  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.
