MOLGMP: A Markov approach for molecular graph generation with GNNs

Molecule generation has experienced multiple breakthroughs in recent years. While traditional techniques are very reliable they have low space exploration potential; therefore, machine learning techniques are needed to delve deep into the vast space of potential compounds, helping to find new ways of designing candidate molecules. This paper presents a sequential Markovian model based on graph neural networks for molecular generation. The model employs a Breadth–First Search (BFS) ordering strategy and a modular architecture to enhance independence between functions, with a focus on maintaining strict independence at each step of the Markovian process. Experimental results on ZINC (MOSES), ZINC (250 K) and Polymers datasets demonstrate the model’s ability to perform both unconditional and conditional molecule generation while preserving dataset properties. Notably, the step–wise approach achieves state–of–the–art results in terms of uniqueness and validity without using valency masks.