What is it about?
Deep neural networks can directly from low-level encoded data without the need of feature extraction. This paper shows how to take directly the molecular graphs to build predictive models for toxicity using available data sets of chemicals and property. Two Graph Convolutional Neural Networks (GCN) models are presented; they predict the yes/no output of the Ames test (a common mutagenicity test). These models take as input the molecular graphs and weight the role of their component subgraphs in producing the output. The net can also give an estimation of the uncertainty of the prediction. Those GCN models can be interpreted; the automatically extracted subgraphs can be compared with the chemical groups considered by the human experts in making the estimations.
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Why is it important?
This paper exploits a large data set of mutagenicity data; besides a set of about six thousand Ames test data of public domain, a very large number of new data derives from the Ames/QSAR International Challenge Project of the National Institute of Health Sciences of Japan.
Perspectives
This paper presents a new class of mutagenicity models. Their main novel characteristics are: - The input is directly the chemical graph. This allows removing possible problems when using other molecular representations. - -There is no need of computing features; in chemistry the computation and choice of the descriptors present many problems. - No functional subgroups are a priori given, removing the bias of the expert knowledge. The important subgroups are found a-posteriori for explanation purposes, not for prediction.
Prof Giuseppina Carla Gini
Politecnico di Milano
Read the Original
This page is a summary of: QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction, Molecular Diversity, June 2021, Springer Science + Business Media,
DOI: 10.1007/s11030-021-10250-2.
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