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, pKa, molecular size and topology parameters). This approach favored interpretability over statistical performance but still achieved an overall classification accuracy of 75%. In the current follow-up work we expanded the database (provided in Supplementary Information) to almost 9400 molecules and performed temporal validation of the model on a set of novel chemicals from recently published lead optimization projects. Validation results showed almost no performance degradation compared to the original study. Additionally, we rebuilt the model using AFT (Accelerated Failure Time) learning objective in XGBoost, which accepts both quantitative and censored data often reported in protein inhibition studies. The new model achieved a similar level of accuracy of discerning hERG blockers from non-blockers at 10 µM threshold, which can be conceived as close to the performance ceiling for methods aiming to describe only non-specific ligand interactions with hERG. Yet, this model outputs quantitative potency values (IC50) and is not tied to a particular classification cut-off. pIC50 from patch-clamp measurements can be predicted with R2 ≈ 0.4 and MAE < 0.5, which enables ligand ranking according to their expected potency levels. The employed approach can be valuable for quantitative modeling of various ADME and drug safety endpoints with a high prevalence of censored data.

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. Various cheminformatics tools have been developed to address these metabolite identification challenges. This article describes the current state of these tools. They can be split into two categories: Pre-experimental metabolite generation and post-experimental data analysis. The former can be subdivided into rule-based, machine learning-based, and docking-based approaches. Post-experimental tools help scientists automatically perform chromatographic deconvolution of LC/MS data and identify metabolites. They can use pre-experimental predictions to improve metabolite identification, but they are not limited to these predictions: unexpected metabolites can also be discovered through fractional mass filtering. In addition to a review of available software tools, we present a description of pre-experimental and post-experimental metabolite structure generation using MetaSense. These software tools improve upon manual techniques, increasing scientist productivity and enabling efficient handling of large datasets. However, the trend of increasingly large datasets and highly data-driven workflows requires a more sophisticated informatics transition in metabolite identification labs. Experimental work has traditionally been separated from the information technology tools that handle our data. We argue that these IT tools can help scientists draw connections via data visualizations and preserve and share results via searchable centralized databases. In addition, data marshalling and homogenization techniques enable future data mining and machine learning.

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

An in silico study, using the GALAS algorithm available in ACD/PhysChem Suite, was performed to calculate the pK a(s) of various oximes with potential application as peptide coupling additives. Among the known oximes and predicted structures, OxymaPure is superior based on the pK a 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 pK a 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. For compounds within this domain the statistical results of the final model approach the data consistency between experimental data from literature and PubChem datasets with the overall accuracy of 89%. However, the original model is applicable only for less than a half of PubChem database. Since the similarity correction procedure of GALAS modeling method allows straightforward model training, the possibility to expand the applicability domain has been investigated. Experimental data from PubChem dataset served as an example of in-house high-throughput screening data. The model successfully adapted itself to both data classified using the same and different IC50 threshold compared with the training set. In addition, adjustment of the CYP3A4 inhibition model to compounds with a novel chemical scaffold has been demonstrated. The reported GALAS model is proposed as a useful tool for virtual screening of compounds for possible drug-drug interactions even prior to the actual synthesis.

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). For all the models considered in this study, the number of misclassifications and inconclusive results was reduced significantly when only predictions of high quality (RI>0.5) were taken into account, demonstrating that RI reflects accuracy of prediction. The applicability domain of regioselectivity models is shown to be easily expandable to cover compound classes of interest to the user. The results obtained so far show promising perspectives for the utilization of the GALAS modeling in the analysis of regioselectivity for other important biotransformation enzymes--a work currently in progress.