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Forty anthraquinone derivatives have been downloaded from PubChem database and investigated in a quantitative structure-activity relationships (QSAR) study. The models describing logPand LD50 of this set were built up on the hypermolecule scheme that mimics the investigated receptor space; the models were validated by the leave-one-out procedure, in the external test set and in a new version of prediction by using similarity clusters. Molecular docking approach using Lamarckian Genetic Algorithm was made on this class of anthraquinones with respect to 3Q3B receptor. The best scored molecules in the docking assay were used as leaders in the similarity clustering procedure. It is demonstrated that the LD50 data of this set of anthraquinones are related to the binding energies of anthraquinone ligands to the 3Q3B receptor. [ABSTRACT FROM AUTHOR]

The article discusses a Quantitative Structure-Activity Relationship study (QSAR) on chemical compound Indolizines and external tests on it using clusters of similar molecules. Particular focus is given to leave-one-out procedure used for a set of indolizines. Also included are molecular docking analysis, binding energy of ligands and hypermolecule comprising.

A study of 40 chlorpropamide derivatives, QSAR (Quantitative Structure Activity Relationship) and molecular docking approaches were applied to explore the structural requisites of chlorpropamide derivatives for CYTOCHROME P450 2C9 inhibitory activity. A set of forty chlorpropamide, was modeled, within the hypermolecule strategy; the predicted activity was LD50 and prediction was done on similarity clusters with the leaders chosen as the best docked ligands on the CYTOCHROME P450 2C9. [ABSTRACT FROM AUTHOR]

Derivatives of (1-methylpiperidin-4-yl) propanoate, with similar biological characteristics, have been reported for patients with Alzheimer disease [1,2]. QSAR study was performed on a set of 40 (1-methylpiperidin-4- yl) propanoate derivatives, downloaded from the PubChem database and aligned over a hypermolecule that mimics the investigated correlational space. The best models describing log P of these (1-methylpiperidin-4-yl) propanoate derivatives were validated by leave-one-out procedure [3] and best predicted within similarity clusters. [ABSTRACT FROM AUTHOR]

A set of 40 resveratrol derivatives, downloaded from the PubChem database, was submitted to a QSAR study, following Diudea's algorithm, involving the hypermolecule concept, in a procedure similar to that of the „alignment“ of drug molecules to the biological receptors. The best models describing log P of this set of resveratrols were validated by the leave-one-out procedure, in the external test set and in a new version of prediction by using clusters of similar molecules. The structures have been optimized at HF 6-31G(d,p) level of theory. Topological indices have been computed by TOPOCLUJ software. [ABSTRACT FROM AUTHOR]

A study of 30 flavonoid derivatives, taken from PubChem database and docked on flavonoid 3-O-glucosyltransferase 3HBF, next submitted to a QSAR study, performed within a hypermolecule frame, to model their LD50 values, is reported. The initial set of molecules was split into a training set and the test set (taken from the best scored molecules in the docking test); the predicted LD50 values, computed on similarity clusters, built up for each of the molecules of the test set, surpassed in accuracy the best model. The binding energies to 3HBF protein, provided by the docking step, are not related to the LD50 of these flavonoids, more protein targets are to be investigated in this respect. However, the docking step was useful in choosing the test set of molecules.

Quantitative Structure-Activity Relationships based on molecular descriptors calculated with correlation weights within the hypermolecule, considered to mimic the investigated correlational space, was performed on a set of 40 flavonoids (PubChem database). The best models describing log P and LD50 of this set of flavonoids were validated by the leave-one-out procedure, in the external test set and in a new version of prediction by using clusters of similar molecules. The best prediction was provided by the similarity cluster procedure.