Suchergebnisse - Constrained Graph Variational Autoencoder

Conditional Constrained Graph Variational Autoencoders for Molecule Design

Autoren: Davide Rigoni Nicolo' Navarin Alessandro Sperduti

Quelle: 2020 IEEE Symposium Series on Computational Intelligence (SSCI). :729-736

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences, Machine Learning (cs.LG), 0104 chemical sciences

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/pdf/2009.00725
http://arxiv.org/abs/2009.00725
https://arxiv.org/pdf/2009.00725
https://www.research.unipd.it/handle/11577/3389931
https://arxiv.org/abs/2009.00725
http://dblp.uni-trier.de/db/journals/corr/corr2009.html#abs-2009-00725
https://dblp.uni-trier.de/db/journals/corr/corr2009.html#abs-2009-00725
https://hdl.handle.net/11577/3389931
https://doi.org/10.1109/SSCI47803.2020.9308554

Genetic Constrained Graph Variational Autoencoder for COVID-19 Drug Discovery

Autoren: Cheng, Tianyue Fan, Tianchi Wang, Landi

Index Begriffe: Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, text

URL: http://arxiv.org/abs/2104.11674

Conditional Constrained Graph Variational Autoencoders for Molecule Design

Autoren: Rigoni, Davide Navarin, Nicolò Sperduti, Alessandro

Index Begriffe: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2009.00725

On the Generation of Novel Ligands for SARS-CoV-2 Protease and ACE2 Receptor via Constrained Graph Variational Autoencoders

Autoren: Jasper Kyle Catapang Junie B. Billones

Schlagwörter: 0301 basic medicine, 03 medical and health sciences, 01 natural sciences, 3. Good health, 0104 chemical sciences

Zugangs-URL: https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c74990842e658846db2d79/original/on-the-generation-of-novel-ligands-for-sars-co-v-2-protease-and-ace2-receptor-via-constrained-graph-variational-autoencoders.pdf
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c7494f4c8919e3e9ad3078/original/on-the-generation-of-novel-ligands-for-sars-co-v-2-protease-and-ace2-receptor-via-constrained-graph-variational-autoencoders.pdf
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c7490e337d6c09f4e27666/original/on-the-generation-of-novel-ligands-for-sars-co-v-2-protease-and-ace2-receptor-via-constrained-graph-variational-autoencoders.pdf
https://europepmc.org/article/PPR/PPR118788
https://chemrxiv.org/engage/chemrxiv/article-details/60c7490e337d6c09f4e27666
https://doi.org/10.26434%2Fchemrxiv.12011157.v3

Constrained Graph Variational Autoencoders for Molecule Design

Autoren: Liu, Qi Allamanis, Miltiadis Brockschmidt, Marc et al.

Index Begriffe: Computer Science - Machine Learning, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1805.09076

Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder

Autoren: Amodeo, Carlo Fortel, Igor Ajilore, Olusola et al.

Index Begriffe: Quantitative Biology - Neurons and Cognition, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2207.02328

Structured Graph Variational Autoencoders for Indoor Furniture layout Generation

Autoren: Chattopadhyay, Aditya Zhang, Xi Wipf, David Paul et al.

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/2204.04867

Self-Organizing Maps-Assisted Variational Autoencoder for Unsupervised Network Anomaly Detection.

Autoren: Huang, Hailong Yang, Jiahong Zeng, Hang et al.

Quelle: Symmetry (20738994). Apr2025, Vol. 17 Issue 4, p520. 15p.

Schlagwörter: *DETECTION algorithms, *SELF-organizing maps, *FEATURE selection, *ANOMALY detection (Computer security), *AUTOENCODERS

Reusability report: exploring the utility of variational graph encoders for predicting molecular toxicity in drug design.

Autoren: Li, Ruijiang Lu, Jiang Liu, Ziyi et al.

Quelle: Nature Machine Intelligence; Dec2024, Vol. 6 Issue 12, p1457-1466, 10p

A Generative Framework for Predicting Antiferromagnets.

Autoren: Gong, Jianhu Zhang, Zhengming Fan, Zhenyu et al.

Quelle: Advanced Science. 12/11/2025, Vol. 12 Issue 46, p1-9. 9p.

Aerodynamic shape optimization using graph variational autoencoders and genetic algorithms.

