MOLGMP: A Markov approach for molecular graph generation with GNNs

Benoit Goupil, Antonin Joly, Niccolò Pancino, Pietro Bongini, Franco Scarselli, Monica Bianchini

Abstract: Highlights•A novel sequential approach to molecular graph generation is presented.•The method exploits Graph Neural Networks to maximize information exploitation.•The employed Markov process and the architecture ensure a flexible learning procedure.•Conditional generation allows tailoring the generated molecules to the objectives of the specific studies.•The experimentation shows that the approach outperforms thestate-of-the-art.