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.
@article{arxiv.2501.13395,
title = {Enhancing Drug Discovery: Quantum Machine Learning for QSAR Prediction with Incomplete Data},
author = {Wei-Yin Chiang and Po-Yu Kao and Tzu-Lan Yeh and Ya-Chu Yang and Yen-Chu Lin and Alex Zhavoronkov},
journal= {arXiv preprint arXiv:2501.13395},
year = {2025}
}