{"title":"RinQ:在当前的量子计算机上预测蛋白质的中心位置","authors":"Shah Ishmam Mohtashim","doi":"10.1016/j.mtquan.2025.100053","DOIUrl":null,"url":null,"abstract":"<div><div>We introduce RinQ, a hybrid quantum–classical framework for identifying functionally critical residues in proteins by formulating centrality detection as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Protein structures are modeled as residue interaction networks (RINs), and the QUBO formulations are solved using D-Wave’s simulated annealing. Applied to a diverse set of proteins, RinQ consistently identifies central residues that closely align with classical benchmarks, demonstrating both the accuracy and robustness of the approach.</div></div>","PeriodicalId":100894,"journal":{"name":"Materials Today Quantum","volume":"7 ","pages":"Article 100053"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"RinQ: Towards predicting central sites in proteins on current quantum computers\",\"authors\":\"Shah Ishmam Mohtashim\",\"doi\":\"10.1016/j.mtquan.2025.100053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>We introduce RinQ, a hybrid quantum–classical framework for identifying functionally critical residues in proteins by formulating centrality detection as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Protein structures are modeled as residue interaction networks (RINs), and the QUBO formulations are solved using D-Wave’s simulated annealing. Applied to a diverse set of proteins, RinQ consistently identifies central residues that closely align with classical benchmarks, demonstrating both the accuracy and robustness of the approach.</div></div>\",\"PeriodicalId\":100894,\"journal\":{\"name\":\"Materials Today Quantum\",\"volume\":\"7 \",\"pages\":\"Article 100053\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials Today Quantum\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2950257825000319\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Today Quantum","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2950257825000319","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
RinQ: Towards predicting central sites in proteins on current quantum computers
We introduce RinQ, a hybrid quantum–classical framework for identifying functionally critical residues in proteins by formulating centrality detection as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Protein structures are modeled as residue interaction networks (RINs), and the QUBO formulations are solved using D-Wave’s simulated annealing. Applied to a diverse set of proteins, RinQ consistently identifies central residues that closely align with classical benchmarks, demonstrating both the accuracy and robustness of the approach.