Integrating Machine Learning and Quantum Circuits for Proton Affinity Predictions

TL;DR

This paper presents a rapid, accurate machine learning approach for predicting proton affinity in molecules, integrating quantum circuit encoders to enhance performance and efficiency in identifying protonation sites.

Contribution

It introduces a hybrid quantum-classical machine learning model using quantum circuits as feature encoders for proton affinity prediction, demonstrating comparable accuracy to classical models.

Findings

Model achieved R2 of 0.96 and MAE of 2.47 kcal/mol.

Quantum circuit encoders performed comparably to classical models on hardware.

Hybrid model is efficient and suitable for complex organic molecules.

Abstract

A key step in interpreting gas-phase ion mobility coupled with mass spectrometry (IM-MS) data for unknown structure prediction involves identifying the most favorable protonated structure. In the gas phase, the site of protonation is determined using proton affinity (PA) measurements. Currently, mass spectrometry and ab initio computation methods are widely used to evaluate PA; however, both methods are resource-intensive and time-consuming. Therefore, there is a critical need for efficient methods to estimate PA, enabling the rapid identification of the most favorable protonation site in complex organic molecules with multiple proton binding sites. In this work, we developed a fast and accurate method for PA prediction by using multiple descriptors in combination with machine learning (ML) models. Using a comprehensive set of 186 descriptors, our model demonstrated strong predictive performance, with an R2 of 0.96 and a MAE of 2.47kcal/mol, comparable to experimental uncertainty. Furthermore, we designed quantum circuits as feature encoders for a classical neural network. To evaluate the effectiveness of this hybrid quantum-classical model, we compared its performance with traditional ML models using a reduced feature set derived from the full set. The result showed that this hybrid model achieved consistent performance comparable to traditional ML models with the same reduced feature set on both a noiseless simulator and real quantum hardware, highlighting the potential of quantum machine learning for accurate and efficient PA predictions.