Optimizing Quantum Data Embeddings for Ligand-Based Virtual Screening

• URL: http://arxiv.org/abs/2512.16177v1
• Date: Thu, 18 Dec 2025 04:56:53 GMT
• Title: Optimizing Quantum Data Embeddings for Ligand-Based Virtual Screening
• Abstract summary: We develop and evaluate a family of quantum-classical hybrid embedding approaches to generate effective molecular representations.<n>Across multiple targets and class-imbalance settings, several quantum and hybrid embedding variants consistently outperform classical baselines.
• Score: 8.298261599142478
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Effective molecular representations are essential for ligand-based virtual screening. We investigate how quantum data embedding strategies can improve this task by developing and evaluating a family of quantum-classical hybrid embedding approaches. These approaches combine classical neural networks with parameterized quantum circuits in different ways to generate expressive molecular representations and are assessed across two benchmark datasets of different sizes: the LIT-PCBA and COVID-19 collections. Across multiple biological targets and class-imbalance settings, several quantum and hybrid embedding variants consistently outperform classical baselines, especially in limited-data regimes. These results highlight the potential of optimized quantum data embeddings as data-efficient tools for ligand-based virtual screening.

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