
Learning GraphLevel Representation for Drug Discovery
Predicating macroscopic influences of drugs on human body, like efficacy...
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FewShot Graph Learning for Molecular Property Prediction
The recent success of graph neural networks has significantly boosted mo...
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Molecular MechanicsDriven Graph Neural Network with Multiplex Graph for Molecular Structures
The prediction of physicochemical properties from molecular structures i...
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Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
Graph Neural Networks (GNNs) achieve an impressive performance on struct...
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Molecular geometry prediction using a deep generative graph neural network
A molecule's geometry, also known as conformation, is one of a molecule'...
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Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks
We apply a temporal edge prediction model for weighted dynamic graphs to...
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Making Graph Neural Networks Worth It for LowData Molecular Machine Learning
Graph neural networks have become very popular for machine learning on m...
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Molecular graph generation with Graph Neural Networks
The generation of graphstructured data is an emerging problem in the field of deep learning. Various solutions have been proposed in the last few years, yet the exploration of this branch is still in an early phase. In sequential approaches, the construction of a graph is the result of a sequence of decisions, in which, at each step, a node or a group of nodes is added to the graph, along with its connections. A very relevant application of graph generation methods is the discovery of new drug molecules, which are naturally represented as graphs. In this paper, we introduce a sequential molecular graph generator based on a set of graph neural network modules, which we call MG^2N^2. Its modular architecture simplifies the training procedure, also allowing an independent retraining of a single module. The use of graph neural networks maximizes the information in input at each generative step, which consists of the subgraph produced during the previous steps. Experiments of unconditional generation on the QM9 dataset show that our model is capable of generalizing molecular patterns seen during the training phase, without overfitting. The results indicate that our method outperforms very competitive baselines, and can be placed among the state of the art approaches for unconditional generation on QM9.
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