# End-to-End Reverse Screening Identifies Protein Targets of Small Molecules Using HelixFold3

arXiv: https://arxiv.org/abs/2601.13693 · HTML: https://mlsanity.com/paper/2601.13693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://mlsanity.com/paper/2210.01776/) (2022)
- [State-specific protein-ligand complex structure prediction with a multi-scale deep generative model](https://mlsanity.com/paper/2209.15171/) (2022)
- [E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking](https://mlsanity.com/paper/2210.06069/) (2022)
- [Technical Report of HelixFold3 for Biomolecular Structure Prediction](https://mlsanity.com/paper/2408.16975/) (2024)
- [One-step Structure Prediction and Screening for Protein-Ligand Complexes using Multi-Task Geometric Deep Learning](https://mlsanity.com/paper/2408.11356/) (2024)
- [COMET:Combined Matrix for Elucidating Targets](https://mlsanity.com/paper/2412.02471/) (2024)

## Abstract (arXiv)

Identifying protein targets for small molecules, or reverse screening, is essential for understanding drug action, guiding compound repurposing, predicting off-target effects, and elucidating the molecular mechanisms of bioactive compounds. Despite its critical role, reverse screening remains challenging because accurately capturing interactions between a small molecule and structurally diverse proteins is inherently complex, and conventional step-wise workflows often propagate errors across decoupled steps such as target structure modeling, pocket identification, docking, and scoring. Here, we present an end-to-end reverse screening strategy leveraging HelixFold3, a high-accuracy biomolecular structure prediction model akin to AlphaFold3, which simultaneously models the folding of proteins from a protein library and the docking of small-molecule ligands within a unified framework. We validate this approach on a diverse and representative set of approximately one hundred small molecules. Compared with conventional reverse docking, our method improves screening accuracy and demonstrates enhanced structural fidelity, binding-site precision, and target prioritization. By systematically linking small molecules to their protein targets, this framework establishes a scalable and straightforward platform for dissecting molecular mechanisms, exploring off-target interactions, and supporting rational drug discovery.