195 dimensional fingerprints were generated and mathematically manipulated using partial least squares (PLS) regression. A variation of PLS-BETA technique was developed for the reduction of noise by eliminating excess variables that resulted in a 636 dimensional fingerprint related to pKi .

Quantitative structure-activity relationship (QSAR) modeling of EC50 of aquatic toxicities for Daphnia magna.

The experimental EC(50) toxicities toward Daphnia magna for a series of 130 benzoic acids, benzaldehydes, phenylsulfonyl acetates, cycloalkane-carboxylates, benzanilides, and other esters were studied using the Best multilinear regression algorithm (BMLR) implemented in CODESSA. A modified quantitative structure-activity relationships (QSAR) procedure was applied guaranteeing the stability and reproducibility of the results. Separating the initial data set into training and test subsets generated three independent models with an average R(2) of .

Modeling the toxicity of chemicals to Tetrahymena pyriformis using heuristic multilinear regression and heuristic back-propagation neural networks.

During the last years, considerable effort has been devoted to model the toxicity of chemicals to Tetrahymena pyriformis for medium and large sized data sets using various artificial neural network (ANN) techniques. Motivation behind this has been to model highly complex relationships with nonlinear character making it possible to describe wide structural diversity within one model. The current work compares the performance of two heuristic methods in developing quantitative structure-activity relationship (QSAR) models: the best multilinear regression (BMLR) approach and the heuristic back-propagation neural networks (hBNN).

Improved efficiency of focal point conformational analysis with truncated correlation consistent basis sets.

It has been suggested that the computational cost of correlated ab initio calculations could be reduced efficiently by using truncated basis sets on hydrogen atoms (Mintz et al., J Chem Phys 2004, 121, 5629). We now explore this proposal in the context of conformational analysis of small molecules, such as hydrogen peroxide, dimethyl ether, ethyl methyl ether, formic acid, methyl formate, and several small alcohols.

Comparative quantitative structure-activity-activity relationships for toxicity to Tetrahymena pyriformis and Pimephales promelas.

An approach for predicting acute aquatic toxicity, in the form of a quantitative structure-activity-activity relationship (QSAAR), is described. This study assessed relative toxic effects to a fish, Pimephales promelas, and a ciliate, Tetrahymena pyriformis, and attempted to form relationships between them. A good agreement between toxic potencies (R2 = 0.

QSPR treatment of the soil sorption coefficients of organic pollutants.

In this study, general and class-specific QSPR models for soil sorption, logK(OC), of 344 organic pollutants (0 < logK(OC) < 4.94) were developed using a large variety of theoretical molecular descriptors based only on molecular structure. Two general models were obtained.