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We describe graph machines, an alternative approach to traditional machinelearning-based QSAR, which circumvents the problem of designing, computing and selecting molecular descriptors. In that approach, which is similar in spirit to recursive networks, molecules are considered as structured data, represented as graphs. For each example of the data set, a mathematical function (graph machine) is built, whose structure reflects the structure of the molecule under consideration; it is thedoi:10.1080/10629360601054313 pmid:17365965 fatcat:53gdkwazrvexvdmryckpgpzhkm