Small molecules play a pivotal role in modern medicine, and scrutinizing their interactions with protein targets is essential for the discovery and development of novel, life-saving therapeutics. The term "bioactivity" encompasses various biological effects resulting from these interactions, including both binding and functional responses. The magnitude of bioactivity dictates the therapeutic or toxic pharmacological outcomes of small molecules, rendering accurate bioactivity prediction crucial for the development of safe and effective drugs. However, existing structural datasets of small molecule-protein interactions are often limited in scale and lack systematically organized bioactivity labels, thereby impeding our understanding of these interactions and precise bioactivity prediction. In this study, we introduce a comprehensive dataset of small molecule-protein interactions, consisting of over a million binding structures, each annotated with real biological activity labels. This dataset is designed to facilitate unbiased bioactivity prediction. We evaluated several classical models on this dataset, and the results demonstrate that the task of unbiased bioactivity prediction is challenging yet essential.

这项研究介绍了一个包含超过一百万个结合结构及其真实生物活性标签的小分子-蛋白质相互作用全面数据集，用于推动无偏生物活性预测，并评估了几种经典模型在该数据集上的效果，结果表明无偏生物活性预测任务既具有挑战性又是必不可少的。

SIU: 百万级结构化小分子-蛋白质相互作用数据集用于无偏生物活性预测