Autocorrelation of Molecular Electrostatic Potential Surface Properties Combined with Partial Least Squares Analysis as New Strategy for the Prediction of the Activity of Human A3 Adenosine Receptor Antagonists.

Autocorrelation of Molecular Electrostatic Potential Surface Properties Combined with Partial Least Squares Analysis as New Strategy for the Prediction of the Activity of Human A3 Adenosine Receptor Antagonists.

Moro S, Bacilieri M, Cacciari B, Spalluto G. “Autocorrelation of Molecular Electrostatic Potential Surface Properties Combined with Partial Least Squares Analysis as New Strategy for the Prediction of the Activity of Human A3 Adenosine Receptor Antagonists.” J. Med. Chem.. 2005;48:5698-5704.

TitleAutocorrelation of Molecular Electrostatic Potential Surface Properties Combined with Partial Least Squares Analysis as New Strategy for the Prediction of the Activity of Human A3 Adenosine Receptor Antagonists
Publication TypeJournal Article
Year of Publication2005
AuthorsMoro S, Bacilieri M, Cacciari B, Spalluto G
JournalJ. Med. Chem.
Volume48
Pagination5698-5704
Date Published08/2005
AbstractThe combination of molecular electrostatic potential (MEP) surface properties (autocorrelation vectors) with the conventional partial least squares (PLS) analysis has been used for the prediction of the human A3 receptor antagonist activities. Three-hundred-fifty-eight structurally diverse human A3 receptor antagonists have been utilized to generate a novel ligand-based three-dimensional structure−activity relationship. Remarkably, our chemical library includes all 21 important chemical classes of human A3 antagonists currently discovered, and it represents the largest molecular collection used to generate a general human A3 antagonist structure−activity relationship. A robust quantitative model has been obtained as described by both cross-validated correlation coefficient (rcv = 0.81) and prediction capability (rpred = 0.82). The proposed MEP/PLS approach can be considered as an alternative hit identification tool in virtual screening applications.
DOI10.1021/jm0502440

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