The ForceFit program package has been developed for fitting classical force field parameters based upon a force matching algorithm to quantum mechanical gradients of configurations that span the potential energy surface of the system. The program, which runs under UNIX and is written in C++, is an easy-to-use, nonproprietary platform that enables gradient fitting of a wide variety of functional force field forms to quantum mechanical information obtained from an array of common electronic structure codes. All aspects of the fitting process are run from a graphical user interface, from the parsing of quantum mechanical data, assembling of a potential energy surface database, setting the force field, and variables to be optimized, choosing a molecular mechanics code for comparison to the reference data, and finally, the initiation of a least squares minimization algorithm. Furthermore, the code is based on a modular templated code design that enables the facile addition of new functionality to the program.

Four different models are used to predict whether a compound will bind to 2C9 with a K i value of less than 10 µM. A training set of 276 compounds and a diverse validation set of 50 compounds were used to build and assess each model. The modeling methods are chosen to exploit the differences in how training sets are used to develop the predictive models. Two of the four methods develop partitioning trees based on global descriptions of structure using nine descriptors. A third method uses the same descriptors to develop local descriptions that relate activity to structures with similar descriptor characteristics. The fourth method uses a graph-theoretic approach to predict activity based on molecular structure. When all of these methods agree, the predictive accuracy is 94%. An external validation set of 11 compounds gives a predictive accuracy of 91% when all methods agree. IntroductionIdentifying drug-drug interaction potential early in drug discovery and development is important because drug-drug interactions can cause life threatening changes in drug levels. Early discovery of potential drug-drug interactions for a compound expedites the decision to eliminate that compound from consideration, thus lowering the cost of drug discovery. Virtual drug screening allows for the prediction of binding affinity prior to synthesis, and if prediction can be trusted, it can guide the drug discovery process. To date no single computational method has proven to be outstanding in this regard, and virtual screening still has not replaced in vitro screening methods or been routinely used in drug design. Drug interaction sites related to metabolism include UDP-glucuronosyltransferase, sulfotransferases, aldehyde oxidase, and the cytochrome P450 enzymes. Because a number of drug-drug interactions are observed for the cytochromes P450, affinity models have been developed for these enzymes. In particular, models have been developed for the three major drug metabolizing enzymes 2C9, 2D6, and 3A4.1-3 These metabolic enzymes have broad overlapping substrate specificity, most often coupled with relatively low affinity. Almost all substrates are also competitive inhibitors of the enzyme that metabolizes them, however, not all inhibitors are substrates. The general rule for drug-drug interactions is that compounds that have K i values, uncorrected for protein binding, greater than 10 µM are unlikely to exhibit important clinical drug-drug interactions. Since most compounds have K i values higher than 10 µM, including