Enhancing Drug Discovery: Quantum Machine Learning for QSAR Prediction with Incomplete Data

TL;DR

This paper investigates how quantum machine learning can improve QSAR prediction in drug discovery, especially with limited data and features, showing quantum classifiers outperform classical ones under these conditions.

Contribution

It demonstrates the potential quantum advantage in QSAR prediction, particularly in scenarios with limited data and features, using quantum classifiers and PCA-based feature selection.

Findings

Quantum classifiers outperform classical ones with limited features and data.

Quantum advantage observed across multiple open datasets.

Quantum methods enhance generalization in QSAR prediction.

Abstract

Qualitative structure-activity relationship (QSAR) is important for drug discovery and offers valuable insights into the biological interactions of potential drug candidates. It has been demonstrated that QSAR can be accurately predicted by machine learning. However, data with poor quality and limited availability are always the most common and critical issues for medical-related applications for machine learning. In this manuscript, we aim to discuss the performance of classical and quantum classifiers in QSAR prediction and attempt to demonstrate the quantum advantages in the generalization power of the quantum classifier under conditions of limited data availability and a reduced number of features. By applying different data embedding methods followed by feature selection through principal component analysis (PCA), we find that the quantum classifier outperforms the classical one when a small number of features are selected and the number of training samples is limited. The generality of quantum advantages in other open datasets is also explored.