Tensor algebra-based geometric methodology to codify central chirality on organic molecules.

A novel mathematical procedure to codify chiral features of organic molecules in the QuBiLS-MIDAS framework is introduced. This procedure constitutes a generalization to that commonly used to date, where the values 1 and -1 (correction factor) are employed to weight the molecular vectors when each atom is labelled as R (rectus) or S (sinister) according to the Cahn-Ingold-Prelog rules. Therefore, values in the range [Formula: see text] with steps equal to 0.25 may be accounted for. The atoms labelled R or S can have negative and positive values assigned (e.g. -3 for an R atom and 1 for an S atom, or vice versa), opposed values (e.g. -3 for an R atom and 3 for an S atom, or vice versa), positive values (e.g. 3 for an R atom and 1 for an S atom) or negative values (e.g. -3 for an R atom and -1 for an S atom). These proposed Chiral QuBiLS-MIDAS 3D-MDs are real numbers, non-symmetric and reduced to 'classical' (non-chiral) QuBiLS-MIDAS 3D-MDs when symmetry is not codified (correction factor equal to zero). In this report, only the factors with opposed values were considered with the purpose of demonstrating the feasibility of this proposal. From QSAR modelling carried out on four chemical datasets (Cramer's steroids, fenoterol stereoisomer derivatives, N-alkylated 3-(3-hydroxyphenyl)-piperidines, and perindoprilat stereoisomers), it was demonstrated that the use of several correction factors contributes to the building of models with greater robustness and predictive ability than those reported in the literature, as well as with respect to the models exclusively developed with QuBiLS-MIDAS 3D-MDs based on the factor 1 | -1. In conclusion, it can be stated that this novel strategy constitutes a suitable alternative to computed chirality-based descriptors, contributing to the development of good models to predict properties depending on symmetry.

Allosteric modulation model of the mu opioid receptor by herkinorin, a potent not alkaloidal agonist.

Modulation of opioid receptors is the primary choice for pain management and structural information studies have gained new horizons with the recently available X-ray crystal structures. Herkinorin is one of the most remarkable salvinorin A derivative with high affinity for the mu opioid receptor, moderate selectivity and lack of nitrogen atoms on its structure. Surprisingly, binding models for herkinorin are lacking. In this work, we explore binding models of herkinorin using automated docking, molecular dynamics simulations, free energy calculations and available experimental information. Our herkinorin D-ICM-1 binding model predicted a binding free energy of -11.52 ± 1.14 kcal mol-1 by alchemical free energy estimations, which is close to the experimental values -10.91 ± 0.2 and -10.80 ± 0.05 kcal mol-1 and is in agreement with experimental structural information. Specifically, D-ICM-1 molecular dynamics simulations showed a water-mediated interaction between D-ICM-1 and the amino acid H2976.52, this interaction coincides with the co-crystallized ligands. Another relevant interaction, with N1272.63, allowed to rationalize herkinorin's selectivity to mu over delta opioid receptors. Our suggested binding model for herkinorin is in agreement with this and additional experimental data. The most remarkable observation derived from our D-ICM-1 model is that herkinorin reaches an allosteric sodium ion binding site near N1503.35. Key interactions in that region appear relevant for the lack of ß-arrestin recruitment by herkinorin. This interaction is key for downstream signaling pathways involved in the development of side effects, such as tolerance. Future SAR studies and medicinal chemistry efforts will benefit from the structural information presented in this work.

Conformation-dependent QSAR approach for the prediction of inhibitory activity of bromodomain modulators.

Epigenetic drug discovery is a promising research field with growing interest in the scientific community, as evidenced by the number of publications and the large amount of structure-epigenetic activity information currently available in the public domain. Computational methods are valuable tools to analyse and understand the activity of large compound collections from their structural information. In this manuscript, QSAR models to predict the inhibitory activity of a diverse and heterogeneous set of 88 organic molecules against the bromodomains BRD2, BRD3 and BRD4 are presented. A conformation-dependent representation of the chemical structures was established using the RDKit software and a training and test set division was performed. Several two-linear and three-linear QuBiLS-MIDAS molecular descriptors ( www.tomocomd.com ) were computed to extract the geometric structural features of the compounds studied. QuBiLS-MIDAS-based features sets, to be used in the modelling, were selected using dimensionality reduction strategies. The multiple linear regression procedure coupled with a genetic algorithm were employed to build the predictive models. Regression models containing between 6 to 9 variables were developed and assessed according to several internal and external validation methods. Analyses of outlier compounds and the applicability domain for each model were performed. As a result, the models against BRD2 and BRD3 with 8 variables and the model with 9 variables against BRD4 were those with the best overall performance according to the criteria accounted for. The results obtained suggest that the models proposed will be a good tool for studying the inhibitory activities of drug candidates against the bromodomains considered during epigenetic drug discovery.

How to recognize and workaround pitfalls in QSAR studies: a critical review.

Quantitative Structure-Activity Relationships (QSAR) are based on the hypothesis that changes in molecular structure reflect proportional changes in the observed response or biological activity. In order to successfully conduct QSAR studies certain conditions have to be met that are not frequently reported in the literature. This suggests that some authors are not aware of the principle flaws, occasional shortcomings, and circumstantial downsides of QSAR methods. The present paper focuses on prerequisites to set up correct models and on limitations of model applications. Their implications are systematically described and illustrated as pitfalls that have strong implications in QSAR, and possible solutions are suggested. The paper is focused on small scale 2D- and 3D-QSAR studies for lead optimization. The work is enriched with comprehensive comments and non-mathematical explanations for the computer practitioner in Medicinal Chemistry.