Výsledky vyhledávání - "Constrained Graph Variational Autoencoder"

Genetic Constrained Graph Variational Autoencoder for COVID-19 Drug Discovery

Autoři: Cheng, Tianyue Fan, Tianchi Wang, Landi

Témata: Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, text

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

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

Autoři: Szablowski, Darius Neitzke, Michael

Přispěvatelé: Szablowski, Darius Neitzke, Michael

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

Popis souboru: application/pdf

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

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

PCF-VAE: posterior collapse free variational autoencoder for de novo drug design.

Autoři: Bhadwal, Arun Singh Kumari, Monika Kumar, Anil

Zdroj: Scientific Reports; 10/1/2025, Vol. 15 Issue 1, p1-13, 13p

Témata: MOLECULAR structure, DRUG development, LATENT variables, COMPUTER-assisted molecular design, CHEMICAL structure

Conditional Constrained Graph Variational Autoencoders for Molecule Design

Autoři: Rigoni, Davide Navarin, Nicolò Sperduti, Alessandro

Témata: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, text

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

Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus.

Autoři: Karimi, Hirou Adibhesami, Mohammad Anvar Hoseinzadeh, Siamak a další

Zdroj: Buildings (2075-5309); May2024, Vol. 14 Issue 5, p1342, 25p

Témata: DEEP reinforcement learning, REINFORCEMENT learning, DEEP learning, ARCHITECTURAL design, ARTIFICIAL intelligence, TECHNOLOGICAL innovations

The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges.

Autoři: Bandi, Ajay Adapa, Pydi Venkata Satya Ramesh Kuchi, Yudu Eswar Vinay Pratap Kumar

Zdroj: Future Internet; Aug2023, Vol. 15 Issue 8, p260, 60p

Témata: GENERATIVE adversarial networks, LANGUAGE models, ARTIFICIAL intelligence, RESEARCH questions

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

Autoři: Gao, Zhenxiang Wang, Xinyu Blumenfeld Gaines, Blake a další

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

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

Conditional Constrained Graph Variational Autoencoders for Molecule Design

Autoři: Davide Rigoni Nicolo' Navarin Alessandro Sperduti

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

Témata: 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

Popis souboru: application/pdf

Přístupová URL adresa: 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

Language models can learn complex molecular distributions.

Autoři: Flam-Shepherd, Daniel Zhu, Kevin Aspuru-Guzik, Alán

Zdroj: Nature Communications; 6/7/2022, Vol. 13 Issue 1, p1-10, 10p

Témata: LANGUAGE models, CHEMICAL models, LEARNING ability, LANGUAGE ability, MOLECULES

Modern machine learning for tackling inverse problems in chemistry: molecular design to realization.

Autoři: Sridharan, Bhuvanesh Goel, Manan Priyakumar, U. Deva

Zdroj: Chemical Communications; 5/2/2022, Vol. 58 Issue 35, p5316-5331, 16p

Témata: INVERSE problems, DEEP learning, MACHINE learning, GENERATIVE adversarial networks, REINFORCEMENT learning, SMALL molecules

Differential evolution and particle swarm optimization against COVID-19.

Autoři: Piotrowski, Adam P. Piotrowska, Agnieszka E.

Zdroj: Artificial Intelligence Review; Mar2022, Vol. 55 Issue 3, p2149-2219, 71p

Témata: PARTICLE swarm optimization, DIFFERENTIAL evolution, PROGRAMMING languages, COVID-19, METAHEURISTIC algorithms, COVID-19 pandemic

Inverse‐QSPR for de novo Design: A Review.

Autoři: Gantzer, Philippe Creton, Benoit Nieto‐Draghi, Carlos

Zdroj: Molecular Informatics; Apr2020, Vol. 39 Issue 4, p1-21, 21p

Témata: CHEMINFORMATICS, PREDICTION models, FORECASTING, DESIGN

Handbook of Evolutionary Machine Learning

Autoři: Wolfgang Banzhaf Penousal Machado Mengjie Zhang

Resource Type: eBook.

Témata: Machine learning, Evolutionary computation

Categories: COMPUTERS / Artificial Intelligence / General, MATHEMATICS / Probability & Statistics / General, SCIENCE / Life Sciences / Evolution, TECHNOLOGY & ENGINEERING / Engineering (General)

Hands-On Graph Neural Networks Using Python : Practical Techniques and Architectures for Building Powerful Graph and Deep Learning Apps with PyTorch

Autoři: Maxime Labonne

Resource Type: eBook.

Témata: Machine learning, Artificial intelligence, Python (Computer program language), Neural networks (Computer science)

Categories: COMPUTERS / Data Science / Neural Networks, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Languages / Python