Drug Discovery with Graph Neural Networks 

Drug Discovery with Graph Neural Networks 

Drug Discovery with Graph Neural Networks : Learn How to Predict Toxicity with GNNs Using Deepchem — a Deep Learning Library for Life Sciences.


In this article, we will cover another crucial factor that determines whether the drug can pass safety tests — toxicity. In fact, the toxicity accounts for 30% of rejected drug candidates making it one of the most important factors to consider during the drug development stage [1]. Machine learning will prove here very beneficial as it can filter out toxic drug candidates in the early stage of the drug discovery process.

I will assume that you’ve read my previous article which explains some topics and terms that I will be using in this article :) Let’s get started!

Approaching the Problem with Graph Neural Networks

The feature engineering part is pretty much the same as in part 1 of the series. To convert molecular structure into an input for GNNs, we can create molecular fingerprints, or feed it into graph neural network using adjacency matrix and feature vectors. This features can be automatically generated by external software such as RDKit or Deepchem so we don’t have to worry much about it.


The biggest difference is in the machine learning task itself. Toxicity prediction is a classification task, in contrary to the solubility prediction which is a regression task as we might recall from the previous article. There are many different toxicity effects such as carcinogenicity, respiratory toxicity, irritation/corrosion, and others [2]. This makes it a slightly more complicated challenge to work with as we might have to cope also with the imbalanced classes.

Fortunately, the toxicity datasets are often considerably bigger than the solubility counterparts. For example, the Tox21 dataset has ~12k training samples when the Delaney dataset used for solubility prediction has only ~3k training samples. This makes neural networks architectures a more promising approach to use as it can capture more hidden information.

deep-learning life-sciences graph-neural-networks toxicity cheminformatics deep learning

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