Warning: Undefined array key "mm" in /www/wwwroot/www.ai-bt.com/si.php on line 10 Deprecated: trim(): Passing null to parameter #1 ($string) of type string is deprecated in /www/wwwroot/www.ai-bt.com/si.php on line 10 A compact review of molecular property prediction with graph neural networks.

Literature DB >> 34895648

A compact review of molecular property prediction with graph neural networks.

Oliver Wieder¹, Stefan Kohlbacher¹, Mélaine Kuenemann², Arthur Garon¹, Pierre Ducrot¹, Thomas Seidel¹, Thierry Langer³.

Abstract

As graph neural networks are becoming more and more powerful and useful in the field of drug discovery, many pharmaceutical companies are getting interested in utilizing these methods for their own in-house frameworks. This is especially compelling for tasks such as the prediction of molecular properties which is often one of the most crucial tasks in computer-aided drug discovery workflows. The immense hype surrounding these kinds of algorithms has led to the development of many different types of promising architectures and in this review we try to structure this highly dynamic field of AI-research by collecting and classifying 80 GNNs that have been used to predict more than 20 molecular properties using 48 different datasets.

Entities: Chemical

Keywords: AI; Computational chemistry; Deep-learning; Drug discovery; Graph neural-networks; Molecular property; Molecular representation; Neural-networks

Mesh：

Year: 2020 PMID： 34895648 DOI： 10.1016/j.ddtec.2020.11.009

Source DB: PubMed Journal: Drug Discov Today Technol ISSN： 1740-6749

Keyword Cloud
Cited

8 in total

A compact review of molecular property prediction with graph neural networks.

1. Accurate Physical Property Predictions via Deep Learning.

Review 2. Deep Learning Concepts and Applications for Synthetic Biology.

3. Improving Small Molecule pK _a Prediction Using Transfer Learning With Graph Neural Networks.

4. Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction.

Review 5. Graph representation learning for structural proteomics.

Review 6. Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs.

Review 7. Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction.

8. Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation.

A compact review of molecular property prediction with graph neural networks.

1. Accurate Physical Property Predictions via Deep Learning.

Review 2. Deep Learning Concepts and Applications for Synthetic Biology.

3. Improving Small Molecule pK a Prediction Using Transfer Learning With Graph Neural Networks.

4. Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction.

Review 5. Graph representation learning for structural proteomics.

Review 6. Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs.

Review 7. Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction.

8. Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation.

3. Improving Small Molecule pK _a Prediction Using Transfer Learning With Graph Neural Networks.