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Found 10 papers, 6 papers with code
Molecule Attention Transformer
Designing a single neural network architecture that performs competitively across a range of molecule property prediction tasks remains largely an open challenge, and its solution may unlock a widespread use of deep learning in the drug discovery industry.
We Should at Least Be Able to Design Molecules That Dock Well
To close this gap, we propose a benchmark based on docking, a popular computational method for assessing molecule binding to a protein.
Spatial Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds.
Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction
Graph neural networks have recently become a standard method for analysing chemical compounds.
Feature-Based Interpolation and Geodesics in the Latent Spaces of Generative Models
In the case of geodesics, we search for the curves with the shortest length, while in the case of generative models we typically apply linear interpolation in the latent space.
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction
In this work, we propose the novel Prototypical Graph Regression Self-explainable Trees (ProGReST) model, which combines prototype learning, soft decision trees, and Graph Neural Networks.
Processing of incomplete images by (graph) convolutional neural networks
We investigate the problem of training neural networks from incomplete images without replacing missing values.
SONG: Self-Organizing Neural Graphs
Recent years have seen a surge in research on deep interpretable neural networks with decision trees as one of the most commonly incorporated tools.
Relative Molecule Self-Attention Transformer
Self-supervised learning holds promise to revolutionize molecule property prediction - a central task to drug discovery and many more industries - by enabling data efficient learning from scarce experimental data.
Docking-based generative approaches in the search for new drug candidates
Despite the great popularity of virtual screening of existing compound libraries, the search for new potential drug candidates also takes advantage of generative protocols, where new compound suggestions are enumerated using various algorithms.
