Physicochemical QSAR analysis of hERG inhibition revisited: towards a quantitative potency prediction.

In an earlier study (Didziapetris R & Lanevskij K (2016). J Comput Aided Mol Des. 30:1175-1188) we collected a database of publicly available hERG inhibition data for almost 6700 drug-like molecules and built a probabilistic Gradient Boosting classifier with a minimal set of physicochemical descriptors (log P, pK, molecular size and topology parameters).

Impact of Established and Emerging Software Tools on the Metabolite Identification Landscape.

Scientists' ability to detect drug-related metabolites at trace concentrations has improved over recent decades. High-resolution instruments enable collection of large amounts of raw experimental data. In fact, the quantity of data produced has become a challenge due to effort required to convert raw data into useful insights.

Understanding OxymaPure as a Peptide Coupling Additive: A Guide to New Oxyma Derivatives.

An study, using the GALAS algorithm available in ACD/PhysChem Suite, was performed to calculate the p (s) of various oximes with potential application as peptide coupling additives. Among the known oximes and predicted structures, OxymaPure is superior based on the p values calculated, confirming the results described in the literature and validating this algorithm for further use in that field. Among the nondescribed oximes, based on p calculation, ethyl 2-(hydroxyimino)-2-nitroacetate seems to be a potential candidate to be used as an additive during peptide coupling.

Trainable structure-activity relationship model for virtual screening of CYP3A4 inhibition.

A new structure-activity relationship model predicting the probability for a compound to inhibit human cytochrome P450 3A4 has been developed using data for >800 compounds from various literature sources and tested on PubChem screening data. Novel GALAS (Global, Adjusted Locally According to Similarity) modeling methodology has been used, which is a combination of baseline global QSAR model and local similarity based corrections. GALAS modeling method allows forecasting the reliability of prediction thus defining the model applicability domain.

Probabilistic prediction of the human CYP3A4 and CYP2D6 metabolism sites.

This article briefly introduces the results of in silico prediction of the most probable metabolism sites for the human cytochrome P450 3A4 and 2D6 isoforms. Ligand-based QSAR models have been developed using a novel GALAS modeling approach, and provide probabilities of being a target of CYP3A4 or CYP2D6 for any atom in a molecule. The GALAS-model development methodology allows evaluation of the reliability of predictions in the form of estimated prediction Reliability Indices (RIs).