Quantum Machine Learning for Predicting Binding Free Energies in Structure-Based Virtual Screening

TL;DR

This paper presents a quantum machine learning approach for predicting binding free energies in virtual screening, demonstrating promising accuracy and robustness even with quantum noise, and highlighting its potential for near-term quantum hardware applications.

Contribution

It introduces a novel quantum machine learning model for binding energy prediction, integrating molecular data into quantum states and validating its effectiveness across different quantum simulation settings.

Findings

Achieves RMSD of 2.37 kcal/mol and Pearson correlation of 0.650.

Maintains prediction consistency with 100,000 shots, suitable for near-term quantum hardware.

Noise slightly impacts accuracy but preserves ligand ranking.

Abstract

In structure-based virtual screening, it is often necessary to evaluate the binding free energy of protein-ligand complexes by considering not only molecular conformations but also how these structures shift and rotate in space. The number of possible combinations grows rapidly and can become overwhelming. While classical computing has limitations in this context, quantum computing offers a promising alternative due to its inherent parallelism. In this study, we introduce a quantum machine learning approach that encodes molecular information into quantum states and processes them using parameterized quantum gates. The model is implemented and trained using PyTorch, and its performance is evaluated under three settings: ideal simulation, limited-shot sampling, and simulations with quantum noise. With six quantum circuit units, the model achieves an RMSD of 2.37 kcal/mol and a Pearson correlation of 0.650. Even when using 100,000 shots, the predictions remain consistent, indicating that the model is compatible with near-term quantum hardware. Although noise slightly reduces accuracy, the ranking of ligand affinities remains largely unchanged. These findings point to a practical and scalable strategy that balances robustness and predictive power, offering a viable path to accelerate virtual screening through moderately deep quantum circuits.