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

TL;DR

Boltzina is a new framework that combines the high accuracy of Boltz-2 with improved computational efficiency for large-scale virtual screening by directly predicting binding affinity from docking poses, enabling practical application.

Contribution

It introduces Boltzina, a novel method that omits slow structure prediction steps, achieving faster screening while maintaining high accuracy, suitable for large compound libraries.

Findings

Boltzina outperforms AutoDock Vina and GNINA in screening performance.

It is up to 11.8 times faster through optimized processing.

The method effectively balances accuracy and efficiency for practical drug discovery.

Abstract

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$\times$ 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.