Discrete graph generative models for small molecule generation
García Alfocea, Laura · UPCommons institutional repository (Universitat Politècnica de Catalunya) · 2025
This project introduces CatMol, a generative model designed to create small molecules by representing them as graphs, using node and edge matrices with categorical attributes. A discrete diffusion probabilistic model is applied to these representations, leveraging a marginal distribution derived from the dataset, which improves performance compared to a uniform distribution. To ensure that the model effectively learns the graph distribution, an attention mechanism incorporating edge information is employed. The model is trained on multiple datasets and evaluated using various performance metrics. On drug-like datasets, CatMol demonstrates comparable performance to state-of-the-art models, with superior results in certain metrics. Additionally, the model’s performance improves with the size of the dataset, suggesting scalability. Future work will involve training on even larger datasets to further validate its scalability and potential.