[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122259-en":3,"doc-seo-122259-105":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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122259,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Synergies between Quantum Mechanics and Machine Learning for Advancing Pharmaceutical Research - Dissertation Defense","The drug development process is resource-intensive, often costing billions and taking over a decade, yet many candidates still fail in late-stage trials. This thesis targets early-stage bottlenecks in drug discovery—chemical-space navigation, molecular-interaction modeling, and prediction of biological properties—by integrating quantum chemistry with machine learning. It reports analysis of the Aquamarine dataset to capture conformations, solvation, and non-covalent effects, and finds that many-body dispersion and implicit solvation materially shape molecular geometries, improving reliability for biological environments.","PhD-FSTM-2025-038  \nFaculty of Science, Technology and Medicine  \nDISSERTATION  \nDefence held on 24 March 2025 in Luxembourg  \nto obtain the degree of  \nDOCTEUR DE L UNIVERSITE DU LUXEMBOURG  \nEN PHYSIQUE  \nby  \nAlessio Fallani  \nBorn on 2 August 1995 in Florence (Italy)  \nSynergies between Quantum Mechanics and Machine Learning for Advancing Pharmaceutical Research  \nDissertation Defense Committee:  \nDr. Aurélia Chenu, Committee President Professor, Université du Luxembourg  \nDr. Alexandre Tkatchenko, Supervisor Professor, Université du Luxembourg  \nDr. Kostyantin Chernichenko, Co-supervisor Senior Data Scientist, Janssen Pharma NV  \nDr. Massimiliano Esposito  \nProfessor, Université du Luxembourg  \nDr. Pavlo O. Dral  \nProfessor, Xiamen University  \nAffidavit  \nI hereby confirm that the PhD thesis entitled “Synergies between Quantum Mechanics and Machine Learning for Advancing Pharmaceutical Research” has been written independently and without any other sources than cited.  \nLuxembourg,      \nName  \nAbstract  \nThe drug development process is resource-intensive, often costing billions and taking over a decade, yet many candidates still fail in late-stage trials. This thesis addresses key bottlenecks in early-stage drug discovery—such as navigating chemical space, modeling molecular interactions, and predicting biological properties—by integrating quantum chemistry and machine learning to develop more accurate and scalable computational methodologies. The analysis of the Aquamarine (AQM) dataset, designed to capture the interplay between molecular conformations, solvation effects, and non-covalent interactions, is presented as a key milestone for future machine learning models dealing with solvation effects for relevant molecules in medicinal chemistry. The results of the analysis reveal that many-body dispersion effects and implicit solvation significantly influence molecular geometries, reinforcing the necessity of accurate modeling for reliable predictions in biological environments. Ina similar direction, the thesis introduces also a photonic quantum simulation framework for studying full Coulomb interactions between quantum Drude oscillators as a way to study dispersion beyond the dipole approximation typical of current models. This study uncovers nontrivial quantum effects, including the formation of entangled Schrödinger cat states during binding and offering insights into the fundamental nature of dispersion interactions. Moving from fundamental problems to more practical applications, the Quantum Inverse Mapping (QIM) framework is introduced to establish a direct, differentiable connection between quantum mechanical properties and molecular structures. This enables multi-objective molecular design and generation of transition path initializations, demonstrating its utility in navigating chemical spaces for different tasks. Finally, the thesis explores the role of quantum chemistry data in enhancing deep learning models for ADMET property modeling. A systematic study on Graph Transformer reveals that pretraining on atom-level quantum properties improves the model’s representation, leading to superior performance. Collectively, these contributions bridge quantum chemistry with machine learning to address key challenges in molecular exploration, electronic structure calculation, and biological property modeling, advancing computational methodologies for rational drug discovery.  \nPreface  \nThe content of this thesis is partly based on the following papers:  \n• Fallani, A.; Medrano Sandonas, L.; Tkatchenko, A. Inverse Mapping of Quantum Properties to Structures for Chemical Space of Small Organic Molecules. Nat. Commun. 2024 , 15, 6061 .  \nContribution: I conceived and designed this work together with L.M.S., with contributions from A.T. I developed the machine learning code, performed model training, and analyzed the model’s performance across various applications in collaboration with L.M.S. A.T. supervised the project an","cbCaijU78ngr0w3w","https://ap.wps.com/l/cbCaijU78ngr0w3w","pdf",12267537,1,146,"English","en",105,"# Abstract\n## Key thesis contributions\n## Aquamarine dataset analysis\n## Photonic quantum simulation framework\n## Quantum Inverse Mapping (QIM)\n## ADMET modeling with Graph Transformer\n# Preface\n## Included publications and contributions","[{\"question\":\"What core problem does the dissertation address in early-stage drug discovery?\",\"answer\":\"It targets major bottlenecks such as navigating chemical space, modeling molecular interactions, and predicting biological properties with higher accuracy and scalability.\"},{\"question\":\"How does the Aquamarine dataset analysis support future machine learning models?\",\"answer\":\"It analyzes molecular conformations alongside solvation effects and non-covalent interactions, showing that many-body dispersion and implicit solvation significantly affect molecular geometries.\"},{\"question\":\"Which quantum-to-structure learning framework is introduced for practical molecular design?\",\"answer\":\"The Quantum Inverse Mapping (QIM) framework establishes a direct, differentiable link between quantum mechanical properties and molecular structures for multi-objective design and chemical-space navigation.\"}]","Synergies between Quantum Mechanics and Machine Learning for Advancing Pharmaceutical Research - Dissertation Defense | PDF",1785809700,368,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"synergies-between-quantum-mechanics-and-machine-learning-for-advancing-pharmaceutical-research-dissertation-defense","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/synergies-between-quantum-mechanics-and-machine-learning-for-advancing-pharmaceutical-research-dissertation-defense/122259/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",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 core problem does the dissertation address in early-stage drug discovery?","Question",{"text":75,"@type":76},"It targets major bottlenecks such as navigating chemical space, modeling molecular interactions, and predicting biological properties with higher accuracy and scalability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the Aquamarine dataset analysis support future machine learning models?",{"text":80,"@type":76},"It analyzes molecular conformations alongside solvation effects and non-covalent interactions, showing that many-body dispersion and implicit solvation significantly affect molecular geometries.",{"name":82,"@type":73,"acceptedAnswer":83},"Which quantum-to-structure learning framework is introduced for practical molecular design?",{"text":84,"@type":76},"The Quantum Inverse Mapping (QIM) framework establishes a direct, differentiable link between quantum mechanical properties and molecular structures for multi-objective design and chemical-space navigation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"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"]