SIU: A Million-Scale Structural Small Molecule-Protein Interaction Dataset for Unbiased Bioactivity Prediction

TL;DR

This paper introduces SIU, a large-scale dataset of over a million small molecule-protein interactions with bioactivity labels, aiming to improve unbiased bioactivity prediction for drug discovery.

Contribution

The creation of a comprehensive, systematically annotated dataset of small molecule-protein interactions at a million scale for unbiased bioactivity prediction.

Findings

Classical models show challenges in unbiased bioactivity prediction.

The dataset enables more accurate and systematic bioactivity analysis.

Unbiased prediction remains a challenging task.

Abstract

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.