A conformer- and alignment-independent three-dimensional structure-activity relationship (3D-QSAR) model has been derived that is based on flexible molecular interaction fields calculated in GRID and the subsequent description of these fields by use of alignmentindependent descriptors derived in ALMOND. The training set consisted of 22 diverse and flexible competitive inhibitors of the drug-metabolizing enzyme CYP2C9 and generated a model with r2 of 0.81 and q2 of 0.62. The predicitive capacity of the model was externally evaluated with a test set of 12 competitive inhibitors and 11 out of 12 were predicted within 0.5 log unit. The most relevant points of interaction in the model correlated well to the amino acids involved in CYP2C9-substrate/inhibitor binding in the active site of a CYP2C9 homology model, further validating the mechanistic sense of our model.. This approach offers the possibility to derive predicitve 3D-QSAR models without the need for an alignment rule for chemically diverse ligands and in the absence of target protein crystal structure information.

Conformer- and Alignment-Independent Model for Predicting Structurally Diverse Competitive CYP2C9 Inhibitors

BARONI, Massimo;CRUCIANI, Gabriele
2004

Abstract

A conformer- and alignment-independent three-dimensional structure-activity relationship (3D-QSAR) model has been derived that is based on flexible molecular interaction fields calculated in GRID and the subsequent description of these fields by use of alignmentindependent descriptors derived in ALMOND. The training set consisted of 22 diverse and flexible competitive inhibitors of the drug-metabolizing enzyme CYP2C9 and generated a model with r2 of 0.81 and q2 of 0.62. The predicitive capacity of the model was externally evaluated with a test set of 12 competitive inhibitors and 11 out of 12 were predicted within 0.5 log unit. The most relevant points of interaction in the model correlated well to the amino acids involved in CYP2C9-substrate/inhibitor binding in the active site of a CYP2C9 homology model, further validating the mechanistic sense of our model.. This approach offers the possibility to derive predicitve 3D-QSAR models without the need for an alignment rule for chemically diverse ligands and in the absence of target protein crystal structure information.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11391/23112
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