Quantum Machine Learning for Drug Discovery

The growing public and private datasets focused on small molecules screened against biological targets or whole organisms 1 provides a wealth of drug discovery relevant data. Increasingly this is used to create machine learning models which can be used for enabling target-based design 2-4 , predict on- or off-target effects and create scoring functions 5,6 . This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large datasets and thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on QC. Here we show how to achieve compression with datasets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands (whole cell screening datasets for plague and M. tuberculosis ) with SVM and data re-uploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This illustrates a quantum advantage for drug discovery to build upon in future.