Geometry-Based Molecular Generation With Deep Constrained Variational Autoencoder

Finding target molecules with specific chemical properties plays a decisive role in drug development. We proposed GEOM-CVAE, a constrained variational autoencoder based on geometric representation for molecular generation with specific properties, which is protein-context-dependent. In terms of mach...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 35; H. 4; S. 4852 - 4861
Hauptverfasser:	Li, Chunyan, Yao, Junfeng, Wei, Wei, Niu, Zhangming, Zeng, Xiangxiang, Li, Jin, Wang, Jianmin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.04.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Chemical properties Chemicophysical properties Computational modeling Convolution Coordinate Decoding Drug development Drugs Embedding Feature extraction Finite element method geometry graph convolutional network Graph representations Graphical representations Machine learning mesh molecular generation Molecular structure Proteins Solid modeling Two dimensional models variational autoencoder (VAE) Visualization
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!