  One of the main challenges in drug discovery is to find molecules that bind
specifically and strongly to their target protein while having minimal binding
to other proteins. By predicting binding affinity, it is possible to identify
the most promising candidates from a large pool of potential compounds,
reducing the number of compounds that need to be tested experimentally.
Recently, deep learning methods have shown superior performance than
traditional computational methods for making accurate predictions on large
datasets. However, the complexity and time-consuming nature of these methods
have limited their usage and development. Quantum machine learning is an
emerging technology that has the potential to improve many classical machine
learning algorithms. In this work we present a hybrid quantum-classical
convolutional neural network, which is able to reduce by 20% the complexity of
the classical network while maintaining optimal performance in the predictions.
Additionally, it results in a significant time savings of up to 40% in the
training process, which means a meaningful speed up of the drug discovery
process.
