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Efficient Learning from 3D Molecular...

Suriana, Patricia.

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Efficient Learning from 3D Molecular Structures Using Equivariant Neural Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Efficient Learning from 3D Molecular Structures Using Equivariant Neural Networks./
Author:	Suriana, Patricia.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	155 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Biology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31102260
ISBN:	9798383013298

Efficient Learning from 3D Molecular Structures Using Equivariant Neural Networks.
Suriana, Patricia.

Efficient Learning from 3D Molecular Structures Using Equivariant Neural Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 155 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Stanford University, 2024.

Deep learning methods operating on three-dimensional (3D) molecular structures show promise in addressing vital challenges in biology and chemistry. The scarcity of experimentally determined structures, however, poses a significant hurdle in many machine learning applications. The incorporation of equivariance into deep learning models, leveraging inherent symmetries in structural biology problems, is essential for efficient learning from limited data. This dissertation delves into the utilization of rotationally and translationally equivariant neural networks in various structural biology problems. These include protein model quality assessment, the development of a machine learning--based scoring function for protein-ligand docking that considers protein flexibility, and the implementation of pocket-aware 3D fragment-based ligand optimization.

ISBN: 9798383013298Subjects--Topical Terms:

522710
Biology.

Efficient Learning from 3D Molecular Structures Using Equivariant Neural Networks.
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245 1 0 $a Efficient Learning from 3D Molecular Structures Using Equivariant Neural Networks.
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300 $a 155 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
500 $a Advisor: Feng, Liang;Kundaje, Anshul;Maduke, Merritt;Dror, Ron.
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520 $a Deep learning methods operating on three-dimensional (3D) molecular structures show promise in addressing vital challenges in biology and chemistry. The scarcity of experimentally determined structures, however, poses a significant hurdle in many machine learning applications. The incorporation of equivariance into deep learning models, leveraging inherent symmetries in structural biology problems, is essential for efficient learning from limited data. This dissertation delves into the utilization of rotationally and translationally equivariant neural networks in various structural biology problems. These include protein model quality assessment, the development of a machine learning--based scoring function for protein-ligand docking that considers protein flexibility, and the implementation of pocket-aware 3D fragment-based ligand optimization.
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650 4 $a Symmetry. $3 536815
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