1,720,962 research outputs found
A search for optimal fastness/accuracy compromises in developing predictive models for estimating CYP2D6 inhibition properties of drug candidates
No full textComputational models enabling classification of substrates for the P4503A4 isoform of human Cytochrome P450
No full textMolecular Modelling of human CYP2D6 and molecular docking of a series of ajmalicine- and quinidine-like inhibitors
No full text3D-models were created and refined for CYP2D6 and for its complexes with ajmalicine and quinidine. The influence of the conformation of the enzyme active site on its interaction with ligands was evaluated by performing three series of molecular docking on selected ajmalicine- and quinidine-like inhibitors. The results suggested that the experimental binding values of ajmalicine- and quinidine-like inhibitors better fit with the energetic terms derived from their interaction with structures of CYP2D6 obtained by, respectively, optimizing the ajmalicine/CYP2D6 and the quinidine/CYP2D6 complexes, rather than exploiting the 3D-strucure of the enzyme not subjected to a ligand-induced conformational change. It suggests the relevance of induced-fit phenomena in the biological system of interest
Molecular Modeling of CYP2D6 complexes and docking of a series of ajmalicine- and quinidine-like inhibitors
No full textDevelopment of QSAR models for predicting hepatocarcinogenic toxicity of chemicals
No full textA dataset comprising 55 chemicals with hepatocarcinogenic potency indices was collected from the Carcinogenic Potency Database with the aim of developing QSAR models enabling prediction of the above unwanted property for New Chemical Entities. The dataset was rationally split into training and test sets by means of a sphere-exclusion type algorithm. Among the many algorithms explored to search regression models, only a Support Vector Machine (SVM) method led to a QSAR model, which was proved to pass rigorous validation criteria, in accordance with the OECD guidelines. The proposed model is capable to explain the hepatocarcinogenic toxicity and could be exploited for predicting this property for chemicals at the early stage of their development, so optimizing resources and reducing animal testing
Optimizing QSAR Models for Predicting Ligand Binding to the Drug-Metabolizing Cytochrome P450 Isoenzyme CYP2D6
No full textThe cytochrome P450 isozyme CYP2D6 binds a large variety of drugs, oxidizing many of them, and plays a crucial role in establishing in vivo drug levels, especially in multidrug regimens. The current study aimed to develop reliable predictive models for estimating the CYP2D6 inhibition properties of drug candidates. Quantitative structure-activity relationship (QSAR) studies utilizing 51 known CYP2D6 inhibitors were carried out. Performance achieved using models based on two-dimensional (2D) molecular descriptors was compared with performance using models entailing additional molecular descriptors that depend upon the three-dimensional (3D) structure of ligands. To properly compute the descriptors, all the 3D inhibitor structures were optimized such that induced-fit binding of the ligand to the active site was accommodated. CODESSA software was used to obtain equations for correlating the structural features of the ligands to their pharmacological effects on CYP2D6 (inhibition). The predictive power of all the QSAR models obtained was estimated by applying rigorous statistical criteria. To assess the robustness and predictability of the models, predictions were carried out on an additional set of known molecules (prediction set). The results showed that only models incorporating 3D descriptors in addition to 2D molecular descriptors possessed the requisite high predictive power for CYP2D6 inhibition
QSAR models for predicting biological properties, developed by combining structure- and ligand-based approaches: an application to the hERG potassium channel inhibition
No full textA strategy for developing accurate quantitative structure-activity relationship models enabling predictions of biological properties, when suitable knowledge concerning both ligands and biological target is available, was tested on a data set where molecules are characterized by high structural diversity. Such a strategy was applied to human ether-a-go-go-related gene K(+) channel inhibition and consists of a combination of ligand- and structure-based approaches, which can be carried out whenever the three-dimensional structure of the target macromolecule is known or may be modeled with good accuracy. Molecular conformations of ligands were obtained by means of molecular docking, performed in a previously built theoretical model of the channel pore, so that descriptors depending upon the three-dimensional molecular structure were properly computed. A modification of the directed sphere-exclusion algorithm was developed and exploited to properly splitting the whole dataset into Training/Test set pairs. Molecular descriptors, computed by means of the codessa program, were used for the search of reliable quantitative structure-activity relationship models that were subsequently identified through a rigorous validation analysis. Finally, pIC(50) values of a prediction set, external to the initial dataset, were predicted and the results confirmed the high predictive power of the model within a quite wide chemical space
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