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 data sets 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 models from antimalarial data sets. There is a continual need for new antimalarials to address drug resistance, and the readily available in vitro data sets could be utilized with newer machine learning approaches as these develop. Furthermore, quantum machine learning is a relatively new method that uses a quantum computer to perform the calculations. First, we present a classical-quantum hybrid computational approach by building a Latent Bernoulli Autoencoder machine learning model for compressing bit-vector descriptors to a size that can be adapted to 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 Quantum Fourier Transform Classifier. We apply both these approaches to build quantum machine learning models for small-molecule antimalarials with quantum simulation software and then benchmark these quantum models against classical machine learning approaches. While there are many challenges currently facing the development of reliable quantum computers, our results demonstrate that there is potential for the use of this technology in the field of drug discovery.

Figure 1.

Latent Bernoulli Autoencoder. Backpropagations uses a surrogate gradient =1 for the Binarize function to allow training.

Figure 2.

The training process. Step 1: ECFP Fingerprints are encoded from 1024 to 28 bits with the LBAE. Step 2: Variational Circuit is set. Step 3: Measure and make predictions on the antimalarial activity of the compounds in the test set.

Figure 3.

Circuit for a Quantum Variational Classifier. A Single layer is comprised of 28 qubits, however here the circuit is shown for 5 qubits.

Figure 4.

3-bit QFT Classifier.

Figure 5.

The patch method. Features are split into groups and sub-classifiers are trained on each group(n). The trained parametric gates are specific to that group (e.g $n=2$).