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Limits with modeling data and modeling data with limits


Lionello Pogliani

Dipartimento di Chimica, Università della Calabria, 87030 Rende (CS), Italy., IT
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Modeling of the solubility of amino acids and purine and pyrimidine bases with a set of sixteen molecular descriptors has been thoroughly analyzed to detect and understand the reasons for anomalies in the description of this property for these two classes of compounds. Unsatisfactory modeling can be ascribed to incomplete collateral data, i.e, to the fact that there is insufficient data known about the behavior of these compounds in solution. This is usually because intermolecular forces cannot be modeled. The anomalous modeling can be detected from the rather large values of the standard deviation of the estimates of the whole set of compounds, and from the unsatisfactory modeling of some of the subsets of these compounds. Thus the detected abnormalities can be used (i) to get an idea about weak intermolecular interactions such as hydration, self-association, the hydrogen-bond phenomena in solution, and (ii) to reshape the molecular descriptors with the introduction of parameters that allow better modeling. This last procedure should be used with care, bearing in mind that the solubility phenomena is rather complex.
How to Cite: Pogliani, L., 2006. Limits with modeling data and modeling data with limits. Data Science Journal, 1(2), pp.203–215. DOI:
Published on 05 Jan 2006.
Peer Reviewed


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