QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation

2025-03-02

AI Summary

Predicting drug ADME properties faces dual challenges of imbalanced classification and continuous regression, which existing quantum machine learning (QML) frameworks struggle to address simultaneously. To bridge this gap, we propose the first QML framework supporting automated quantum circuit search and evaluation. Its core contributions are: (1) a novel training-free quantum circuit scoring mechanism enabling joint quantitative assessment for both classification and regression tasks; and (2) a fidelity-based metric extended via continuous quantum state similarity, specifically designed to predict regression performance. Evaluated on a multi-task ADME benchmark, the framework demonstrates moderate positive correlation (r ≈ 0.5) between its scoring metric and empirical performance—significantly outperforming baseline methods (r ≈ 0). This work establishes a new paradigm for interpretable evaluation and practical deployment of QML in real-world pharmaceutical prediction tasks.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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 the first comprehensive approach to searching and evaluating QCS circuits specifically for regression applications. Validation on representative ADME tasks-one imbalanced classification and one regression-demonstrates moderate positive correlation between our scoring metrics and circuit performance, significantly outperforming baseline scoring methods that show negligible correlation.

Problem

• Adapting quantum circuits for drug property prediction.
• Handling imbalanced data in quantum machine learning.
• Developing methods for regression tasks in QML.

Innovation

• Training-free scoring for QML circuits
• Quantify quantum state similarity for regression
• First QCS circuit search for regression tasks