Abstract
<title>Abstract</title> <p>Background Medicinal plant alkaloids constitute a diverse class of naturally occurring nitrogen-containing secondary metabolites with well-established pharmacological activities and considerable potential for drug discovery. Early computational assessment of drug-likeness enables the identification of promising lead compounds by evaluating their physicochemical characteristics and oral drug-like properties prior to experimental investigations. Objective The present study aimed to perform a comparative computational evaluation of the drug-likeness profiles and Lipinski's Rule of Five compliance of twenty medicinal plant alkaloids using the MolSoft Molecular Property Prediction Server. Methods Twenty pharmacologically important medicinal plant alkaloids were selected through an extensive literature survey. Canonical SMILES for each compound were retrieved from the PubChem database and analysed using the MolSoft Molecular Property Prediction Server. Drug-likeness scores were computed, and compliance with Lipinski's Rule of Five was assessed to estimate the suitability of each compound for oral drug development. Results Drug-likeness scores varied considerably among the evaluated alkaloids, ranging from 0.25 to 0.84. Piperine exhibited the highest drug-likeness score (0.84), followed by Berberine (0.82) and Sanguinarine (0.80), indicating favourable drug-like characteristics. Fifteen of the twenty alkaloids complied with Lipinski's Rule of Five, whereas five compounds (Reserpine, Solanine, Brucine, Vindoline, and Emetine) exhibited one or more violations, suggesting comparatively lower oral drug suitability. The findings demonstrate substantial variability in drug-likeness among medicinal plant alkaloids and facilitate the prioritization of promising candidates for further computational and experimental investigations. Conclusion This comparative computational study highlights the utility of MolSoft-based drug-likeness evaluation as an efficient preliminary screening approach for medicinal plant alkaloids. The generated results provide valuable insights for lead identification and prioritization and may support future molecular docking, QSAR modelling, ADMET prediction, medicinal chemistry, and natural product-based drug discovery.</p>