Broad-Based Quantitative Structure−Activity Relationship Modeling of Potency and Selectivity of Farnesyltransferase Inhibitors Using a Bayesian Regularized Neural Network

• Published in: Journal of medicinal chemistry Vol. 47; no. 25; pp. 6230 - 6238
• Main Authors: Polley, Mitchell J, Winkler, David A, Burden, Frank R
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 02.12.2004
• Subjects: Alkyl and Aryl Transferases - antagonists & inhibitors; Alkyl and Aryl Transferases - chemistry; Animals; Antineoplastic agents; Antineoplastic Agents - chemistry; Antineoplastic Agents - pharmacology; Bayes Theorem; Biological and medical sciences; Cercopithecus aethiops; COS Cells; Enzyme Inhibitors - chemistry; Enzyme Inhibitors - pharmacology; Farnesyltranstransferase; General aspects; Medical sciences; Mice; Models, Molecular; Neural Networks (Computer); NIH 3T3 Cells; Pharmacology. Drug treatments; Quantitative Structure-Activity Relationship; Bayes estimation; Enzyme; Transferases; Prediction; Enzyme inhibitor; Selectivity; Regression analysis; Neural network; Farnesyl-diphosphate farnesyltransferase; In vitro; Modeling; In vivo; Cell line; Structure activity relation; Animal; Molecular model; Tumor cell
• ISSN: 0022-2623; 1520-4804
• DOI: 10.1021/jm049621j

Inhibitors of the enzyme farnesyltransferase show potential as novel anticancer agents. There are many known inhibitors, but efforts to build predictive SAR models have been hampered by the structural diversity and flexibility of inhibitors. We have undertaken for the first time a QSAR study of the potency and selectivity of a large, diverse data set of farnesyltransferase inhibitors. We used novel molecular descriptors based on binned atomic properties and invariants of molecular matrices and a robust, nonlinear QSAR mapping paradigm, the Bayesian regularized neural network. We have built robust QSAR models of farnesyltransferase inhibition, geranylgeranyltransferase inhibition, and in vivo data. We have derived a novel selectivity index that allows us to model potency and selectivity simultaneously and have built robust QSAR models using this index that have the potential to discover new potent and selective inhibitors.