标题： 百万规模的数据集和可泛化的基础模型用于纳米材料-蛋白质相互作用 显示英文标题

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

摘要： 解锁纳米材料在医学和环境科学中的潜力取决于理解它们与蛋白质的相互作用，这是一个复杂的决策空间，人工智能有望带来变革性的影响。 然而，进展受到了有限数据集和现有模型泛化能力受限的阻碍。 在此，我们提出了NanoPro-3M，迄今为止最大的纳米材料-蛋白质相互作用数据集，包含超过320万个样本和37,000种独特的蛋白质。 利用这一数据集，我们提出了NanoProFormer，一种通过多模态表示学习预测纳米材料-蛋白质亲和力的基础模型，表现出强大的泛化能力，能够处理缺失特征以及未见过的纳米材料或蛋白质。 我们证明了多模态建模显著优于单模态方法，并确定了冠层形成的决定因素。 此外，我们通过零样本推理和微调展示了其在一系列下游任务中的适用性。 总之，这项工作为高性能和通用的纳米材料-蛋白质相互作用终点预测奠定了坚实的基础，减少了实验依赖性，并加速了各种体外应用。 显示英文摘要

摘要： 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.