Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models.

In-silico models were generated to predict the extent of inhibition of cytochrome P450 isoenzymes using a set of relatively interpretable descriptors in conjunction with partial least squares (PLS) and regression trees (RT). The former was chosen due to the conservative nature of the resultant models built and the latter to more effectively account for any non-linearity between dependent and independent variables. All models are statistically significant and agree with the known SAR and they could be used as a guide to P450 liability through a classification based on the continuous pIC50 prediction given by the model.

Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data.

A 'global' model of hERG K(+) channel was built to satisfy three basic criteria for QSAR models in drug discovery: (1) assessment of the applicability domain, (2) assuring that model decisions can be interpreted by medicinal chemists and (3) assessment of model performance after the model was built. A combination of D-optimal onion design and hierarchical partial least squares modelling was applied to construct a global model of hERG blockade in order to maximize the applicability domain of the model and to enhance its interpretability. Additionally, easily interpretable hERG specific fragment-based descriptors were developed.

State-of-the-art tools for computational site of metabolism predictions: comparative analysis, mechanistical insights, and future applications.

In drug design, it is crucial to have reliable information on how a chemical entity behaves in the presence of metabolizing enzymes. This requires substantial experimental efforts. Consequently, being able to predict the likely site/s of metabolism in any compound, synthesized or virtual, would be highly beneficial and time efficient.

Reaction site mapping of xenobiotic biotransformations.

Predictive metabolism methods can be used in drug discovery projects to enhance the understanding of structure-metabolism relationships. The present study uses data mining methods to exploit biotransformation data that have been recorded in the MDL Metabolite database. Reacting center fingerprints were derived from a comparison of substrates and their corresponding products listed in the database.