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

arXiv: https://arxiv.org/abs/2406.08961 · HTML: https://mlsanity.com/paper/2406.08961/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 67 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [ATOM3D: Tasks On Molecules in Three Dimensions](https://mlsanity.com/paper/2012.04035/) (2020)
- [ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment](https://mlsanity.com/paper/2310.07229/) (2023)

## Built on by (most cited first, 2 of 2)

- [MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models](https://mlsanity.com/paper/2603.18256/) (2026)
- [Structure-guided molecular design with contrastive 3D protein-ligand learning](https://mlsanity.com/paper/2604.19562/) (2026)

## Abstract (arXiv)

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.