Deep Graph Learning (DGL) which is based on ANNs, is capable of learning from graph-structured data . It is included as part of the ProSurfScan platform developed by Chemotargets. This platform has been successfully applied to the identification of novel compounds for different targets. It yielded the first AI-designed drug for Huntington's disease, which is currently in clinical trials . ProSurfScan allows estimation of the compatibility and binding mode of fragments on different regions of a protein surface. Therefore, the protein surface is represented as a complete graph consisting of nodes with pharmacophoric features derived from the analysis of a triangulated mesh representation of the protein surface . Two complementary methods are employed to carry out the predictions. A clique detection algorithm is used to compare the protein surface with known surfaces associated with fragments from ligands present in structures from the Protein Data Bank (PDB) (aka fragment environments). This allows placement of the fragment based on the largest subgraph found between the fragment-environment and the protein surface. In addition, a series of DGL models is built using Graph Convolutional Neural Networks (GCNN) that estimate the compatibility of the fragments with respect to distinct regions of the protein surface.