Autoren: Jabón, Jorge Corbera, Sergio Álvarez, Roberto et al.

Quelle: Structural & Multidisciplinary Optimization; Mar2024, Vol. 67 Issue 3, p1-18, 18p

Fragment‐based deep molecular generation using hierarchical chemical graph representation and multi‐resolution graph variational autoencoder.

Autoren: Gao, Zhenxiang Wang, Xinyu Blumenfeld Gaines, Blake et al.

Quelle: Molecular Informatics; May2023, Vol. 42 Issue 5, p1-15, 15p

Schlagwörter: REPRESENTATIONS of graphs, MOLECULAR graphs, MOIETIES (Chemistry), MOLECULAR structure, DRUG discovery

TV-CCANM: a transformer variational inference in confounding cascade additive noise model for causal effect estimation.

Autoren: Ahmad, Sohail Wang, Hong

Quelle: Journal of Statistical Computation & Simulation. Sep2025, Vol. 95 Issue 14, p3048-3076. 29p.

Schlagwörter: *CAUSAL inference, *CAUSAL models

Nearest Neighbours Graph Variational AutoEncoder.

Autoren: Arsini, Lorenzo Caccia, Barbara Ciardiello, Andrea et al.

Quelle: Algorithms; Mar2023, Vol. 16 Issue 3, p143, 17p

Schlagwörter: DEEP learning, PHASE coding

Vergleich verschiedener Variationaler Autencoder-Konzepte zur Generierung effektiver Molekülrepräsentationen von niedermolekularen organischen Verbindungen

Autoren: Szablowski, Darius Neitzke, Michael

Weitere Verfasser: Szablowski, Darius Neitzke, Michael

Schlagwörter: Variationale Autoencoder, niedermolekulare organische Verbindungen, VAE, SMILES, Chem- VAE, fragmentbasiertes Deep-Generatives-Model, Constrained Graph Variational Autoencoder, CGVAE, 004: Informatik, ddc:004

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/20.500.12738/16162

Verfügbarkeit: https://hdl.handle.net/20.500.12738/16162

On the Generation of Novel Ligands for SARS-CoV-2 Protease and ACE2 Receptor via Constrained Graph Variational Autoencoders

Autoren: Catapang, Jasper Kyle Billones, Junie B.

Verfügbarkeit: https://doi.org/10.26434/chemrxiv.12011157.v1
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c7490e337d6c09f4e27666/original/on-the-generation-of-novel-ligands-for-sars-co-v-2-protease-and-ace2-receptor-via-constrained-graph-variational-autoencoders.pdf

DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalities

Autoren: Shaik, Nagur Shareef Cherukuri, Teja Krishna Masood, Adnan et al.

Schlagwörter: Computer Vision and Pattern Recognition, Artificial Intelligence, Machine Learning

Relation: http://arxiv.org/abs/2511.05968

Verfügbarkeit: http://arxiv.org/abs/2511.05968

Investigators at Xiamen University Report Findings in Obesity, Fitness and Wellness (Geometry-based Molecular Generation With Deep Constrained Variational Autoencoder).

Quelle: Drug Week; 6/28/2024, p1058-1058, 1p

Schlagwörter: OBESITY, DRUG discovery, REPRESENTATIONS of graphs, SCIENCE projects, THREE-dimensional imaging

Geografische Kategorien: XIAMEN Shi (China)

Knowledge-embedding deep interpretable graph model for wear prediction: Application in pantograph-catenary systems.

Autoren: Mo, Yutao Peng, Yizhen Wu, Jing et al.

Quelle: Reliability Engineering & System Safety. Sep2025, Vol. 261, pN.PAG-N.PAG. 1p.

Schlagwörter: *URBAN transit systems, *DIRECTED graphs, *AUTOENCODERS, *PANTOGRAPH, *CATENARY, *PREDICTION models

Generating tertiary protein structures via interpretable graph variational autoencoders.

Autoren: Xiaojie Guo Yuanqi Du Tadepalli, Sivani et al.

Quelle: Bioinformatics Advances; 2021, Vol. 1 Issue 1, p1-9, 9p

Schlagwörter: PROTEINS, COMPUTER vision, IMAGE processing, MOTION analysis, MACHINE learning