{"id":12699,"date":"2025-03-12T10:57:49","date_gmt":"2025-03-12T09:57:49","guid":{"rendered":"https:\/\/www.uni.lu\/research-en\/?post_type=research-projects&p=12699"},"modified":"2025-06-17T09:59:38","modified_gmt":"2025-06-17T07:59:38","slug":"aqma-approaching-quantum-mechanical-accuracy-for-drug-protein-binding-with-machine-learning","status":"publish","type":"research-projects","link":"https:\/\/www.uni.lu\/research-en\/research-projects\/aqma-approaching-quantum-mechanical-accuracy-for-drug-protein-binding-with-machine-learning\/","title":{"rendered":"Approaching Quantum Mechanical Accuracy for Drug-Protein Binding with Machine Learning (AQMA)"},"content":{"rendered":"\n
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\nApproaching Quantum Mechanical Accuracy for Drug-Protein Binding with Machine Learning<\/strong><\/strong><\/strong><\/strong><\/strong><\/strong> (AQMA<\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>)<\/h1>\n<\/div>\n<\/header>\n
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<\/p>\n\n\n

    \n\n<\/ul>\n<\/div>\n<\/div>\n <\/div>\n<\/section>\n\n
    <\/div>\n\n\n

    \nThe project at a glance<\/h2>\n\n\n
    \n
      \n
    • \n \n
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      \n <\/use><\/svg><\/div>\n\n\n

      Start date:
      01 September 2021<\/strong><\/p>\n<\/div>\n<\/li>\n

    • \n \n
      \n
      \n <\/use><\/svg><\/div>\n\n\n

      Duration in months:
      48<\/strong>
      <\/p>\n<\/div>\n<\/li>\n

    • \n \n
      \n
      \n <\/use><\/svg><\/div>\n\n\n

      Funding:
      IAS Luxembourg<\/strong><\/p>\n<\/div>\n<\/li>\n

    • \n \n
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      \n <\/use><\/svg><\/div>\n\n\n

      Principal Investigator(s):
      Mirela PULEVA<\/strong><\/p>\n<\/div>\n<\/li>\n <\/ul>\n<\/div>\n\n\n\n

      \nAbout<\/h2>\n\n\n\n

      Computational modelling of drug-protein binding is of vital importance in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel candidates for biological targets, genetic studies, and gene technology. The challenge in these investigations is two-fold: (1) even slight inaccuracies of 1 kcal\/mol in the prediction of energetics in biological systems can lead to erroneous conclusions, (2) the large size of drug-protein systems means that the use of highly accurate quantum-mechanical (QM) methods is not viable. Hence, it is a grand challenge to develop efficient biomolecular force fields that can achieve and exceed stringent accuracy requirements while being efficient enough for modeling drug-protein binding in atomistic detail. The recent combination of machine learning (ML) to accelerate QM calculations has already led to breakthroughs in our ability to obtain dynamical insights into small molecules. However, large realistic molecules remain out of reach of QM\/ML approaches. In this context, the present PhD project will combine quantum mechanics, machine learning, and the theory of intermolecular interactions to develop QM\/ML approaches applicable to large biomolecular systems, focusing on drug-protein binding. The main goal will be to develop a hybrid QM\/ML model for capturing ubiquitous non-covalent interactions that largely determine biomolecular processes and functions. The developed model will then be combined with existing efficient ML force fields for local chemical bonding and applied to study folding of small proteins and the interactions between drugs and their protein targets in free-energy simulations. An appropriate benchmark dataset of representative organic molecules to examine our model performance will also be generated. The aim is to reach an applicability range in system size of, e.g., the medically significant DJ-1 protein, for which our results will be compared to further calculations and experimental data from the Cell Signaling Group at the LCSB\/Uni.lu.<\/p>\n\n\n

      <\/div>\n\n\n

      \nOrganisation and Partners<\/h2>\n\n\n\n

      Faculty of Science, Technology and Medicine (FSTM)
      Institute for Advanced Studies (IAS)<\/strong><\/p>\n\n\n

      \n \n

      \nProject team<\/h2>\n
        \n
      • \n
        \n
        \n
        \n \n\"Dr.\n <\/figure>\n <\/div>\n
        \n

        Dr. Mirela PULEVA<\/h3>\n

        Postdoctoral researcher<\/p>\n

        \n \n \n Learn more<\/span>\n <\/span>\n <\/use><\/svg> <\/a>\n<\/div>\n <\/div>\n <\/div>\n<\/div>\n<\/div><\/li>
      • \n
        \n
        \n
        \n \n\"Prof\n <\/figure>\n <\/div>\n
        \n

