DeepAI AI Chat
Log In Sign Up

A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs

by   Pietro Bongini, et al.

Predicting drug side-effects before they occur is a key task in keeping the number of drug-related hospitalizations low and to improve drug discovery processes. Automatic predictors of side-effects generally are not able to process the structure of the drug, resulting in a loss of information. Graph neural networks have seen great success in recent years, thanks to their ability of exploiting the information conveyed by the graph structure and labels. These models have been used in a wide variety of biological applications, among which the prediction of drug side-effects on a large knowledge graph. Exploiting the molecular graph encoding the structure of the drug represents a novel approach, in which the problem is formulated as a multi-class multi-label graph-focused classification. We developed a methodology to carry out this task, using recurrent Graph Neural Networks, and building a dataset from freely accessible and well established data sources. The results show that our method has an improved classification capability, under many parameters and metrics, with respect to previously available predictors.


page 1

page 2

page 3

page 4


Modular multi-source prediction of drug side-effects with DruGNN

Drug Side-Effects (DSEs) have a high impact on public health, care syste...

Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information

We propose a novel neural method to extract drug-drug interactions (DDIs...

ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning

In drug discovery, knowledge of the graph structure of chemical compound...

Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction

We introduce Bi-GNN for modeling biological link prediction tasks such a...

Disentangle VAE for Molecular Generation

Automatic molecule generation plays an important role on drug discovery ...

Structure-Based Networks for Drug Validation

Classifying chemicals according to putative modes of action (MOAs) is of...

An Efficient Drug-Drug Interactions Prediction Technology for Molecularly Intelligent Manufacturing

Drug-Drug Interactions (DDIs) prediction is an essential issue in the mo...