Machine learning and traditional QSAR modeling methods: a case study of known PXR activators

Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure-activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R2) of 0.70 versus an R2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures.

New Sulfanilamide Derivatives Incorporating Heterocyclic Carboxamide Moieties as Carbonic Anhydrase Inhibitors

Carbonic Anhydrases (CAs) are ubiquitous metalloenzymes involved in several disease conditions. There are 15 human CA (hCA) isoforms and their high homology represents a challenge for the discovery of potential drugs devoid of off-target side effects. For this reason, many synthetic and pharmacologic research efforts are underway to achieve the full pharmacological potential of CA modulators of activity. We report here a novel series of sulfanilamide derivatives containing heterocyclic carboxamide moieties which were evaluated as CA inhibitors against the physiological relevant isoforms hCA I, II, IX, and XII. Some of them showed selectivity toward isoform hCA II and hCA XII. Molecular docking was performed for some of these compounds on isoforms hCA II and XII to understand the possible interaction with the active site amino acid residues, which rationalized the reported inhibitory activity.