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Background: Saponin metabolism shows high structural variability due to the diversity of aglycones and glycosylations (Gly). Although they represent a potential source of drug design, their metabolism remains misunderstood yet due to insufficient investments in analytical methods. Aims: Bibliographic structural data offer a wide field for extensive statistical analysis, highlighting mechanistic orders governing metabolic diversity. This work presents an original simulation method based on simplex rule for highlighting regulatory mechanisms of metabolism from categorical structural data. Methods: Simulation was applied on a set of 231 saponins of the Caryophyllaceae plant family initially affiliated to four aglycone types: gypsogenin (Gyp), quillaic acid (QA), gypsogenic acid (GA), and 16-OH-gypsogenic acid (16-OH-GA). Molecules were initially characterized by relative glycosylation levels of different carbons. Simplex approach was applied by combining saponins of the four aglycone groups using a complete set of N gradual weightings between structural groups. In silico combinations were applied by randomly sampling representative saponins from the four groups conforming to their weights given by mixture design. Gly profiles of sampled saponins were averaged to calculate a barycentric molecular profile for each mixture. With N mixtures, N barycentric molecules were iteratively calculated by bootstrap, leading to smoothed data from which Gly trends between carbons were highlighted. Results: Sequential, competing and cooperative Gly trends were highlighted according to the types of aglycones, attached saccharides and positions of substituted carbons. Such various conditional Gly trends seemed to be linked to multiple factors, including steric effects, regio-selectivity, enzymatic specificity and enzymatic promiscuity. These simulated results could be helpfully useful in chemical synthesis and drug design. Conclusion: These simulated results could usefully help for chemical syntheses and drug design.

International audience; Metabolisms represent highly organized systems characterized by strong regulations satisfying the mass conservation principle. This makes a whole chemical resource to be competitively shared between several ways at both intra-and inter-molecular scales. Whole resource sharing can be statistically associated with a constant sum-unit constraint which represents the basis of simplex mixture rule. In this work, a new simplex-based simulation approach was developed to learn scaffold information on metabolic processes controlling molecular diversity from a wide set of observed chemical structures. Starting from a dataset of chemical structures classified into p clusters, a machine learning process was applied by linearly combining the p clusters j and randomly sampling a constant number (n) of molecules according to different clusters' weights (wj/w) given by Scheffé's mixture matrix. At the output of mixture design, molecular linear combinations lead to calculate barycentric molecules integrating the characteristics of the different weighted clusters. The N mixtures-design was iterated by bootstrap technique leading to extensive exploration of chemical variability between and within clusters. Finally, the K response matrices issued from K iterated mixture designs were averaged to calculate a smoothed matrix containing scaffold information on regulation processes responsible for molecular diversification at inter- and intra-molecular (atomic) scales. This matrix was used as a backbone for graphical analysis of positive and negative trends between atomic characteristics (chemical substitutions) at both mentioned scales. This new simplex approach was illustrated by cycloartane-based saponins of Astragalus genus by combining three desmosylation clusters characterized by relative glycosylation levels of different aglycones' carbons.