Computational Characterization of Alkaloid RW47 Venoterpine: DFT, Drug-Likeness, and Target Prediction Insights for Therapeutic Potential

Abstract

Abstract

Venoterpine (ARV) is a naturally occurring alkaloid with an underexplored pharmacological profile that warrants systematic investigation for therapeutic applications. The current study employed multi-faceted in-silico approaches to investigate the potential of ARV comprehensively. The SwissADME analysis showed satisfactory physicochemical properties and good water solubility and lipophilic properties. Additionally, the compound exhibited favorable pharmacokinetic properties, having high gastrointestinal absorption, the ability to cross the blood-brain barrier, non-substrate status for P-glycoprotein (P-gp), and no inhibition of cytochrome P450 enzymes. The compound also showed adherence to different druglikeness filters, including Lipinski’s rule of five, and predicted an excellent medicinal chemistry. The DFT computations were performed at the B3LYP level and at a 6-311G∗∗ basis set, which calculated ionization potential -6.6537 eV, electron affinity -0.6989 eV, and 5.9548 eV energy gap, suggesting a stable nature of the compound inside the biological system. The dipole moment of 3.6290 Debye suggests potential for polar interaction and influencing its solubility, while the electrophilicity index of 2.2696 indicates moderate reactivity. In silico target prediction identified probable biological targets of ARV, predicting potential interactions with oxidoreductases (33%), cytochrome P450 enzymes (13%), kinases (13%), lyases (20%), membrane receptors (6.7%), and unspecified proteins (6.7%). The integrated insights from SwissADME, DFT analysis, and target prediction provide a holistic picture of ARV's potential as a bioactive molecule and warrant further experimental investigation.