QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910032944168960 |
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| author | Zheng, Kangyu Fu, Tianfan Liang, Zhiding |
| author_facet | Zheng, Kangyu Fu, Tianfan Liang, Zhiding |
| contents | The biomedical field is beginning to explore the use of quantum machine learning (QML) for tasks traditionally handled by classical machine learning, especially in predicting ADME (absorption, distribution, metabolism, and excretion) properties, which are essential in drug evaluation. However, ADME tasks pose unique challenges for existing quantum computing systems (QCS) frameworks, as they involve both classification with unbalanced dataset and regression problems. These dual requirements make it necessary to adapt and refine current QCS frameworks to effectively address the complexities of ADME predictions. We propose a novel training-free scoring mechanism to evaluate QML circuit performance on imbalanced classification and regression tasks. Our mechanism demonstrates significant correlation between scoring metrics and test performance on imbalanced classification tasks. Additionally, we develop methods to quantify continuous similarity relationships between quantum states, enabling performance prediction for regression tasks. This represents a novel training-free approach to searching and evaluating QCS circuits specifically for regression applications. Validation on representative ADME tasks-eight imbalanced classification and four regression-demonstrates moderate correlation between our scoring metrics and circuit performance, significantly outperforming baseline scoring methods that show negligible correlation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01927 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation Zheng, Kangyu Fu, Tianfan Liang, Zhiding Quantum Physics Artificial Intelligence Machine Learning The biomedical field is beginning to explore the use of quantum machine learning (QML) for tasks traditionally handled by classical machine learning, especially in predicting ADME (absorption, distribution, metabolism, and excretion) properties, which are essential in drug evaluation. However, ADME tasks pose unique challenges for existing quantum computing systems (QCS) frameworks, as they involve both classification with unbalanced dataset and regression problems. These dual requirements make it necessary to adapt and refine current QCS frameworks to effectively address the complexities of ADME predictions. We propose a novel training-free scoring mechanism to evaluate QML circuit performance on imbalanced classification and regression tasks. Our mechanism demonstrates significant correlation between scoring metrics and test performance on imbalanced classification tasks. Additionally, we develop methods to quantify continuous similarity relationships between quantum states, enabling performance prediction for regression tasks. This represents a novel training-free approach to searching and evaluating QCS circuits specifically for regression applications. Validation on representative ADME tasks-eight imbalanced classification and four regression-demonstrates moderate correlation between our scoring metrics and circuit performance, significantly outperforming baseline scoring methods that show negligible correlation. |
| title | QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation |
| topic | Quantum Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.01927 |