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

Kairi Furui, Masahito Ohue

Keywords: Virtual screening, Structure-based drug design, Molecular docking, Binding prediction

TL;DR: Boltzina achieves efficient virtual screening by combining Boltz-2's machine learning accuracy with AutoDock Vina's speed, delivering 7.3× speedup over Boltz-2 while maintaining superior performance over conventional methods.

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.

Submission Track: Paper Track (Full Paper)

Submission Category: AI-Guided Design

Institution Location: {Kanagawa, Japan}

Submission Number: 21