{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124277"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124277","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine learning for 3D small molecule drug discovery","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["Guan, Jiaqi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Peng, Jian","Ma, Jianzhu","Banerjee, Arindam","El-Kebir, Mohammed"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Machine Learning","Drug Discovery","Generative Models"],"languages":["en","eng"],"rights":["Copyright 2024 Jiaqi Guan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124277","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Peng, Jian","Ma, Jianzhu","Banerjee, Arindam","El-Kebir, Mohammed"]},{"key":"dc:creator","label":"Author","values":["Guan, Jiaqi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-15"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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":["Machine Learning","Drug Discovery","Generative Models"]}]},{"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 Jiaqi Guan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124277"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Jiaqi Guan, accepted the attached license on 2024-04-13 at 16:58.","The student, Jiaqi Guan, submitted this Dissertation for approval on 2024-04-13 at 17:09.","This Dissertation was approved for publication on 2024-04-15 at 15:01.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20398 on 2024-09-16 at 00:34:12","With the rapid development of geometric machine learning and the availability of ever-increasing biological data, there lies a significant opportunity to expedite drug development processes and substantially reduce associated costs by employing appropriate machine learning (ML) algorithms. This dissertation introduces a suite of tailored ML algorithms aimed at addressing critical challenges in 3D small molecule drug discovery, with the overarching goal of shortening drug development cycles and enhancing drug discovery outcomes. We first investigate the fundamental molecular conformation optimization problem and present a neural energy minimization framework to efficiently and accurately predict molecular conformations. Building upon this groundwork, we extend our framework to atom types and establish connections with diffusion-based generative models. This extension facilitates the introduction of TargetDiff, a SE(3)-equivariant diffusion model to generate ligand molecules for specific protein pockets. We then focus on a specific linker design problem in ROteolysis TArgeting Chimeras (PROTACs) discovery where the fragment poses are unknown, and describe how our proposed LinkerNet addresses this problem with a diffusion model and physics-inspired fragment pose prediction module. Finally, we present a novel paradigm for molecular docking by considering multiple ligands docking to the protein pocket. Collectively, this dissertation showcases the potential of machine learning and deep generative models to revolutionize 3D small molecule drug discovery by translating data into accelerated novel discoveries."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Machine learning for 3D small molecule drug discovery"]}]}],"canonical_facts":{"dc:contributor":["Peng, Jian","Ma, Jianzhu","Banerjee, Arindam","El-Kebir, Mohammed"],"dc:creator":["Guan, Jiaqi"],"dc:date":["2024-05","2024-04-15"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Jiaqi Guan, accepted the attached license on 2024-04-13 at 16:58.","The student, Jiaqi Guan, submitted this Dissertation for approval on 2024-04-13 at 17:09.","This Dissertation was approved for publication on 2024-04-15 at 15:01.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20398 on 2024-09-16 at 00:34:12","With the rapid development of geometric machine learning and the availability of ever-increasing biological data, there lies a significant opportunity to expedite drug development processes and substantially reduce associated costs by employing appropriate machine learning (ML) algorithms. This dissertation introduces a suite of tailored ML algorithms aimed at addressing critical challenges in 3D small molecule drug discovery, with the overarching goal of shortening drug development cycles and enhancing drug discovery outcomes. We first investigate the fundamental molecular conformation optimization problem and present a neural energy minimization framework to efficiently and accurately predict molecular conformations. Building upon this groundwork, we extend our framework to atom types and establish connections with diffusion-based generative models. This extension facilitates the introduction of TargetDiff, a SE(3)-equivariant diffusion model to generate ligand molecules for specific protein pockets. We then focus on a specific linker design problem in ROteolysis TArgeting Chimeras (PROTACs) discovery where the fragment poses are unknown, and describe how our proposed LinkerNet addresses this problem with a diffusion model and physics-inspired fragment pose prediction module. Finally, we present a novel paradigm for molecular docking by considering multiple ligands docking to the protein pocket. Collectively, this dissertation showcases the potential of machine learning and deep generative models to revolutionize 3D small molecule drug discovery by translating data into accelerated novel discoveries."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124277"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Jiaqi Guan"],"dc:subject":["Machine Learning","Drug Discovery","Generative Models"],"dc:title":["Machine learning for 3D small molecule drug discovery"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}