a new method to analyze and predict the binding energy for enzyme - transition state inhibitor interactions is presented . computational neural networks are employed to discovery quantum mechanical features of transition states and putative inhibitors necessary for binding . the method is able to generate its own relationship between the quantum mechanical structure of the inhibitor and the strength of binding . feed - forward neural networks with back propagation of error can be trained to recognize the quantum mechanical electrostatic potential at the entire van der waals surface , rather than a collapsed representation , of a group of training inhibitors and to predict the strength of interactions between the enzyme and a group of novel inhibitors . the experimental results show that the neural networks can predict with quantitative accuracy the binding strength of new inhibitors . the method is in fact able to predict the large binding free energy of the transition state , when trained with less tightly bound inhibitors . the present method is also applicable to prediction of the binding free energy of a ligand to a receptor . the application of this approach to the study of transition state inhibitors and ligands would permit evaluation of chemical libraries of potential inhibitory , agonistic , or antagonistic agents . the method is amenable to incorporation in a computer - readable medium accessible by general - purpose computers .