Several QSAR models have been developed using a linear optimization approach that enabled distinguishing metabolic substances isolated from human-, bacterial-, plant- and fungal- cells. Seven binary classifiers based on a k-Nearest Neighbors method have been created using a variety of 'inductive' and traditional QSAR descriptors that allowed up to 95% accurate recognition of the studied groups of chemical substances. The conducted comparative QSAR analysis based on the above mentioned linear optimization approach helped to identify the extent of overlaps between the groups of compounds, such as cross-recognition of fungal and bacterial metabolites and association between fungal and plant substances. Human metabolites exhibited very different QSAR behavior in chemical space and demonstrated no significant overlap with bacterial-, fungal-, and plant-derived molecules. When the developed QSAR models were applied to collections of conventional human therapeutics and antimicrobials, it was observed that the first group of substances demonstrate the strongest association with human metabolites, while the second group exhibit tendency of 'bacterial metabolite - like' behavior. We speculate that the established 'drugs - human metabolites' and 'antimicrobials - bacterial metabolites' associations result from strict bioavailability requirements imposed on conventional therapeutic substances, which further support their metabolite-like properties. It is anticipated that the study may bring additional insight into QSAR determinants for human-, bacterial-, fungal- and plant metabolites and may help rationalizing design and discovery of novel bioactive substances with improved, metabolite-like properties.