A million-scale dataset and generalizable foundation model for nanomaterial-protein interactions

• URL: http://arxiv.org/abs/2507.14245v1
• Date: Fri, 18 Jul 2025 00:00:52 GMT
• Title: A million-scale dataset and generalizable foundation model for nanomaterial-protein interactions
• Authors: Hengjie Yu, Kenneth A. Dawson, Haiyun Yang, Shuya Liu, Yan Yan, Yaochu Jin,
• Abstract summary: We propose NanoPro-3M, the largest nanomaterial-protein interaction dataset to date, comprising over 3.2 million samples and 37,000 unique proteins.<n>We present NanoProFormer, a foundational model that predicts nanomaterial-protein affinities through multimodal representation learning.
• Score: 22.339823160991934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unlocking the potential of nanomaterials in medicine and environmental science hinges on understanding their interactions with proteins, a complex decision space where AI is poised to make a transformative impact. However, progress has been hindered by limited datasets and the restricted generalizability of existing models. Here, we propose NanoPro-3M, the largest nanomaterial-protein interaction dataset to date, comprising over 3.2 million samples and 37,000 unique proteins. Leveraging this, we present NanoProFormer, a foundational model that predicts nanomaterial-protein affinities through multimodal representation learning, demonstrating strong generalization, handling missing features, and unseen nanomaterials or proteins. We show that multimodal modeling significantly outperforms single-modality approaches and identifies key determinants of corona formation. Furthermore, we demonstrate its applicability to a range of downstream tasks through zero-shot inference and fine-tuning. Together, this work establishes a solid foundation for high-performance and generalized prediction of nanomaterial-protein interaction endpoints, reducing experimental reliance and accelerating various in vitro applications.

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