Toxmatch-A chemical classification and activity prediction tool based on similarity measures
ABSTRACT Chemical similarity forms the underlying basis for the development of (Quantitative) Structure-Activity Relationships ((Q)SARs), expert systems and chemical groupings. Recently a new software tool to facilitate chemical similarity calculations named Toxmatch was developed. Toxmatch encodes a number of similarity indices to help in the systematic development of chemical groupings, including endpoint specific groupings and read-across, and the comparison of model training and test sets. Two rule-based classification schemes were additionally implemented, namely: the Verhaar scheme for assigning mode of action for aquatic toxicants and the BfR rulebase for skin irritation and corrosion. In this study, a variety of different descriptor-based similarity indices were used to evaluate and compare the BfR training set with respect to its test set. The descriptors utilised in this comparison were the same as those used to derive the original BfR rules i.e. the descriptors selected were relevant for skin irritation/corrosion. The Euclidean distance index was found to be the most predictive of the indices in assessing the performance of the rules.
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ABSTRACT: The application of quantitative structure–activity relationships (QSARs) has significantly impacted the paradigm of drug discovery. Following the successful utilization of linear solvation free-energy relationships (LSERs), numerous 2D- and 3D-QSAR methods have been developed, most of them based on descriptors for hydrophobicity, polarizability, ionic interactions, and hydrogen bonding. QSAR models allow for the calculation of physicochemical properties (e.g., lipophilicity), the prediction of biological activity (or toxicity), as well as the evaluation of absorption, distribution, metabolism, and excretion (ADME). In pharmaceutical research, QSAR has a particular interest in the preclinical stages of drug discovery to replace tedious and costly experimentation, to filter large chemical databases, and to select drug candidates. However, to be part of drug discovery and development strategies, QSARs need to meet different criteria (e.g., sufficient predictivity). This chapter describes the foundation of modern QSAR in drug discovery and presents some current challenges and applications for the discovery and optimization of drug candidates KeywordsBlood–brain barrier-Cyclooxygenase inhibitor-Drug design-hERG-QSAR12/2009: pages 261-282;
Article: Recent Advances in QSAR Studies
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ABSTRACT: The aim of this chapter is to outline the theoretical background and application of quantum mechanics (QM) derived descriptors in computational toxicology, specifically in (quantitative) structure–activity relationship models ((Q)SARs). The chapter includes a discussion of the mechanistic rationale for the need for such descriptors in terms of the underlying chemistry. Having established the mechanistic rationale for quantum mechanical descriptors, a brief discussion of the underlying mathematical theory to quantum mechanical methodologies is presented, the aim being to help the reader understand (in simple terms) the differences between the commonly used levels of theory that one finds when surveying the computational toxicological literature. Finally, the chapter highlights a number of (Q)SAR models in which QM descriptors have been utilised to model a range of toxicological effects KeywordsGeometry optimisation-Semi-empirical methods-Density functional theory-Quantum mechanical descriptors12/2009: pages 13-28;