Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery
Abstract
Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry and toxicology. Over at least the last 20 years the exploitation of machine learning on molecular datasets has been used to understand the structure-activity relationships that exist between biomolecules and druggable targets. More recently these methods have also seen application for phenotypic screening data for neglected diseases such as tuberculosis and malaria. Herein, we apply machine learning to build quantum Quantitative Structure Activity Relationship (qQSAR) models from antimalarial datasets. There is a continual need for new antimalarials to address drug resistance and the readily available in vitro datasets could be utilized with newer machine learning approaches as these develop. Furthermore, quantum machine learning is a relatively new method which uses a quantum computer to perform the calculations. First, we present a classical-quantum hybrid computational approach by building a Latent Bernoulli Autoencoder (LBAE) machine learning model for compressing bit-vector descriptors to a size that is computable with quantum computers for classification tasks with limited loss of embedded information. Second, we apply our method for feature map compression to quantum classification algorithms, including a completely novel machine learning algorithm with no analogy in classical computers: the Classifier. We apply both these approaches to build…
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