HATS5m as an Example of GETAWAY Molecular Descriptor in Assessing the Similarity/Diversity of the Structural Features of 4-Thiazolidinone.

Among the various methods for drug design, the approach using molecular descriptors for quantitative structure-activity relationships (QSAR) bears promise for the prediction of innovative molecular structures with bespoke pharmacological activity. Despite the growing number of successful potential applications, the QSAR models often remain hard to interpret. The difficulty arises from the use of advanced chemometric or machine learning methods on the one hand, and the complexity of molecular descriptors on the other hand. Thus, there is a need to interpret molecular descriptors for identifying the features of molecules crucial for desirable activity. For example, the development of structure-activity modeling of different molecule endpoints confirmed the usefulness of H-GETAWAY (H-GEometry, Topology, and Atom-Weights AssemblY) descriptors in molecular sciences. However, compared with other 3D molecular descriptors, H-GETAWAY interpretation is much more complicated. The present study provides insights into the interpretation of the HATS5m descriptor (H-GETAWAY) concerning the molecular structures of the 4-thiazolidinone derivatives with antitrypanosomal activity. According to the published study, an increase in antitrypanosomal activity is associated with both a decrease and an increase in HATS5m (leverage-weighted autocorrelation with lag 5, weighted by atomic masses) values. The substructure-based method explored how the changes in molecular features affect the HATS5m value. Based on this approach, we proposed substituents that translate into low and high HATS5m. The detailed interpretation of H-GETAWAY descriptors requires the consideration of three elements: weighting scheme, leverages, and the Dirac delta function. Particular attention should be paid to the impact of chemical compounds' size and shape and the leverage values of individual atoms.

An application of QSRR approach and multiple linear regression method for lipophilicity assessment of flavonoids.

The analysis of quantitative structure-retention relationships (QSRR) is useful tool for assessment of compound's lipophilicity/hydrophobicity due to similarity between its retention in chromatographic system and ability to permeation through biological membranes. The main goal of this study was to compare usefulness of two reversed-phase chromatographic columns (Synergy POLAR and Synergy-FUSION) for lipophilicity assessment of 30 structurally diverse flavonoids using the QSRR approach and multiple linear regression method. The developed MLR models included the mechanistically interpretable geometrical descriptors: 3D Molecule Representation of Structure based on Electron diffraction (3D-MoRSE) and Radial Distribution Function (RDF). Both models were evaluated by the internal and external validation and selected descriptors were further interpreted. According to obtained results the FUSION-RP column can be recommended to log kw prediction of flavonoids. The comprehensive interpretation of molecular descriptors was used to present the molecular mechanisms and structural features governing the chromatographic retention of tested compounds.