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QSAR studies of copper azamacrocycles and thiosemicarbazones: MM3 parameter development and prediction of biological properties.

ABSTRACT: Genetic algorithms (GA) were used to develop specific copper metal-ligand force field parameters for the MM3 force field, from a combination of crystallographic structures and ab initio calculations. These new parameters produced results in good agreement with experiment and previously reported copper metal-ligand parameters for the AMBER force field. The MM3 parameters were then used to develop several quantitative structure-activity relationship (QSAR) models. A successful QSAR for predicting the lipophilicity (log P(ow)) of several classes of Cu(II)-chelating ligands was built using a training set of 32 Cu(II) radiometal complexes and 6 simple molecular descriptors. The QSAR exhibited a correlation between the predicted and experimental log P(ow) with an r(2) = 0.95, q(2) = 0.92. When applied to an external test set of 11 Cu(II) complexes, the QSAR preformed with great accuracy; r(2) = 0.93 and a q(2) = 0.91 utilizing a leave-one-out cross-validation analysis. Additional QSAR models were developed to predict the biodistribution of a smaller set of Cu(II) bis(thiosemicarbazone) complexes.

SUBMITTER: Wolohan P

PROVIDER: S-EPMC2566539 | biostudies-literature | 2005 Aug

REPOSITORIES: biostudies-literature

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QSAR studies of copper azamacrocycles and thiosemicarbazones: MM3 parameter development and prediction of biological properties.

Wolohan Peter P Yoo Jeongsoo J Welch Michael J MJ Reichert David E DE

Journal of medicinal chemistry 20050801 17

Genetic algorithms (GA) were used to develop specific copper metal-ligand force field parameters for the MM3 force field, from a combination of crystallographic structures and ab initio calculations. These new parameters produced results in good agreement with experiment and previously reported copper metal-ligand parameters for the AMBER force field. The MM3 parameters were then used to develop several quantitative structure-activity relationship (QSAR) models. A successful QSAR for predicting ...[more]

PMID: 16107156

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QSAR studies of copper azamacrocycles and thiosemicarbazones: MM3 parameter development and prediction of biological properties.

Publications

QSAR studies of copper azamacrocycles and thiosemicarbazones: MM3 parameter development and prediction of biological properties.

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