# Boltzina: Efficient and Accurate Virtual Screening via Docking-Guided Binding Prediction with Boltz-2

arXiv: https://arxiv.org/abs/2508.17555 · HTML: https://mlsanity.com/paper/2508.17555/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 32 references that are in the map.
- First posted on arXiv 24 Aug 2025.

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

- [TerraBind: Fast and Accurate Binding Affinity Prediction through Coarse Structural Representations](https://mlsanity.com/paper/2602.07735/) (2026)
- [Adapting Boltz-2 with limited experimental activity data improves early enrichment in virtual screening](https://mlsanity.com/paper/2609.24302/) (2026)

## Abstract (arXiv)

In structure-based drug discovery, virtual screening using conventional molecular docking methods can be performed rapidly but suffers from limitations in prediction accuracy. Recently, Boltz-2 was proposed, achieving extremely high accuracy in binding affinity prediction, but requiring approximately 20 seconds per compound per GPU, making it difficult to apply to large-scale screening of hundreds of thousands to millions of compounds. This study proposes Boltzina, a novel framework that leverages Boltz-2's high accuracy while significantly improving computational efficiency. Boltzina achieves both accuracy and speed by omitting the rate-limiting structure prediction from Boltz-2's architecture and directly predicting affinity from AutoDock Vina docking poses. We evaluate on eight assays from the MF-PCBA dataset and show that while Boltzina performs below Boltz-2, it provides significantly higher screening performance compared to AutoDock Vina and GNINA. Additionally, Boltzina achieved up to 11.8× faster through reduced recycling iterations and batch processing. Furthermore, we investigated multi-pose selection strategies and two-stage screening combining Boltzina and Boltz-2, presenting optimization methods for accuracy and efficiency according to application requirements. This study represents the first attempt to apply Boltz-2's high-accuracy predictions to practical-scale screening, offering a pipeline that combines both accuracy and efficiency in computational biology. The Boltzina is available on github; https://github.com/ohuelab/boltzina.