University of Illinois Urbana-Champaign

Machine learning for 3D small molecule drug discovery

Guan, Jiaqi

Content Files
GUAN-DISSERTATION-2024.pdf

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https://hdl.handle.net/2142/124277

Description

Title
Machine learning for 3D small molecule drug discovery
Author(s)
Guan, Jiaqi
Issue Date
2024-04-15
Director of Research (if dissertation) or Advisor (if thesis)
  • Peng, Jian
  • Ma, Jianzhu
Doctoral Committee Chair(s)
Peng, Jian
Committee Member(s)
  • Banerjee, Arindam
  • El-Kebir, Mohammed
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Machine Learning
  • Drug Discovery
  • Generative Models
Abstract
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.
Graduation Semester
2024-05
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/124277
Copyright and License Information
Copyright 2024 Jiaqi Guan

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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois