A rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries.
QSAR models for a diverse set of compounds for cytochrome P450 1A2 inhibition have been produced using 4 statistical approaches; partial least squares (PLS), multiple linear regression (MLR), classification and regression trees (CART), and bayesian neural networks (BNN). The models complement one another and have identified the following descriptors as important features for CYP1A2 inhibition; lipophilicity, aromaticity, charge, and the HOMO/LUMO energies. Furthermore all models are global and have been used to predict a diverse independent set of compounds. For the first time in the field of QSAR, the kappa index of agreement has comprehensively been used to assess the overall accuracy of the model's predictive power. The models are statistically significant and can be used as a rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries.
Chohan
Kamaldeep K
KK
Department of Physical & Metabolic Sciences, AstraZeneca R&D Charnwood, Bakewell Road, Loughborough, Leicestershire LE11 5RH, U.K. Kamaldeep.Chohan@astrazeneca.com
Paine
Stuart W
SW
Mistry
Jaina
J
Barton
Patrick
P
Davis
Andrew M
AM
D001499:Bayes Theorem; D019388:Cytochrome P-450 CYP1A2 / Q000737:chemistry*; D065609:Cytochrome P-450 CYP1A2 Inhibitors; D016208:Databases, Factual; D004791:Enzyme Inhibitors / Q000737:chemistry*; D008958:Models, Molecular; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
D065609:Cytochrome P-450 CYP1A2 Inhibitors; D004791:Enzyme Inhibitors; D019388:Cytochrome P-450 CYP1A2