        Prof Alexandre TKATCHENKO<\/h3>\n

        Full professor in Theoretical Condensed Matter Physics<\/p>\n

        \n \n \n Learn more<\/span>\n <\/span>\n <\/use><\/svg> <\/a>\n<\/div>\n <\/div>\n <\/div>\n<\/div>\n<\/div><\/li>
      • \n
        \n
        \n
        \n \n\"Assoc.\n <\/figure>\n <\/div>\n
        \n

        Assoc. Prof Alexander SKUPIN<\/h3>\n

        Deputy Director of LCSB, Associate professor\/Chief scientist 2 in Modelling of Biomedical Data<\/p>\n

        \n \n \n Learn more<\/span>\n <\/span>\n <\/use><\/svg> <\/a>\n<\/div>\n <\/div>\n <\/div>\n<\/div>\n<\/div><\/li><\/ul>\n\n<\/div>\n\n<\/div><\/section>\n\n\n
        \n
        \n \n

        \nMore about Quantum<\/h2>\n\n
          \n
        • \n \n \n \n Quantum research at Uni.lu <\/span>\n <\/use><\/svg> <\/span>\n <\/a>\n<\/li>\n<\/ul>\n <\/div>\n<\/section>","protected":false},"excerpt":{"rendered":"

          Computational modelling of drug-protein binding is of vital importance in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel candidates for biological targets, genetic studies, and gene technology. The challenge in these investigations is two-fold: (1) even slight inaccuracies of 1 kcal\/mol in the prediction of energetics in biological systems can lead to erroneous conclusions, (2) the large size of drug-protein systems means that the use of highly accurate quantum-mechanical (QM) methods is not viable. Hence, it is a grand challenge to develop efficient biomolecular force fields that can achieve and exceed stringent accuracy requirements while being efficient enough for modeling drug-protein binding in atomistic detail. The recent combination of machine learning (ML) to accelerate QM calculations has already led to breakthroughs in our ability to obtain dynamical insights into small molecules. However, large realistic molecules remain out of reach of QM\/ML approaches. In this context, the present PhD project will combine quantum mechanics, machine learning, and the theory of intermolecular interactions to develop QM\/ML approaches applicable to large biomolecular systems, focusing on drug-protein binding. The main goal will be to develop a hybrid QM\/ML model for capturing ubiquitous non-covalent interactions that largely determine biomolecular processes and functions. The developed model will then be combined with existing efficient ML force fields for local chemical bonding and applied to study folding of small proteins and the interactions between drugs and their protein targets in free-energy simulations. An appropriate benchmark dataset of representative organic molecules to examine our model performance will also be generated. The aim is to reach an applicability range in system size of, e.g., the medically significant DJ-1 protein, for which our results will be compared to further calculations and experimental data from the Cell Signaling Group at the LCSB\/Uni.lu.<\/p>\n","protected":false},"author":203,"featured_media":12854,"parent":0,"menu_order":0,"template":"","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"AQMA","uluxPrePostTitle":"Research project","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"rpp_rp_identifier":"13129","rpp_taxonomies_in_sync":true},"research-project-status":[],"research-project-type":[],"field-of-interest":[],"organisation":[],"authorship":[203,329],"acf":[],"yoast_head":"\nApproaching Quantum Mechanical Accuracy for Drug-Protein Binding with Machine Learning (AQMA) I Uni.lu<\/title>\n<meta name=\"description\" content=\"Computational modelling of drug-protein binding is of vital importance in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel candidates for biological targets, genetic studies, and gene technology. 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In this context, the present PhD project will combine quantum mechanics, machine learning, and the theory of intermolecular interactions to develop QM\/ML approaches applicable to large biomolecular systems, focusing on drug-protein binding. The main goal will be to develop a hybrid QM\/ML model for capturing ubiquitous non-covalent interactions that largely determine biomolecular processes and functions. The developed model will then be combined with existing efficient ML force fields for local chemical bonding and applied to study folding of small proteins and the interactions between drugs and their protein targets in free-energy simulations. An appropriate benchmark dataset of representative organic molecules to examine our model performance will also be generated. 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In this context, the present PhD project will combine quantum mechanics, machine learning, and the theory of intermolecular interactions to develop QM\/ML approaches applicable to large biomolecular systems, focusing on drug-protein binding. The main goal will be to develop a hybrid QM\/ML model for capturing ubiquitous non-covalent interactions that largely determine biomolecular processes and functions. The developed model will then be combined with existing efficient ML force fields for local chemical bonding and applied to study folding of small proteins and the interactions between drugs and their protein targets in free-energy simulations. An appropriate benchmark dataset of representative organic molecules to examine our model performance will also be generated. 