Abstract
Quantitative Structure-Activity Relationship (QSAR) analysis is a computational method that predicts a chemical’s properties, such as its biodegradability, from its molecular structure. It is a powerful, cost-effective alternative to traditional lab testing. While classical machine learning (ML) approaches like k-nearest neighbor (kNN) and support vector machines (SVM) have been successful in QSAR, complex problems remain a challenge. Quantum machine learning (QML), a subfield of quantum computing, has emerged to address problems that may be too complex for classical methods. This study proposes a hybrid classical-quantum model for a QSAR biodegradability classification task. Our model leverages a classical neural network (NN) for feature extraction and a quantum neural network (QNN) for classification. The hybrid model yielded promising results, achieving an accuracy of 87.96%, precision of 84.29%, recall of 79.21%, F1-Score of 81.61%, specificity of 91.85%, and an AUROC of 0.92. Compared to classical models with a similar number of trainable parameters, our hybrid approach achieved performance nearly comparable to state-of-the-art methods. These results suggest QML could become a strong alternative to classical ML in computational chemistry.
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