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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Efficient Learning from 3D Molecular Structures Using Equivariant Neural Networks./
Author:	Suriana, Patricia.
Description:	1 online resource (155 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Symmetry. -
Online resource:	click for full text (PQDT)
ISBN:	9798383013298

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

Efficient Learning from 3D Molecular Structures Using Equivariant Neural Networks. - 1 online resource (155 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383013298Subjects--Topical Terms:

569115
Symmetry.
Index Terms--Genre/Form:

554714
Electronic books.

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 1 online resource (155 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
500 $a Advisor: Feng, Liang;Kundaje, Anshul;Maduke, Merritt;Dror, Ron.
502 $a Thesis (Ph.D.)--Stanford University, 2024.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Symmetry. $3 569115
650 4 $a Neural networks. $3 1011215
650 4 $a Deep learning. $3 1127428
650 4 $a Biology. $3 599573
655 7 $a Electronic books. $2 local $3 554714
690 $a 0306
690 $a 0800
710 2 $a Stanford University. $3 1184533
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 85-12B.
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