Use of the R-group descriptor for alignment-free QSAR

An R-group descriptor characterises the distribution of some atom-based property, such as elemental type or partial atomic charge, at increasing numbers of bonds distant from the point of substitution on a parent ring system. Application of Partial Least Squares (PLS) to datasets for which bioactivity data and R-group descriptor information are available is shown to provide an effective way of generating QSAR models with a high level of predictive ability. The resulting models are competitive with the models produced by established QSAR approaches, are readily interpretable in structural terms, and are shown to be of value in the optimisation of a lead series

Prediction of pKa Using Machine Learning Methods with Rooted Topological Torsion Fingerprints: Application to Aliphatic Amines.

The acid-base dissociation constant, p, is a key parameter to define the ionization state of a compound and directly affects its biopharmaceutical profile. In this study, we developed a novel approach for p prediction using rooted topological torsion fingerprints in combination with five machine learning (ML) methods: random forest, partial least squares, extreme gradient boosting, lasso regression, and support vector regression. With a large and diverse set of 14 499 experimental p values, p models were developed for aliphatic amines. The models demonstrated consistently good prediction statistics and were able to generate accurate prospective predictions as validated with an external test set of 726 p values (RMSE 0.45, MAE 0.33, and 0.84 by the top model). The factors that may affect prediction accuracy and model applicability were carefully assessed. The results demonstrated that rooted topological torsion fingerprints coupled with ML methods provide a promising approach for developing accurate p prediction models

Insight into the mechanism of inactivation and pH sensitivity in potassium channels from molecular dynamics simulations

Potassium (K+) channels can regulate ionic conduction through their pore by a mechanism, involving the selectivity filter, known as C-type inactivation. This process is rapid in the hERG K+ channel and is fundamental to its physiological role. Although mutations within hERG are known to remove this process, a structural basis for the inactivation mechanism has yet to be characterized. Using MD simulations based on homology modeling, we observe that the carbonyl of the filter aromatic, Phe627, forming the S-0 K+ binding site, swiftly rotates away from the conduction axis in the wild-type channel. In contrast, in well-characterized non-inactivating mutant channels, this conformational change occurs less frequently. In the non-inactivating channels, interactions with a water molecule located behind the selectivity filter are critical to the enhanced stability of the conducting state. We observe comparable conformational changes in the acid sensitive TASK-1 channel and propose a common mechanism in these channels for regulating efflux of K+ ions through the selectivity filter