Using Recursive Feature Machines with a custom hybrid fingerprint yields lower solubility prediction errors than graph neural networks on ESOL and FreeSolv, while also producing feature-importance scores.
A Bayesian Flow Network Framework for Chemistry Tasks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In this work, we introduce ChemBFN, a language model that handles chemistry tasks based on Bayesian flow networks working on discrete data. A new accuracy schedule is proposed to improve the sampling quality by significantly reducing the 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 the 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.
fields
q-bio.BM 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints
Using Recursive Feature Machines with a custom hybrid fingerprint yields lower solubility prediction errors than graph neural networks on ESOL and FreeSolv, while also producing feature-importance scores.