Cargando…

A graph-convolutional neural network model for the prediction of chemical reactivity

We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). The prediction task is factored into two stages comparable to manual expert approaches: considering possible sites of reactivity and evaluating their relative likel...

Descripción completa

Detalles Bibliográficos
Autores principales:	Coley, Connor W., Jin, Wengong, Rogers, Luke, Jamison, Timothy F., Jaakkola, Tommi S., Green, William H., Barzilay, Regina, Jensen, Klavs F.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Royal Society of Chemistry 2018
Materias:	Chemistry
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6335848/ https://www.ncbi.nlm.nih.gov/pubmed/30746086 http://dx.doi.org/10.1039/c8sc04228d

Ejemplares similares

Prediction of Organic Reaction Outcomes Using Machine Learning
por: Coley, Connor W., et al.
Publicado: (2017)

Analyzing Learned Molecular Representations for Property Prediction
por: Yang, Kevin, et al.
Publicado: (2019)

Correction to Analyzing Learned Molecular Representations for Property Prediction
por: Yang, Kevin, et al.
Publicado: (2019)

Computer-Assisted Retrosynthesis Based on Molecular Similarity
por: Coley, Connor W., et al.
Publicado: (2017)

Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning
por: Wang, Xiaoxue, et al.
Publicado: (2020)

Deep learning identifies synergistic drug combinations for treating COVID-19
por: Jin, Wengong, et al.
Publicado: (2021)

Dual graph convolutional neural network for predicting chemical networks
por: Harada, Shonosuke, et al.
Publicado: (2020)

Prediction of pharmacological activities from chemical structures with graph convolutional neural networks
por: Sakai, Miyuki, et al.
Publicado: (2021)

Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network
por: Chen, Jiarui, et al.
Publicado: (2021)

A deep graph convolutional neural network architecture for graph classification
por: Zhou, Yuchen, et al.
Publicado: (2023)

Similarity based enzymatic retrosynthesis
por: Sankaranarayanan, Karthik, et al.
Publicado: (2022)

Using Machine Learning To Predict Suitable Conditions for Organic Reactions
por: Gao, Hanyu, et al.
Publicado: (2018)

Geometric Feature Extraction of Point Cloud of Chemical Reactor Based on Dynamic Graph Convolution Neural Network
por: Xing, Zhizhong, et al.
Publicado: (2021)

A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification
por: Lin, Lan, et al.
Publicado: (2023)

Node Classification in Complex Social Graphs via Knowledge-Graph Embeddings and Convolutional Neural Network
por: Molokwu, Bonaventure C., et al.
Publicado: (2020)

Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks
por: Montanari, Floriane, et al.
Publicado: (2019)

Ear Detection Using Convolutional Neural Network on Graphs with Filter Rotation
por: Tomczyk, Arkadiusz, et al.
Publicado: (2019)

Using graph convolutional neural networks to learn a representation for glycans
por: Burkholz, Rebekka, et al.
Publicado: (2021)

Improved Graph Convolutional Neural Network for Dance Tracking and Pose Estimation
por: Zhang, Liangliang
Publicado: (2022)

Co-embedding of edges and nodes with deep graph convolutional neural networks
por: Zhou, Yuchen, et al.
Publicado: (2023)

Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations
por: Xuan, Ping, et al.
Publicado: (2019)

A graph representation of molecular ensembles for polymer property prediction
por: Aldeghi, Matteo, et al.
Publicado: (2022)

CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph
por: Wang, Wei, et al.
Publicado: (2020)

Computer-assisted multistep chemoenzymatic retrosynthesis using a chemical synthesis planner
por: Sankaranarayanan, Karthik, et al.
Publicado: (2023)

Predicting chemical shifts with graph neural networks
por: Yang, Ziyue, et al.
Publicado: (2021)

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network
por: Wang, Shui-Hua, et al.
Publicado: (2021)

Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks
por: Marzullo, Aldo, et al.
Publicado: (2019)

CGNet: A graph-knowledge embedded convolutional neural network for detection of pneumonia
por: Yu, Xiang, et al.
Publicado: (2021)

GCRNN: graph convolutional recurrent neural network for compound–protein interaction prediction
por: Elbasani, Ermal, et al.
Publicado: (2022)

Simplified, interpretable graph convolutional neural networks for small molecule activity prediction
por: Weber, Jeffrey K., et al.
Publicado: (2021)

Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph
por: Li, Zhibo, et al.
Publicado: (2022)

Enhanced brain tumor classification using graph convolutional neural network architecture
por: Ravinder, M., et al.
Publicado: (2023)

Application of graph frequency attention convolutional neural networks in depression treatment response
por: Lu, Zihe, et al.
Publicado: (2023)

Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors
por: Guan, Yanfei, et al.
Publicado: (2020)

ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs
por: Soto, Paola, et al.
Publicado: (2021)

Structure-based protein function prediction using graph convolutional networks
por: Gligorijević, Vladimir, et al.
Publicado: (2021)

CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction
por: Zeng, Daojian, et al.
Publicado: (2021)

Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform
por: Nambiar, Anirudh M. K., et al.
Publicado: (2022)

SGCN: A Graph Sparsifier Based on Graph Convolutional Networks
por: Li, Jiayu, et al.
Publicado: (2020)

SGGformer: Shifted Graph Convolutional Graph-Transformer for Traffic Prediction
por: Pu, Shilin, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_agjpuguvl7gco74m2q1mvvlbel