{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124634"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124634","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Computational insights into biomolecular systems using artificial intelligence","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Park, Hyun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Biophysics & Quant Biology","degree_department":null,"school":null,"contributors":["Tajkhorshid, Emad","Aksimentiev, Aleksei","Pogorelov, Taras","Huerta, Eliu A."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:02Z","subjects":["Ai","Machine Learning","Deep Learning","Biophysics","Biomolecule","Protein","Lipid","Drug","Polymer","Mof","Ai Framework","Transition Pathway","Generative Ai","Drug Discovery","Membrane","Topological Data Analysis","Alphafold","Membtda","Prottransvae","Apace","Ghpmof-assemble","Diffusion Model","Vae","Predictive Model","Molecular Dynamics","Monte Carlo","High Performance Computing","Structure Prediction","Conformational Diversity"],"languages":["en","eng"],"rights":["Copyright 2024 Hyun Park"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124634","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tajkhorshid, Emad","Aksimentiev, Aleksei","Pogorelov, Taras","Huerta, Eliu A."]},{"key":"dc:creator","label":"Author","values":["Park, Hyun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-03-26"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biophysics & Quant Biology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Ai","Machine Learning","Deep Learning","Biophysics","Biomolecule","Protein","Lipid","Drug","Polymer","Mof","Ai Framework","Transition Pathway","Generative Ai","Drug Discovery","Membrane","Topological Data Analysis","Alphafold","Membtda","Prottransvae","Apace","Ghpmof-assemble","Diffusion Model","Vae","Predictive Model","Molecular Dynamics","Monte Carlo","High Performance Computing","Structure Prediction","Conformational Diversity"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Hyun Park"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124634"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Hyun Park, accepted the attached license on 2024-03-20 at 17:35.","The student, Hyun Park, submitted this Dissertation for approval on 2024-03-20 at 17:56.","This Dissertation was approved for publication on 2024-03-26 at 16:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20259 on 2024-09-16 at 00:48:54","In this thesis, the transformative potential of artificial intelligence (AI) in molecular sciences is explored, spanning protein dynamics, biological membrane behavior, polymer morphology prediction, novel material design, and drug development. Leveraging AI algorithms such as variational autoencoders (VAE) and deep neural networks (DNN), novel insights into the transition pathways of transmembrane transporter proteins are uncovered, alongside a deeper understanding of membrane properties through the integration of topological data analysis(TDA) with AI models. Additionally, the development of computational frameworks, including GHP-MOFassemble for accelerated discovery of metal-organic frameworks (MOFs) optimized for CO2 capture, and machine learning approaches for enhanced prediction of polymer morphology, demonstrates the transformative potential of AI in material design. Furthermore, AI methodologies in drug discovery facilitate the design of kinase inhibitor drugs with improved properties and selectivity, while the optimization of large AI models for protein structure prediction expedites drug discovery efforts. Through this interdisciplinary investigation, AI emerges as a powerful tool for addressing complex biological challenges and shaping the future of molecular sciences."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Computational insights into biomolecular systems using artificial intelligence"]}]}],"canonical_facts":{"dc:contributor":["Tajkhorshid, Emad","Aksimentiev, Aleksei","Pogorelov, Taras","Huerta, Eliu A."],"dc:creator":["Park, Hyun"],"dc:date":["2024-05","2024-03-26"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Hyun Park, accepted the attached license on 2024-03-20 at 17:35.","The student, Hyun Park, submitted this Dissertation for approval on 2024-03-20 at 17:56.","This Dissertation was approved for publication on 2024-03-26 at 16:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20259 on 2024-09-16 at 00:48:54","In this thesis, the transformative potential of artificial intelligence (AI) in molecular sciences is explored, spanning protein dynamics, biological membrane behavior, polymer morphology prediction, novel material design, and drug development. Leveraging AI algorithms such as variational autoencoders (VAE) and deep neural networks (DNN), novel insights into the transition pathways of transmembrane transporter proteins are uncovered, alongside a deeper understanding of membrane properties through the integration of topological data analysis(TDA) with AI models. Additionally, the development of computational frameworks, including GHP-MOFassemble for accelerated discovery of metal-organic frameworks (MOFs) optimized for CO2 capture, and machine learning approaches for enhanced prediction of polymer morphology, demonstrates the transformative potential of AI in material design. Furthermore, AI methodologies in drug discovery facilitate the design of kinase inhibitor drugs with improved properties and selectivity, while the optimization of large AI models for protein structure prediction expedites drug discovery efforts. 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