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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery.

Matthew A Dorsey¹, Kelvin Dsouza², Dhruv Ranganath³

¹Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States.

Journal of Chemical Information and Modeling

|July 16, 2024

Summary

Quantum machine learning models show promise for discovering new antimalarial drugs by analyzing molecular data. This approach utilizes quantum computing to accelerate drug discovery and combat drug resistance.

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Area of Science:

Computational chemistry
Machine learning
Quantum computing

Background:

Machine learning (ML) is crucial in drug discovery for analyzing molecular data and understanding structure-activity relationships.
ML has been applied to phenotypic screening data for diseases like tuberculosis and malaria.
New antimalarials are needed due to increasing drug resistance.

Purpose of the Study:

To apply machine learning to build quantum Quantitative Structure Activity Relationship (QSAR) models for antimalarial drug discovery.
To explore the potential of quantum machine learning (QML) in drug discovery.

Main Methods:

Developed a classical-quantum hybrid approach using a Latent Bernoulli Autoencoder for compressing molecular descriptors.
Applied feature map compression to quantum classification algorithms, including the novel Quantum Fourier Transform Classifier.
Built and benchmarked QML models for small-molecule antimalarials using quantum simulation software against classical ML methods.

Main Results:

Demonstrated a method for compressing bit-vector descriptors for quantum computation with minimal information loss.
Successfully applied QML models to antimalarial data sets.
Benchmarked quantum models against classical ML approaches, showing potential for QML in drug discovery.

Conclusions:

Quantum machine learning offers a promising avenue for accelerating antimalarial drug discovery.
Despite current challenges in quantum computing, the technology has potential applications in pharmaceutical research.
This study highlights the feasibility of using QML for developing new therapeutics against drug-resistant diseases.