A Scalable Heuristic for Molecular Docking on Neutral-Atom Quantum Processors

TL;DR

This paper presents a divide-and-conquer heuristic that enables molecular docking problems to be tackled on neutral-atom quantum processors by decomposing large graphs into manageable sub-problems, improving scalability and solution quality.

Contribution

The authors introduce a scalable quantum heuristic for molecular docking that overcomes size limitations of current quantum devices using a graph decomposition approach.

Findings

The quantum heuristic outperforms a greedy baseline on benchmark instances.

It achieves the optimal solution on a 540-node graph instance.

The approach is validated on real-world protein-ligand complexes.

Abstract

Molecular docking is a critical computational method in drug discovery used to predict the binding conformation and orientation of a ligand within a protein's binding site. Mapping this challenge onto a graph-based problem, specifically the Maximum Weighted Independent Set (MWIS) problem, allows it to be addressed by specialized hardware such as neutral-atom quantum processors. However, a significant bottleneck has been the size mismatch between biologically relevant molecular systems and the limited capacity of near-term quantum devices. In this work, we overcome this scaling limitation by the use of a divide-and-conquer heuristic introduced in Cazals 2025. This algorithm decomposes a single, intractable graph instance into smaller sub-problems that can be solved sequentially on a neutral-atom quantum emulator, incurring only a linear computational overhead. We benchmark this approach on 10 real-world protein-ligand complexes, including 9 from the Astex Diverse Set, with graphs ranging from 225 to 585 vertices. The quantum heuristic consistently outperforms a greedy baseline and achieves the provably optimal solution on a 540-node instance (TACE-AS). We further assess the biological relevance of the reconstructed poses via the fraction of native contacts, and benchmark the full workflow on a standard dataset of diverse protein-ligand complexes. Our work establishes a scalable blueprint for applying quantum optimization to molecular docking, while identifying concrete directions for improving both the algorithmic strategy and the underlying graph model.