The cytochrome P450 (P450) superfamily plays an important role in the metabolism of drug compounds, and it is therefore highly desirable to have models that can predict whether a compound interacts with a specific isoform of the P450s. In this work, we provide in silico models for classification of CYP1A2 inhibitors and noninhibitors. Training and test sets consisted of approximately 400 and 7000 compounds, respectively. Various machine learning techniques, such as binary quantitative structure activity relationship, support vector machine (SVM), random forest, kappa nearest neighbor (kNN), and decision tree methods were used to develop in silico models, based on Volsurf and Molecular Operating Environment descriptors. The best models were obtained using the SVM, random forest, and kNN methods in combination with the BestFirst variable selection method, resulting in models with 73 to 76% of accuracy on the test set prediction (Matthews correlation coefficients of 0.51 and 0.52). Finally, a decision tree model based on Lipinski's Rule-of-Five descriptors was also developed. This model predicts 67% of the compounds correctly and gives a simple and interesting insight into the issue of classification. All of the models developed in this work are fast and precise enough to be applicable for virtual screening of CYP1A2 inhibitors or noninhibitors or can be used as simple filters in the drug discovery process.

Motivation: Cytochromes P450 are the most important class of drug metabolizing enzymes. Prediction of drug metabolism is important in development of new drugs, to understand and reduce adverse drug reactions and to reduce animal testing. Results: SMARTCyp 3.0 is an updated version of our previous web server for prediction of site-of-metabolism for Cytochrome P450-mediated metabolism, now in Python 3 with increased structural coverage and new features. The SMARTCyp program is a first principle-based method using density functional theory determined activation energies for more than 250 molecules to identify the most likely site-of-metabolism. New features include a similarity measure between the query molecule and the model fragment, a new graphical interface and additional parameters expanding the structural coverage of the SMARTCyp program. Availability and implementation: The SMARTCyp server is freely available for use on the web at smartcyp.sund.ku.dk. Supplementary information: Supplementary data are available at Bioinformatics online.

The serotonin transporter (SERT) regulates extracellular levels of the neurotransmitter serotonin (5-hydroxytryptamine) in the brain by facilitating uptake of released 5-hydroxytryptamine into neuronal cells. SERT is the target for widely used antidepressant drugs, including imipramine, fluoxetine, and (S)-citalopram, which are competitive inhibitors of the transport function. Knowledge of the molecular details of the antidepressant binding sites in SERT has been limited due to lack of structural data on SERT. Here, we present a characterization of the (S)-citalopram binding pocket in human SERT (hSERT) using mutational and computational approaches. Comparative modeling and ligand docking reveal that (S)-citalopram fits into the hSERT substrate binding pocket, where (S)-citalopram can adopt a number of different binding orientations. We find, however, that only one of these binding modes is functionally relevant from studying the effects of 64 point mutations around the putative substrate binding site. The mutational mapping also identify novel hSERT residues that are crucial for (S)-citalopram binding. The model defines the molecular determinants for (S)-citalopram binding to hSERT and demonstrates that the antidepressant binding site overlaps with the substrate binding site.