A compact review of molecular property prediction with graph neural networks.
By O. Wieder, S. Kohlbacher, Mélaine A. Kuenemann, A. Garon, P. Ducrot, T. Seidel, T. Langer. Drug Discovery Today : Technologies, 2020
545 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/a50f37cdd0614567ef52ffa63c70285d97630ce4
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In Computational Theory and Mathematics
In Materials Chemistry
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Citations in the corpus
- Geometry-enhanced molecular representation learning for property prediction (2021, methodology): “(2) GEM achieves more substantial improvements on the regression datasets than the classification datasets.”
- Graph Representation Learning in Biomedicine and Healthcare (2021, unknown): “A drug’s binding affinity and specificity to its target determine the drug’s effectiveness and potential for off-target effects 34 .”