From explainable to predictive, and to causal

The use of AI algorithms for drug discovery is one of the most promising areas for its societal value. Historically, most deep learning approaches in this area have used black box models, providing little insight into discoveries.

A recent study published in Nature uses explainable graph neural networks to address the urgent need for new antibiotics due to the ongoing antibiotic resistance crisis.

The study begins with the testing and labeling of 39,312 compounds,

– which become training data for four ensembles of graph neural networks,
– which make predictions for a total of 12,076,365 compounds in the test set (hits vs. non-hits based on antibiotic activity and cytotoxicity),
– of which 3,646 compounds are selected based on the probability that they will act as antibiotics without being toxic to humans,
– which are then reduced to 283 compounds by a series of empirical steps,
– and to 4 compounds by experimental testing,
– and of the 4, two “drug-like” compounds are tested in mice,

and one of the two is found to be effective against MRSA infections in this controlled experiment, thus closing the causal loop.

This is a great application of combining explainable predictive models with causal identification, and demonstrates that machine learning models used in high-stakes areas can be explainable without compromising performance.

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