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)
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)
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)
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)
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)
The recent developments of statistical learning focused mainly on vector machines, i.e. on machines that learn from examples described by a vector of features. There are many fields where structured data must be handled; therefore, it would be desirable to learn from examples described by graphs. The presentation describes graph machines, which learn real(More)
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