Bayesian Flow Is All You Need to Sample Out-of-Distribution Chemical Spaces

• URL: http://arxiv.org/abs/2412.11439v4
• Date: Sat, 15 Feb 2025 01:29:33 GMT
• Title: Bayesian Flow Is All You Need to Sample Out-of-Distribution Chemical Spaces
• Authors: Nianze Tao,
• Abstract summary: We show that Bayesian flow network is capable of effortlessly generating high quality out-of-distribution samples. We introduce a semi-autoregressive training/sampling method that helps to enhance the model performance and surpass the state-of-the-art models.
• Score: 0.0
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• 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 is capable of effortlessly generating high quality out-of-distribution samples that meet several scenarios. We introduce a semi-autoregressive training/sampling method that helps to enhance the model performance and surpass the state-of-the-art models.

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