Principal Component Analysis (PCA) as useful tool in predicting the biological activity of Buchenavianines

In silico prediction of ADME parameters, Lipinski’s Rule of Five, toxicity, or the reliability level of the predicted biological activity of a given structure against a specific pathogen are an integral part of modern drug discovery to accelerate the identification of promising lead compounds while reducing development costs and toxicity risks. In this context, multivariate statistical methods such as Principal Component Analysis (PCA) play an important role in the evaluation of large chemical and biological datasets, facilitating the identification of structural similarities, physicochemical trends, or predictions of pharmacological behavior. Natural products remain one of the most important sources of structurally diverse bioactive scaffolds, serving as inspiration for the development of novel therapeutics. Buchenavianines are a rare class of piperidine-flavonoid alkaloids isolated from tropical woody species of the genus Buchenavia (Combretaceae). Scientific interest in buchenavianines was initially stimulated by the discovery of potent anti-HIV activity in extracts of Buchenavia capitata, which demonstrated their ability to interfere with viral replication pathways and highlighted their potential as antiviral drug candidates. Studies have suggested that buchenavianines may possess anti-inflammatory, antioxidant, and anticancer potential, therefore they represent important lead compounds for medicinal chemistry in the development of a new generation of drugs. PCA analysis showed that the first two principal components explained 92.7% of the total variance (PC1 = 56%, PC2 = 36.7%), indicating that the selected descriptors adequately captured the variability among the studied buchenavianines. This study underscores the importance of these secondary metabolites as lead compounds in modern medicinal chemistry and drug discovery.