Graph convolutional networks for computational drug development and discovery
By Mengying Sun, Sendong Zhao, C. Gilvary, Olivier Elemento, Jiayu Zhou, Fei Wang. Briefings Bioinform., 2019
382 citations recorded by the source corpus.
Despite the fact that deep learning has achieved remarkable success in various domains over the past decade, its application in molecular informatics and drug discovery is still limited. Recent advances in adapting deep architectures to structured data have opened a new paradigm for pharmaceutical research. In this survey, we provide a systematic review on the emerging field of graph convolutional networks and their applications in drug discovery and molecular informatics. Typically we are interested in why and how graph convolution networks can help in drug-related tasks. We elaborate the existing applications through four perspectives: molecular property and activity prediction, interaction prediction, synthesis prediction and de novo drug design. We briefly introduce the theoretical foundations behind graph convolutional networks and illustrate various architectures based on different formulations. Then we summarize the representative applications in drug-related problems. We also discuss the current challenges and future possibilities of applying graph convolutional networks to drug discovery.
Subfields: Computational Theory and Mathematics, Environmental Chemistry, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/da95d02a50b8208a17127987dde3cdf8da455114
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Citations in the corpus
- Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes (2022, positive): “by treating each 3D structure as a graph and performing message passing scheme on it [1, 18, 34, 41, 43].”
- A review on machine learning approaches and trends in drug discovery (2021, background): “In the last years many researches have been published using this kind of algorithms or variants for a certain function [49].”
- Evolutionary Architecture Search for Graph Neural Networks (2020, background): “Due to the encouraging learning ability for graph-structured data in many domains, GNN has recently seen a plethora of successful realworld applications such as image recognition [5], new drug discovery [6], and traffic prediction [7], ect.”
- Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity (2021, background): “Inspired by the great advantage of graph neural networks (GNNs) in modeling graph data, much attention has been devoted to applying them in computational drug discovery [37], such as the prediction of molecular property [10] and protein interface [25].”
- Graph Neural Networks and Their Current Applications in Bioinformatics (2021, background): “Although previous studies have surveyed deep learning applications in bioinformatics (Wood and Hirst, 2004; Min et al., 2016; Sun et al., 2019) recently published a review paper of GCN in bioinformatics, these studies are confined to a narrow field such as drug discovery in reference.”
- Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities (2022, methodology): “GCNN models have been widely used for predicting the physical properties of both molecules [244, 245, 244] and crystals [235, 246, 247].”
- MoFlow: An Invertible Flow Model for Generating Molecular Graphs (2020, methodology): “orementioned invertible mapping f A|Band f −1 A|B by the scheme of the affine coupling layer. Different from traditional affine coupling layer, our coupling transformation relies on graph convolution [32] and thus we name such a coupling transformation as a graph coupling layer. For each graph coupl”