Bayesian Flow Network Framework for Chemistry Tasks

• Published in: Journal of chemical information and modeling 2025-01
• Main Authors: Tao, Nianze, Abe, Minori
• Format: Article
• Language: English
• ISSN: 1549-9596; 1549-960X; 1549-960X
• DOI: 10.1021/acs.jcim.4c01792

In this work, we introduce ChemBFN, a language model that handles chemistry tasks based on Bayesian flow networks working with discrete data. A new accuracy schedule is proposed to improve sampling quality by significantly reducing reconstruction loss. We show evidence that our method is appropriate for generating molecules with satisfied diversity, even when a smaller number of sampling steps is used. A classifier-free guidance method is adapted for conditional generation. It is also worthwhile to point out that after generative training, our model can be fine-tuned on regression and classification tasks with state-of-the-art performance, which opens the gate of building all-in-one models in a single module style. Our model has been open sourced at https://github.com/Augus1999/bayesian-flow-network-for-chemistry.