The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions. The models were based on a set of structural, physicochemical, and stereo-electronic descriptors and were generated by the random forest algorithm. For each considered biotransformation, two types of models were developed: the first type involved all non-reactive atoms and included atom types among the descriptors, while the second type involved only non-reactive centers having the same atom type(s) of the reactive atoms. All the models of the first type revealed very high performances; the models of the second type show on average worst performances while being almost always able to recognize the reactive centers; only conjugations with glucuronic acid are unsatisfactorily predicted by the models of the second type. Feature evaluation confirms the major role of lipophilicity, self-polarizability, and H-bonding for almost all considered reactions. The obtained results emphasize the possibility of recognizing the sites of metabolism by classification models trained on MetaQSAR database. The two types of models can be synergistically combined since the first models identify which atoms can undergo a given metabolic reactions, while the second models detect the truly reactive centers. The generated models are available as scripts for the VEGA program.

(1) Background: Within the framework of the European Interreg Italy–Switzerland B-ICE & Heritage project (2018–2022), this study originated from a three-year ethnobotanical survey in Valmalenco (Sondrio, Italy). Following a preliminary work published by our group, this research further explored the folk therapeutic use of Achillea erba-rotta subsp. moschata (Wulfen) I.Richardson (Asteraceae) for dyspepsia disorders, specifically its anti-inflammatory potential at a gastrointestinal level. (2) Methods: Semi-structured interviews were performed. The bitter taste was investigated through molecular docking software (PLANTS, GOLD), while the anti-inflammatory activity of the hydroethanolic extract, infusion, and decoction was evaluated based on the release of IL-8 and IL-6 after treatment with TNFα or Helicobacter pylori. The minimum inhibitory concentration and bacterial adhesion on the gastric epithelium were evaluated. (3) Results: In total, 401 respondents were interviewed. Molecular docking highlighted di-caffeoylquinic acids as the main compounds responsible for the interaction with bitter taste receptors. The moderate inhibition of IL-6 and IL-8 release was recorded, while, in the co-culture with H. pylori, stronger anti-inflammatory potential was expressed (29–45 μg/mL). The concentration-dependent inhibition of H. pylori growth was recorded (MIC = 100 μg/mL), with a significant anti-adhesive effect. (4) Conclusions: Confirming the folk tradition, the study emphasizes the species’ potentiality for dyspepsia disorders. Future studies are needed to identify the components mostly responsible for the biological effects.

Background/Objectives: D4R antagonists have recently been suggested as potential therapeutic alternatives to the standard treatments of glioblastoma (GBM). In this study, new piperidine-based ligands, analogs of the potent and selective D4R compounds 77-LH-28-1 (7) and its 4-benzyl analog 8, were synthesized and studied to investigate the effects produced by variations in the distances between the pharmacophoric features on the D4R affinity and selectivity. Methods: All the new compounds 9–20 were evaluated for their radioligand binding affinity at D2-like receptor subtypes and the results were rationalized by docking studies and molecular dynamics (MD) simulations. The functional profiles of the most interesting derivatives were assessed at D4R Go and Gi protein and β-arrestin by BRET assay and their potential anticancer activity was determined in GBM cell lines. Results: Radioligand binding results highlighted that the derivatives bearing a terminal butyl chain showed structure–activity relationships different from those with a benzyl terminal. From functional studies performed on the best derivatives 12 and 16, the response profiles of both compounds were more robust in antagonist mode, with derivative 16 showing higher antagonist potency than 12 across all three transducers. Interestingly, 12 and 16 dose-dependently decreased the cell viability of GBM cells, inducing cell death and cell cycle arrest, promoting an increase in ROS production, causing mitochondrial dysfunction, and significantly inhibiting colony formation. Conclusions: The promising biological profiles of 12 and 16 make them new lead candidates that warrant further investigation to gain a better understanding of the mechanism behind their antitumor activity and better evaluate their potential for GBM treatment.