Quantitative structure-activity relationship of flavonoid p56(lck) protein tyrosine kinase inhibitors. A neural network approach

Specific inhibitors of protein tyrosine kinase as antiproliferative agents are instrumental in several aspects of neoplastic disease and have found wide interest as potential pharmacological agents, We have applied an artificial neural network based on a counterpropagation algorithm to develop quantitative structure-activity relationships in a large dataset of 105 flavonoid derivatives that inhibit the enzyme p56(lck) protein tyrosine kinase. The results of such approach were compared with the linear multiregression analysis with regard to the ability to fit biological activity surfaces, predict activity, and explore the nonlinear aspects of the dependence of activity on properties. Excellent correlation was obtained for both classical and quantum chemical descriptors, and relevance of the descriptors to binding properties of the enzyme receptor active site is hypothesized.