A Scalable Heuristic for Molecular Docking on Neutral-Atom Quantum Processors
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917431947034624 |
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| author | Garrigues, Mathieu Onofre, Victor Coelho, Wesley Acheche, S. |
| author_facet | Garrigues, Mathieu Onofre, Victor Coelho, Wesley Acheche, S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Scalable Heuristic for Molecular Docking on Neutral-Atom Quantum Processors Garrigues, Mathieu Onofre, Victor Coelho, Wesley Acheche, S. Quantum Physics Chemical Physics 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. |
| title | A Scalable Heuristic for Molecular Docking on Neutral-Atom Quantum Processors |
| topic | Quantum Physics Chemical Physics |
| url | https://arxiv.org/abs/2508.18147 |