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.

翻译：生物医学领域正开始探索利用量子机器学习（QML）来完成传统上由经典机器学习处理的任务，尤其是在预测药物评价中至关重要的ADME（吸收、分布、代谢和排泄）性质方面。然而，ADME任务对现有的量子计算系统（QCS）框架提出了独特的挑战，因为它们同时涉及不平衡数据集的分类问题和回归问题。这种双重需求使得必须对现有QCS框架进行调整与优化，以有效应对ADME预测的复杂性。我们提出了一种新颖的无训练评分机制，用于评估QML电路在不平衡分类和回归任务上的性能。我们的机制证明了评分指标与不平衡分类任务测试性能之间存在显著相关性。此外，我们开发了量化量子态之间连续相似性关系的方法，从而能够预测回归任务的性能。这代表了一种专门针对回归应用搜索和评估QCS电路的新型无训练方法。在代表性ADME任务（八项不平衡分类和四项回归）上的验证表明，我们的评分指标与电路性能之间存在中等程度的相关性，其表现显著优于相关性可忽略不计的基线评分方法。