When: Nov 9th, 2022 – 11:00 – 11:30 AM
Where: Google meet link
Drug Side Effect Prediction with Deep Learning Molecular Embedding in a Graph of Graphs Domain
In collaboration with Yohann Perronn, Pietro Bongini and Franco Scarselli
Drug Side Effects (DSEs) or Adverse Drug Reactions (ADRs) constitute an important health risk, with approximately 197.000 annual deaths, in Europe alone. Therefore, during the drug development process, DSEs detection is of utmost importance and the occurrence of ADRs prevents many candidate molecules to go through clinical trials. Thus, early prediction of DSEs has the potential to massively reduce drug development times and costs. In this work, data is represented in a non–euclidean manner, in the form of a graph of graphs domain. In such domain, structures of molecule are represented by molecular graphs, each of which becomes a node in the higher level graph. In the latter, nodes stand for drugs and genes, and arcs represent their relationships. Such relational nature represents an important novelty for the DSE prediction task and it is directly used during the prediction. For this purpose, the MolecularGNN model is proposed. This new classifier is based on Graph Neural Networks, a connectionist model capable of processing data in form of graphs. The approach represents an improvement over a previous method, called DruGNN, as it is also capable of extracting information from the graph–based molecular structures, producing a task–based neural fingerprint (NF) of the molecule which is adapted to the specific task. The architecture has been compared with other GNN models in terms of performance, showing that the proposed approach is very promising.