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
translated by 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 extract scaffold information on
metabolic processes controlling molecular diversity from a wide set of observed chemical structures.
Starting from a wide dataset of chemical structures initially classified into p clusters, a machine learning
process was applied by linearly combining the p clusters j through several (N) samplings of a constant
number (n) of molecules by respecting different clusters’ weights (wj/w) given by Scheffé’s mixture matrix.
At the output of mixture design, the N molecular linear combinations lead to calculate N barycentric
molecules integrating the characteristics of the different weighted clusters. The mixture-design was iterated
by bootstrap technique for extensive exploration of chemical variability between and within clusters.
Finally, the K response matrices resulting 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 multidirectional 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.
Keywords: Computational chemistry, Simulation, Training, Molecular diversity, Cycloartane, Glycosylation, Desmosylation.
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