An improved method for computing potential-derived charges is described which is based upon the CHELP program available from QCPE.' This approach (CHELPG) is shown to be considerably less dependent upon molecular orientation than the original CHELP program. In the second part of this work, the CHELPG point selection algorithm was used to analyze the changes in the potential-derived charges in formamide during rotation about the C-N bond. In order to achieve a level of rotational invariance less than 10% of the magnitude of the electronic effects studied, a n equally-spaced array of points 0.3 A apart was required. Points found to be greater than 2.8 A from any nucleus were eliminated, along with all points contained within the defined VDW distances from each of the atoms. The results are compared to those obtained by using CHELP. Even when large numbers of points (ca. 3000) were sampled using the CHELP selection routine, the results did not indicate a satisfactory level of rotational invariance. On the basis of these results, the original CHELP program was found to be inadequate for analyzing internal rotations.

SMARTCyp is an in silico method that predicts the sites of cytochrome P450-mediated metabolism of druglike molecules. The method is foremost a reactivity model, and as such, it shows a preference for predicting sites that are metabolized by the cytochrome P450 3A4 isoform. SMARTCyp predicts the site of metabolism directly from the 2D structure of a molecule, without requiring calculation of electronic properties or generation of 3D structures. This is a major advantage, because it makes SMARTCyp very fast. Other advantages are that experimental data are not a prerequisite to create the model, and it can easily be integrated with other methods to create models for other cytochrome P450 isoforms. Benchmarking tests on a database of 394 3A4 substrates show that SMARTCyp successfully identifies at least one metabolic site in the top two ranked positions 76% of the time. SMARTCyp is available for download at http://www.farma.ku.dk/p450.

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This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as “metabolophores”: A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent,i isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.