[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85129-en":3,"doc-seo-85129-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85129,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Q-Score: A Quantum-Native Scoring Function for Molecular Docking","Molecular docking predicts how a small molecule binds to a protein and remains a major bottleneck in drug discovery. Classical scoring functions rely on empirical pairwise contact sums and miss quantum-mechanical effects such as orbital charge transfer that shape binding specificity. Q-Score replaces additive atom-pair scoring by predicting donor–acceptor orbital interaction energies with a graph neural network, encoding them in a weighted graph. Binding quality is obtained by solving a maximum-weight vertex clique problem using Digitized-Counterdiabatic QAOA.","Q-Score: A Quantum-Native Scoring Function for  \nMolecular Docking  \nKangyu Zheng  \nDepartment of Computer Science and Engineering The Chinese University of Hong Kong Shatin, N.T., Hong Kong SAR [kyzheng@cse.cuhk.edu.hk](kyzheng@cse.cuhk.edu.hk)  \nYidong Zhou  \nDepartment of Electrical and Computer Engineering Rutgers University Piscataway, NJ [yidong.zhou@rutgers.edu](yidong.zhou@rutgers.edu)  \nRui-Hao Li  \nComputational Life Sciences Cleveland Clinic  \nCleveland, OH[LIR9@ccf.org](LIR9@ccf.org)  \nZixin Ding  \nDepartment of Computer Science The University of Chicago Chicago, IL [zixin@uchicago.edu](zixin@uchicago.edu)  \nZhiding Liang  \nDepartment of Computer Science and Engineering The Chinese University of Hong Kong Shatin, N.T., Hong Kong SAR [zliang@cse.cuhk.edu.hk](zliang@cse.cuhk.edu.hk)  \narXiv :2607 .09737v1 [physics .chem-ph] 2 Jul 2026  \nShaohua Li  \nDepartment of Computer Science and Engineering  \nThe Chinese University of Hong Kong  \nShatin, N.T., Hong Kong SAR [shaohuali@cuhk.edu.hk](shaohuali@cuhk.edu.hk)  \nAbstract—Molecular docking predicts how a small molecule binds to a protein and is a key bottleneck in drug discovery. Classical scoring functions sum empirical pairwise contacts, blind to quantum-mechanical effects like orbital charge transfer that govern binding specificity. We introduce Q-Score, encoding GNNpredicted orbital donor-acceptor energies into a weighted graph and scoring binding by solving a maximum-weight vertex clique problem via Digitized-Counterdiabatic QAOA. Each interaction anchor maps to one qubit and compatibility constraints become edges. Across 11 protein targets, DC-QAOA recovers the exact optimum on 8 at 10 qubits. On 1000 AI-generated molecules, Q-Score is orthogonal to classical scoring with Spearman rhoof 0.05, driven by orbital quality with rho of 0.90, and free of molecular-weight bias, enriching for strong orbital interactions at twice the random rate. DC-QAOA achieves a mean approximation ratio of 0.94 with 52 percent exact. Execution of 1000 circuitson IBM Eagle confirms 6-qubit solvability on NISQ hardware. Index Terms—quantum computing, artificial intelligence  \nI. INTRODUCTION  \nMolecular docking is one of the most computationally intensive steps in drug discovery, requiring the evaluation of thousands to millions of candidate molecules against a protein target. The task has two components: a search algorithm that proposes candidate placements of a molecule inside the protein binding pocket, and a scoring function that ranks those placements by estimated binding quality. Because scoring is invoked at every step of the search, its cost dominates the overall workflow, and all widely deployed docking tools [1],[2] use fast empirical scoring functions that sum pairwise atomic interaction terms such as van der Waals contacts, hydrogen bonds, and desolvation penalties [3] .  \nThis additive formulation enables high throughput but introduces a systematic limitation: because the score is a sum over atom pairs, larger molecules accumulate more favorable terms simply by having more atoms, regardless of the quality of their individual interactions [4] . The scoring function is also unable to capture quantum-mechanical effects, such as orbital-level charge transfer between donor and acceptor groups, π-stacking stabilization, and hyperconjugation, that often govern binding specificity [5] . These stereoelectronic effects determine why a molecule binds to one pocket and not another, yet they have no direct representation in the empirical scoring vocabulary.  \nRecent work has shown that molecular docking can be reformulated as a combinatorial optimization problem by representing candidate protein-ligand interaction contacts as nodes in a weighted graph and encoding geometric compatibility as edges [6], [7] . Selecting the best subset of mutually compatible contacts then becomes a maximum-weight vertex clique problem (MWVCP), which maps naturally onto an Ising Hamiltonian and can be solved u","cbCaijgc310JCGHT","https://ap.wps.com/l/cbCaijgc310JCGHT","pdf",1033747,2,1,13,"English","en",105,"# Abstract\n# Introduction\n## Limitations of classical scoring\n## Reformulating docking as combinatorial optimization\n## Q-Score approach and quantum solver","[{\"question\":\"What problem does Q-Score address in molecular docking?\",\"answer\":\"Classical scoring sums empirical pairwise contacts and cannot represent quantum-level orbital effects like charge transfer, which can govern binding specificity. Q-Score introduces an orbital-information-based scoring mechanism to capture these effects.\"},{\"question\":\"How does Q-Score encode chemical interactions for quantum optimization?\",\"answer\":\"Q-Score builds an Orbital Interaction Graph from GNN-predicted donor–acceptor orbital energies. Each interaction anchor maps to a qubit and compatibility constraints become edges in a maximum-weight vertex clique formulation.\"},{\"question\":\"What do the experiments show about Q-Score compared with classical scoring?\",\"answer\":\"Across 11 protein targets, DC-QAOA recovers the exact optimum on 8 at 10 qubits, with selected cliques identifying key binding contacts. On 1000 AI-generated molecules, Q-Score is orthogonal to classical scoring (Spearman rho ≈ 0.05) and shows strong orbital selectivity without molecular-weight bias; IBM Eagle execution supports solvability on NISQ hardware.\"}]",1784201272,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"q-score-a-quantum-native-scoring-function-for-molecular-docking","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/q-score-a-quantum-native-scoring-function-for-molecular-docking/85129/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does Q-Score address in molecular docking?","Question",{"text":75,"@type":76},"Classical scoring sums empirical pairwise contacts and cannot represent quantum-level orbital effects like charge transfer, which can govern binding specificity. Q-Score introduces an orbital-information-based scoring mechanism to capture these effects.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Q-Score encode chemical interactions for quantum optimization?",{"text":80,"@type":76},"Q-Score builds an Orbital Interaction Graph from GNN-predicted donor–acceptor orbital energies. Each interaction anchor maps to a qubit and compatibility constraints become edges in a maximum-weight vertex clique formulation.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the experiments show about Q-Score compared with classical scoring?",{"text":84,"@type":76},"Across 11 protein targets, DC-QAOA recovers the exact optimum on 8 at 10 qubits, with selected cliques identifying key binding contacts. On 1000 AI-generated molecules, Q-Score is orthogonal to classical scoring (Spearman rho ≈ 0.05) and shows strong orbital selectivity without molecular-weight bias; IBM Eagle execution supports solvability on NISQ hardware.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]