Investigating ML potentials and deep generative models for efficient conformational sampling
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Shenoy, Nikhil
issued: 2024-07-06T08:06:44Z
issued: 2024
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Efficiently sampling from the landscape of molecular conformations is an important task in computational drug discovery. Simulation approaches like Molecular Dynamics (MD) require the use of an energy function that is fast, accurate, transferable and scalable. Traditional energy function methods like Force Fields are fast, but inaccurate while Quantum Mechanical (QM) Methods are accurate but slow. Recently, Machine Learning (ML) potentials trained on datasets labeled with QM methods have become popular as they address the accuracy and speed trade-off. However, generating QM datasets is a cost-intensive exercise, and design choices like conformational diversity (coverage of the conformational landscape) and structural diversity (coverage of the chemical space) during the generation process can introduce biases into the dataset. In the first part of the thesis, we explore the intricate relationship between dataset biases, specifically conformational and structural diversity, and ML potential generalization. We investigate these dynamics through two distinct experiments: a fixed budget one, where the dataset size remains constant, and a fixed molecular set one, which focuses on fixed structural diversity while varying conformational diversity. Our results reveal the critical need for balanced structural and conformational diversity in QM datasets for optimal generalization, which current datasets lack. We believe that these findings can inform future data generation and development of ML potentials that generalize beyond training data. An alternative approach is directly sampling conformations given the molecular graph using deep generative models like Diffusion. Existing competitive approaches either use expensive local structure methods or rely on general-purpose large architecture models without task-specific inductive biases. In the second part of the thesis, we challenge the status quo and develop a simple and scalable deep generative method, Equivariant Transform…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88586
note: Investigating ML Potentials and DeepGenerative Models for EfficientConformational SamplingbyNikhil ShenoyB.Tech., Indian Institute of Technology, New Delhi, 2020A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)July 2024© Nikhil Shenoy 2024The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:Investigating ML Potentials and Deep Generative Models for Effi-cient Conformational Samplingsubmitted by Nikhil Shenoy in partial fulfillment of the requirements forthe degree of Master of Science in Computer Science.Examining Committee:Jiarui Ding, Assistant Professor, Computer Science, University of BritishColumbiaSupervisorDominique Beaini, Adjunct Professor, Computer Science, University of Mon-trealSupervisory Committee MemberKhanh Dao Duc, Assistant Professor, Mathematics, University of BritishColumbiaSupervisory Committee MemberiiAbstractEfficiently sampling from the landscape of molecular conformations is animportant task in computational drug discovery. Simulation approaches likeMolecular Dynamics (MD) require the use of an energy function that is fast,accurate, transferable and scalable. Traditional energy function methodslike Force Fields are fast, but inaccurate while Quantum Mechanical (QM)Methods are accurate but slow. Recently, Machine Learning (ML) potentialstrained on datasets labeled with QM methods have become popular as theyaddress the accuracy and speed trade-off. However, generating QM datasetsis a cost-intensive exercise, and design choices like conformational diversity(coverage of the conformational landscape) and structural diversity (coverageof the chemical space) during the generation process can introduce biasesinto the dataset.In the first part of the thesis, we explore the intricate relationshi…
has type: Thesis/Dissertation
date issued: 2024-11
is shown at: 10.14288/1.0444096
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Ding, Jiarui
type: Text
identifier URI: http://hdl.handle.net/2429/88586