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The challenge in these investigations is two-fold: (1) even slight inaccuracies of 1 kcal\/mol in the prediction of energetics in biological systems can lead to erroneous conclusions, (2) the large size of drug-protein systems means that the use of highly accurate quantum-mechanical (QM) methods is not viable. Hence, it is a grand challenge to develop efficient biomolecular force fields that can achieve and exceed stringent accuracy requirements while being efficient enough for modeling drug-protein binding in atomistic detail. The recent combination of machine learning (ML) to accelerate QM calculations has already led to breakthroughs in our ability to obtain dynamical insights into small molecules. However, large realistic molecules remain out of reach of QM\/ML approaches. In this context, the present PhD project will combine quantum mechanics, machine learning, and the theory of intermolecular interactions to develop QM\/ML approaches applicable to large biomolecular systems, focusing on drug-protein binding. The main goal will be to develop a hybrid QM\/ML model for capturing ubiquitous non-covalent interactions that largely determine biomolecular processes and functions. The developed model will then be combined with existing efficient ML force fields for local chemical bonding and applied to study folding of small proteins and the interactions between drugs and their protein targets in free-energy simulations. An appropriate benchmark dataset of representative organic molecules to examine our model performance will also be generated. The aim is to reach an applicability range in system size of, e.g., the medically significant DJ-1 protein, for which our results will be compared to further calculations and experimental data from the Cell Signaling Group at the LCSB\/Uni.lu.","breadcrumb":{"@id":"https:\/\/www.uni.lu\/research-en\/research-projects\/aqma-approaching-quantum-mechanical-accuracy-for-drug-protein-binding-with-machine-learning\/#breadcrumb"},"inLanguage":"en-GB","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.uni.lu\/research-en\/research-projects\/aqma-approaching-quantum-mechanical-accuracy-for-drug-protein-binding-with-machine-learning\/"]}]},{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/www.uni.lu\/research-en\/research-projects\/aqma-approaching-quantum-mechanical-accuracy-for-drug-protein-binding-with-machine-learning\/#primaryimage","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/8\/2025\/03\/kobu-agency-67L18R4tW_w-unsplash-scaled.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/8\/2025\/03\/kobu-agency-67L18R4tW_w-unsplash-scaled.jpg","width":2560,"height":1716,"caption":"Photo by KOBU Agency on Unsplash"},{"@type":"BreadcrumbList","@id":"https:\/\/www.uni.lu\/research-en\/research-projects\/aqma-approaching-quantum-mechanical-accuracy-for-drug-protein-binding-with-machine-learning\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.uni.lu\/en"},{"@type":"ListItem","position":2,"name":"Research","item":"https:\/\/www.uni.lu\/research-en\/"},{"@type":"ListItem","position":3,"name":"Research Projects Pages","item":"https:\/\/www.uni.lu\/research-en\/research-projects\/"},{"@type":"ListItem","position":4,"name":"Approaching Quantum Mechanical Accuracy for Drug-Protein Binding with Machine Learning (AQMA)"}]},{"@type":"WebSite","@id":"https:\/\/www.uni.lu\/research-en\/#website","url":"https:\/\/www.uni.lu\/research-en\/","name":"Uni.lu","description":"Research at the University of Luxembourg","publisher":{"@id":"https:\/\/www.uni.lu\/research-en\/#organization"},"alternateName":"University of Luxembourg","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.uni.lu\/research-en\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-GB"},{"@type":"Organization","@id":"https:\/\/www.uni.lu\/research-en\/#organization","name":"University of Luxembourg","alternateName":"Uni.lu","url":"https:\/\/www.uni.lu\/research-en\/","logo":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/www.uni.lu\/research-en\/#\/schema\/logo\/image\/","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/8\/2026\/03\/03115550\/UNIV_SM-Profile_1600x1600px-scaled.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/8\/2026\/03\/03115550\/UNIV_SM-Profile_1600x1600px-scaled.jpg","width":2560,"height":2560,"caption":"University of Luxembourg"},"image":{"@id":"https:\/\/www.uni.lu\/research-en\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/research-projects\/12699"}],"collection":[{"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/research-projects"}],"about":[{"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/types\/research-projects"}],"author":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/users\/203"}],"version-history":[{"count":5,"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/research-projects\/12699\/revisions"}],"predecessor-version":[{"id":13749,"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/research-projects\/12699\/revisions\/13749"}],"wp:authorship":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/users\/203"},{"embeddable":true,"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/users\/329"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/media\/12854"}],"wp:attachment":[{"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/media?parent=12699"}],"wp:term":[{"taxonomy":"research-project-status","embeddable":true,"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/research-project-status?post=12699"},{"taxonomy":"research-project-type","embeddable":true,"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/research-project-type?post=12699"},{"taxonomy":"field-of-interest","embeddable":true,"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/field-of-interest?post=12699"},{"taxonomy":"organisation","embeddable":true,"href":"https:\/\/www.uni.lu\/research-en\/wp-json\/wp\/v2\/organisation?post=12699"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}