[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122893-en":3,"doc-seo-122893-105":30,"detail-sidebar-cat-0-en-105":95},{"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":28,"seo_description":14,"update_tm":29,"read_time":11},122893,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","The flux and flow of data - Connecting large datasets with machine learning in a drug discovery environment","This document, authored by Brandon J. Bongers, delves into the critical role of data management and machine learning within the drug discovery process. Titled \"The flux and flow of data: Connecting large datasets with machine learning in a drug discovery environment,\" the work emphasizes the transformative impact of advanced computational approaches on accelerating and optimizing the identification and development of new therapeutic agents. It explores the inherent complexities of handling vast and diverse datasets generated throughout the drug discovery pipeline, from initial target identification to preclinical and clinical development. Bongers highlights how machine learning algorithms are instrumental in extracting meaningful insights from this data, enabling researchers to make more informed decisions, predict compound efficacy, and identify potential candidates with greater efficiency. The document further discusses the infrastructure and methodologies required to effectively connect and analyze these large-scale datasets, underscoring the importance of robust data pipelines and advanced analytical tools. This research serves as a valuable resource for professionals in pharmaceutical research, bioinformatics, and data science, offering insights into the current state and future potential of applying machine learning to overcome the challenges in modern drug discovery.","The flux and flow of data: connecting large datasets with machine learning in a drug discovery envirionment  \nBongers, B.J.  \nCitation  \nBongers, B. J. (2024, May 8) . The flux and flow of data: connecting large datasets with machine learning in a drug discovery envirionment. Retrieved from [https://hdl.handle.net/1887/3753416](https://hdl.handle.net/1887/3753416)  \nVersion: Publisher's Version  \nLicence agreement concerning inclusion of doctoral  \nLicense:  thesis in the Institutional Repository of the University  \nof Leiden  \nDownloaded from:  [https://hdl.handle.net/1887/3753416](https://hdl.handle.net/1887/3753416)  \n[Note:](Note: To cite this publication please use the final published version)[ To cite this publication please use the final published version](Note: To cite this publication please use the final published version) (if applicable) .  \nThe flux and flow of data  \nConnecting large datasets with machine learning ina drug discovery environment  \nBrandon J. Bongers","cbCaihC1MvVYgA5t","https://ap.wps.com/l/cbCaihC1MvVYgA5t","pdf",723546,2,1,3,"English","en",105,"# The flux and flow of data\n## Connecting large datasets with machine learning in a drug discovery environment","[{\"question\":\"Who is the author of this document?\",\"answer\":\"The author of this document is Brandon J. 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