[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123287-en":3,"doc-seo-123287-105":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":4,"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},123287,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Combining Molecular Dynamics and Machine Learning for Drug Design - Dissertation","Drug discovery is expensive and slow, and accelerating the path from early screening to market approval requires computational methods that can propose candidates and clarify drug–target recognition mechanisms. This dissertation leverages molecular dynamics (MD) for atomic-level mechanistic insight while addressing MD’s high computational cost and complexity. It tackles postprocessing and high-dimensional trajectory characterization with deep learning, emphasizing representation learning and robust feature validation. The work further modernizes MD and computer-aided drug discovery using web/database technologies aligned with transparent, reproducible data practices.","Combining Molecular Dynamics and Machine Learning for Drug Design  \nDissertation  \nzur  \nErlangung der naturwissenschaftlichen Doktorw¨urde  \n([Dr. sc. nat](Dr. sc. nat).)  \nvorgelegt der  \nMathematisch-naturwissenschaftlichen Fakult¨at  \nder  \nUniversit¨at Z¨urich  \nvon  \nYang Zhang  \naus  \nder Volksrepublik China  \nPromotionskommission:  \nProf. Dr. Amedeo Caflisch (Vorsitz) Prof. Dr. J¨urg Hutter  \nDr. Andreas Vitalis  \nProf. Dr. Vincent Zoete  \nZ¨urich, 2024  \nSummary  \nDrug discovery is a capital-intensive and time-consuming process that requires significant human resources to progress from early discovery to market approval. To accelerate this process, numerous computational techniques have been developed to design potential drug candidates or decipher the recognition patterns between drug molecules and their targets. Molecular dynamics (MD) simulation offers valuable atomic-level mechanistic insights that enhance understanding of molecular interactions, which are critical for drug development. However, MD simulation is a computationally demanding technique with a steep learning curve, necessitating substantial resources, advanced data management, and considerable programming skills. The user-friendliness of scientific software has become a crucial factor in making advanced techniques accessible to a broader audience. Moreover, the conventional, artisanal approach prevalent in MD research is increasingly at odds with modern demands for scientific data management and the growing focus on open and reproducible science. This emphasizes the importance of establishing robust data protocols and transparent methodologies to improve the credibility and applicability of MD research. Recently, significant advancements in information technology have significantly improved the accessibility of personal computers to specialized computer-aided drug discovery processes, providing a unique opportunity to democratize this technique.  \nDue to the data-intensive and high-dimensional nature of MD trajectory, the postprocessing and characterization of structural dynamics remain challenging. Deep learning, featured by its capability to extract hidden patterns from data and its “data-hungry”nature, has been successfully applied to non-Euclidean manifolds and is well-suited to complement MD simulations. However, the majority of deep learning research in drug discovery focuses on static molecular structures, often overlooking the dynamic aspects of molecular interactions. Integrating molecular dynamics trajectories with deep learning offers a promising strategy to not only facilitate the structural perception of machine learning models, but also to improve the reusability of MD trajectories. Effective numerical interpretation and comprehension of molecular structures in machine learning necessitate their abstraction into certain representations, such as graphs, surfaces, point clouds and 3D mesh grids, which are similar to or directly borrowed from the field of computer vision and graphics. This similarity draws intriguing parallels between the two fields, suggesting that methodologies and algorithms from computer vision could be adapted for molecular science. However, unlike computer vision and graphics, which deal with macroscopic objects and visual signal processing, molecular science focuses on atomic-level structures and interactions, necessitating specialized feature engineering. However, feature engineering in molecular science often relies on empirical knowledge and lacks systematic methods for feature validation. Understanding the expressiveness of geometric and topological features is essential to inform the development of more expressive molecular representations.  \nIn this thesis, I concentrate on two primary objectives: firstly, to democratize and modernize computer-aided drug discovery and molecular dynamics simulations through the application of contemporary web and database technologies; and secondly, to explore the methods for ex","cbCaiuXFTVfzQY4B","https://ap.wps.com/l/cbCaiuXFTVfzQY4B","pdf",54342917,1,189,"English","en",105,"# Summary\n# Thesis Objectives\n# Chapter 1: Overview\n# Chapter 2: ACGui Platform and FAIR Data Management\n# Chapter 3: Dataset and Molecular Representation Learning\n# Chapter 4: Dynamic Feature Extraction and Affinity Prediction\n# Appendix: Electric Field Applied Study","[{\"question\":\"Why are molecular dynamics (MD) simulations valuable for drug design?\",\"answer\":\"MD provides atomic-level mechanistic insights into molecular interactions that are critical for understanding and developing drug candidates.\"},{\"question\":\"What challenge does MD pose in practical drug-discovery workflows?\",\"answer\":\"MD is computationally demanding, requires substantial resources and advanced data management, and has a steep learning curve for effective use.\"},{\"question\":\"How does the thesis integrate deep learning with MD trajectories?\",\"answer\":\"It extracts structural dynamic features from MD trajectories and integrates them into modern machine learning models, including 3D convolutional neural networks, to improve prediction of ligand binding affinity.\"}]","Combining Molecular Dynamics and Machine Learning for Drug Design - Dissertation | PDF",1785815765,476,{"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},"combining-molecular-dynamics-and-machine-learning-for-drug-design-dissertation","",{"@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/combining-molecular-dynamics-and-machine-learning-for-drug-design-dissertation/123287/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are molecular dynamics (MD) simulations valuable for drug design?","Question",{"text":75,"@type":76},"MD provides atomic-level mechanistic insights into molecular interactions that are critical for understanding and developing drug candidates.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenge does MD pose in practical drug-discovery workflows?",{"text":80,"@type":76},"MD is computationally demanding, requires substantial resources and advanced data management, and has a steep learning curve for effective use.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis integrate deep learning with MD trajectories?",{"text":84,"@type":76},"It extracts structural dynamic features from MD trajectories and integrates them into modern machine learning models, including 3D convolutional neural networks, to improve prediction of ligand binding affinity.","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"]