國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Efficient Learning from 3D Molecular Structures Using Equivariant Neural Networks./
作者:	Suriana, Patricia.
面頁冊數:	1 online resource (155 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Biology. -
電子資源:	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:

599573
Biology.
Index Terms--Genre/Form:

554714
Electronic books.

Efficient Learning from 3D Molecular Structures Using Equivariant Neural Networks.
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502 $a Thesis (Ph.D.)--Stanford University, 2024.
504 $a Includes bibliographical references
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