# RapidDock: Unlocking Proteome-scale Molecular Docking

arXiv: https://arxiv.org/abs/2411.00004 · HTML: https://mlsanity.com/paper/2411.00004/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://mlsanity.com/paper/2210.01776/) (2022)
- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (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)
- [Deep Confident Steps to New Pockets: Strategies for Docking Generalization](https://mlsanity.com/paper/2402.18396/) (2024)
- [FABind: Fast and Accurate Protein-Ligand Binding](https://mlsanity.com/paper/2310.06763/) (2023)
- [Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?](https://mlsanity.com/paper/2302.07061/) (2023)
- [FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation](https://mlsanity.com/paper/2403.20261/) (2024)

## Abstract (arXiv)

Accelerating molecular docking -- the process of predicting how molecules bind to protein targets -- could boost small-molecule drug discovery and revolutionize medicine. Unfortunately, current molecular docking tools are too slow to screen potential drugs against all relevant proteins, which often results in missed drug candidates or unexpected side effects occurring in clinical trials. To address this gap, we introduce RapidDock, an efficient transformer-based model for blind molecular docking. RapidDock achieves at least a 100 × speed advantage over existing methods without compromising accuracy. On the Posebusters and DockGen benchmarks, our method achieves 52.1% and 44.0% success rates (RMSD<2rA), respectively. The average inference time is 0.04 seconds on a single GPU, highlighting RapidDock's potential for large-scale docking studies. We examine the key features of RapidDock that enable leveraging the transformer architecture for molecular docking, including the use of relative distance embeddings of 3D structures in attention matrices, pre-training on protein folding, and a custom loss function invariant to molecular symmetries.