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.

A combined experimental and computational chiroptical approach to establish the biosynthesis and absolute configuration of licochalcone L

Often, chiral natural products exist as single stereoisomers; however, simultaneous occurrences of both enantiomers can exist in nature, resulting in scalemic or racemic mixtures. Ascertaining natural products' absolute configuration (AC) is pivotal for attributing their specific biological signature. Specific rotation data commonly characterize chiral non-racemic natural products; however, measurement conditions, viz., solvent and concentration, can influence the sign of specific rotation values, especially when characterizing natural products possessing small specific rotation values. For example, licochalcone L, a minor constituent of Glycyrrhiza inflata, was reported with a specific rotation of [α]_D²²= +13 (c 0.1, CHCl₃); however, not establishing the AC and the reported zero specific rotation for an identical compound, licochalcone AF1, resulted in debatable chirality and its biogenesis. In this study, a combined experimental and computational chiroptical approach involving specific rotation and electronic circular dichroism (ECD) data, supported by time-dependent density functional theory (TDDFT), were effectively utilized to establish the AC of licochalcone L as the (E, 2″S)-isomer. Establishing the 2″S absolute configuration permitted the conception of a reasonable biosynthetic pathway involving intramolecular '5-exo-tet' ring opening of a chiral oxirane to form chiral licochalcone L in G. inflata.