Interpretable correlation descriptors for quantitative structure-activity relationships

Interpretable correlation descriptors for quantitative structure-activity relationships

BACKGROUND The topological maximum cross correlation (TMACC) descriptors are alignment-independent 2D descriptors for the derivation of QSARs. TMACC descriptors are generated using atomic properties determined by molecular topology. Previous validation (J Chem Inf Model 2007, 47: 626-634) of the TMACC descriptor suggests it is competitive with the current state of the art. RESULTS Here, we il...

Theoretical Descriptors for the Correlation of Aquatic Toxicity of Environmental Pollutants by Quantitative Structure-Toxicity Relationships

Quantitative structure-toxicity relationships were developed for the prediction of aqueous toxicities for Poecilia reticulata (guppy) using the CODESSA treatment. A two-parameter correlation was found for class 1 toxins with R(2) = 0.96, and a five-parameter correlation was found for class 2 toxins with R(2) = 0.92. A five-parameter correlation for class 3 toxins had R(2) = 0.85. The correlatio...

Structure-activity relationships of taxoids: a molecular descriptors family approach

Introduction: Taxoids, groups of diterpenoid cyclodecanes isolated from the genus Taxus, are known and used as anticancer agents. Starting from the successful results obtained by an original molecular descriptors family on structure-activity relationships (Jäntschi and Bolboacă, 2007), the aim of the research was to investigate and to assess the estimation and prediction abilities of this appro...

History of Quantitative Structure-Activity Relationships

Nonlinear Prediction of Quantitative Structure-Activity Relationships

Predicting the log of the partition coefficient P is a long-standing benchmark problem in Quantitative Structure-Activity Relationships (QSAR). In this paper we show that a relatively simple molecular representation (using 14 variables) can be combined with leading edge machine learning algorithms to predict logP on new compounds more accurately than existing benchmark algorithms which use comp...