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[2412.11439] Bayesian Flow Is All You Need to Sample Out-of-Distribution Chemical Spaces
Summary
The paper presents a Bayesian flow network, specifically the ChemBFN model, which effectively generates out-of-distribution chemical samples for drug design, surpassing existing methods.
Why It Matters
This research addresses a significant challenge in drug design by enabling the generation of novel molecules beyond the training data distribution. This capability can accelerate the discovery of new drugs and improve the efficiency of the development process, making it highly relevant for researchers in machine learning and chemistry.
Key Takeaways
- The ChemBFN model can generate high-quality out-of-distribution samples.
- Incorporating a reinforcement learning strategy enhances the model's performance.
- A semi-autoregressive approach is introduced to improve training and inference.
- The paper provides a theoretical analysis of the model's capabilities.
- This research could significantly impact de novo drug design processes.
Computer Science > Machine Learning arXiv:2412.11439 (cs) [Submitted on 16 Dec 2024 (v1), last revised 16 Feb 2026 (this version, v5)] Title:Bayesian Flow Is All You Need to Sample Out-of-Distribution Chemical Spaces Authors:Nianze Tao, Minori Abe View a PDF of the paper titled Bayesian Flow Is All You Need to Sample Out-of-Distribution Chemical Spaces, by Nianze Tao and Minori Abe View PDF HTML (experimental) Abstract:Generating novel molecules with higher properties than the training space, namely the out-of-distribution generation, is important for de novo drug design. However, it is not easy for distribution learning-based models, for example diffusion models, to solve this challenge as these methods are designed to fit the distribution of training data as close as possible. In this paper, we show that Bayesian flow network, especially ChemBFN model, is capable of intrinsically generating high quality out-of-distribution samples that meet several scenarios. A reinforcement learning strategy is added to the ChemBFN and a controllable ordinary differential equation solver-like generating process is employed that accelerate the sampling processes. Most importantly, we introduce a semi-autoregressive strategy during training and inference that enhances the model performance and surpass the state-of-the-art models. A theoretical analysis of out-of-distribution generation in ChemBFN with semi-autoregressive approach is included as well. Comments: Subjects: Machine Learning (...
