Arthur F. Duprat

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The prediction of properties of molecules from their structure (QSAR) is basically a nonlinear regression problem. Neural networks are proven to be parsimonious universal approximators of nonlinear functions; therefore, they are excellent candidates for performing the nonlinear regression tasks involved in QSAR. However, their full potential can be(More)
In either sperm whale or horse heart myoglobin, binding of NO and lowering of solution pH work together to weaken, and ultimately break, the bond between iron and the proximal histidine. This is reminiscent of the reaction observed at neutral pH in the case of guanylate cyclase, the heme enzyme that catalyzes the conversion of GTP to cGMP. Bond breaking is(More)
The present paper is a short survey of the development of numerical learning from structured data, an old problem that was first addressed by the end of the years 1980, and has recently undergone exciting developments, both from a theoretical point of view and for applications. Traditionally, numerical machine learning deals with unstructured data, in the(More)
The recent developments of statistical learning focused on vector machines, which learn from examples that are described by vectors of features. However, there are many fields where structured data must be handled; therefore, it would be desirable to learn from examples described by graphs. Graph machines learn real numbers from graphs. Basically, for each(More)
Two calixarene-based model systems (a and b) for monocopper enzymes are compared. Both present a tris(pyridine) coordination site for Cu that mimics the imidazole-rich neutral binding site in enzymes. Upon reaction with 1 equiv of copper(I), the tridentate ligands gave rise to ill-defined unsymmetrical complexes. However, in the presence of an organonitrile(More)
Gadolinium(III) complexes constitute the largest class of compounds used as contrast agents for Magnetic Resonance Imaging (MRI). A quantitative structure-property relationship (QSPR) machine-learning based method is applied to predict the thermodynamic stability constants of these complexes (log KGdL), a property commonly associated with the toxicity of(More)
We investigate a prospective path to processing “big data” in the field of computer-aided drug design, motivated by the expected increase of the size of available databases. We argue that graph machines, which exempt the designer of a predictive model from handcrafting, selecting and computing ad hoc molecular descriptors, may open a way(More)
Four novel calix[6]arene-based cuprous complexes are described. They present a biomimetic tris(imidazole) coordination core associated with a hydrophobic cavity that wraps the apical binding site. Each differs from the other by the methyl or ethyl substituents present on the phenoxyl groups (OR1) and on the imidazole arms (NR2) of the calix[6]arene(More)
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