Computational insights into biomolecular systems using artificial intelligence

Park, Hyun

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https://hdl.handle.net/2142/124634 Copy

Description

Title

Computational insights into biomolecular systems using artificial intelligence

Author(s)

Park, Hyun

Issue Date

2024-03-26

Director of Research (if dissertation) or Advisor (if thesis)

Tajkhorshid, Emad

Doctoral Committee Chair(s)

Tajkhorshid, Emad

Committee Member(s)

• Aksimentiev, Aleksei
• Pogorelov, Taras
• Huerta, Eliu A.

Department of Study

School of Molecular & Cell Bio

Discipline

Biophysics & Quant Biology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• 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

Language

eng

Abstract

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.

Graduation Semester

2024-05

Type of Resource

Text

Handle URL

https://hdl.handle.net/2142/124634

Copyright and License Information

Copyright 2024 Hyun Park

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