Feedback-Based Quantum Control for Safe and Synergistic Drug Combination Design
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918305061666816 |
|---|---|
| author | Nhi, Mai Nguyen Phuong Tran, Lan Nguyen Ho, Le Bin |
| author_facet | Nhi, Mai Nguyen Phuong Tran, Lan Nguyen Ho, Le Bin |
| contents | Drug-drug interactions (DDIs) strongly affect the safety and efficacy of combination therapies. Despite the availability of large DDI databases, selecting optimal multi-drug combinations that balance safety, therapeutic benefit, and regimen size remains a challenging combinatorial optimization problem. Here, we present a quantum-control-based framework for DDI-aware drug combination optimization, in which known harmful and synergistic interactions are encoded into Ising Hamiltonians as penalties and rewards, respectively. The optimization is performed using the feedback-based quantum algorithm FALQON, a gradient-free variational approach. We study two clinically motivated tasks: the Maximum Safe Subset problem and the Synergy-Constrained Optimization problem. Numerical simulations using interaction data from Drugs.com and SYNERGxDB demonstrate efficient convergence and high-quality solutions for clinically relevant drug sets, including COVID-19 case studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18082 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Feedback-Based Quantum Control for Safe and Synergistic Drug Combination Design Nhi, Mai Nguyen Phuong Tran, Lan Nguyen Ho, Le Bin Quantum Physics Chemical Physics Computational Physics Medical Physics Drug-drug interactions (DDIs) strongly affect the safety and efficacy of combination therapies. Despite the availability of large DDI databases, selecting optimal multi-drug combinations that balance safety, therapeutic benefit, and regimen size remains a challenging combinatorial optimization problem. Here, we present a quantum-control-based framework for DDI-aware drug combination optimization, in which known harmful and synergistic interactions are encoded into Ising Hamiltonians as penalties and rewards, respectively. The optimization is performed using the feedback-based quantum algorithm FALQON, a gradient-free variational approach. We study two clinically motivated tasks: the Maximum Safe Subset problem and the Synergy-Constrained Optimization problem. Numerical simulations using interaction data from Drugs.com and SYNERGxDB demonstrate efficient convergence and high-quality solutions for clinically relevant drug sets, including COVID-19 case studies. |
| title | Feedback-Based Quantum Control for Safe and Synergistic Drug Combination Design |
| topic | Quantum Physics Chemical Physics Computational Physics Medical Physics |
| url | https://arxiv.org/abs/2601.18082 |