Prediction of inhibitor binding free energies by quantum neural networks. Nucleoside analogues binding to trypanosomal nucleoside hydrolase

A computational method has been developed to predict inhibitor binding ener gy for untested inhibitor molecules. A neural network is trained from the e lectrostatic potential surfaces of known inhibitors and their binding energ ies. The algorithm is then able to predict, with high accuracy, the binding energy of unknown inhibitors. IU-nucleoside hydrolase from Crithidia fasci culata and the inhibitor molecules described previously [Miles, R. W. Tyler , P. C, Evans, G. Furneaux R, H., Parkin, D. W., and Schramm, V. L. (1999) Biochemistry 38, xxxx-xxxx] are used as the test system. Discrete points on the molecular electrostatic potential surface of inhibitor molecules are i nput to neural networks to identify the quantum mechanical features that co ntribute to binding. Feed-forward neural networks with back-propagation of error are trained to recognize the quantum mechanical electrostatic potenti al and geometry at the entire van der Waals surface of a group of training molecules and to predict the strength of interactions between the enzyme an d novel inhibitors. The binding energies of unknown inhibitors were predict ed, followed by experimental determination of K-i values. Predictions of Ki values using this theory are compared to other methods and are more robust in estimating inhibitory strength. The average deviation in estimating K-i values for 18 unknown inhibitor molecules, with 21 training molecules, is a factor of 5 x K-i over a range of 660 000 in K-i values for all molecules . The a posteriori accuracy of the predictions suggests the method will be effective as a guide for experimental inhibitor design.