Recent advances in quantum machine learning (QML) have opened new pathways for accelerating early-stage drug discovery through molecular representation in Hilbert space [1]–[3]. In this work, we present a hybrid quantum–classical framework that integrates quantum kernel estimation and variational quantum neural networks (QNNs) for ligand–target binding prediction [4], [5].A synthetic dataset reflecting the statistical behavior of the BindingDB database [6] was constructed to evaluate quantum descriptors and kernel performance under controlled conditions. The proposed quantum feature maps translate seven key molecular descriptors—molecular weight (MW), logP, hydrogen bond donors (HBD), acceptors (HBA), rotatable bonds (RB), aromatic rings (AR), and topological polar surface area (TPSA)—into entangled qubit states [7], [8].Comparative analyses with classical baselines (SVR, Random Forest, and deep neural networks) revealed that quantum embeddings achieve competitive predictive accuracy (RMSE ≈ 0.06) with improved stability under bootstrap resampling [9]. Quantum kernel alignment and sensitivity studies demonstrated that aromaticity and polarity jointly determine the representational power of QML models [10].Beyond performance, we emphasize Explainable Quantum Pharmacology (EQP)—a paradigm in which interpretability, reproducibility, and physicochemical causality are as essential as accuracy [11].Our findings establish a reproducible, interpretable, and computationally efficient foundation for hybrid QML pipelines in molecular modeling, paving the way for next-generation AI-driven drug discovery.
The pharmaceutical industry faces mounting challenges in drug discovery, where exploring a chemical space exceeding 10 60 possible compounds remains computationally prohibitive. Traditional in-silico screening approaches struggle to capture the quantum mechanical interactions that determine molecular behavior. To overcome these limitations, this study introduces a hybrid quantum–classical machine learning framework that uniquely integrates molecular graph theory with quantum-enhanced learning for accelerated drug discovery. Using the QM9 benchmark dataset containing 133,885 molecules of low-to-medium structural complexity, we extract Laplacian eigenvalue spectra as topological descriptors that preserve both connectivity and electronic information. These features are jointly processed by Random Forest (RF), Support Vector Machine (SVM), and a Variational Quantum Classifier (VQC) in a weighted ensemble architecture designed for balanced generalization. The Quantum Graph Neural Network (QGNN) component achieves 97.5% accuracy, while the hybrid ensemble attains 100.0% accuracy, confirming strong predictive reliability. Although quantum training requires roughly 3.2× longer computation, it captures high-dimensional correlations that classical methods overlook. Overall, the proposed framework bridges the gap between theoretical quantum computing and practical pharmaceutical modeling, providing a scalable, physics-inspired pathway toward intelligent molecular screening and next-generation drug discovery systems.
Predicting drug-target binding affinity with limited training data remains a central challenge in computational drug discovery. We introduce a hybrid quantum-classical framework combining molecular descriptors with variational quantum circuits for interpretable binding affinity prediction. Seven physicochemical descriptors (MW, logP, TPSA, HBD/HBA, rotatable bonds, aromatic rings) were encoded into 6-qubit variational circuits using parameterized rotations and controlled-Z entanglement. Quantum kernels were extracted and used for regression on 1,200 BindingDB molecules across kinase, GPCR, and protease targets, with external validation on 300 ChEMBL compounds. Performance was compared against SVR, random forest, gradient boosting, and neural network baselines using 100-fold bootstrap validation. Variational quantum regression achieved MSE = 0.056 ± 0.009, outperforming SVR by 32% (0.082 ± 0.013), RF by 28%, and GBM by 24%. Quantum advantage was most significant in low-data regimes (n < 500), where VQR maintained R2 > 0.85 versus R2 < 0.72 for classical methods. External validation confirmed generalization (R2 = 0.83). Our Explainable Quantum Pharmacology framework revealed TPSA and logP as dominant predictive features through gradientbased sensitivity analysis, aligning with established medicinal chemistry principles. Quantum kernels provide measurable, chemically interpretable improvements for molecular property prediction in data-limited scenarios. This work demonstrates practical quantum advantage for early-stage drug discovery applications on near-term quantum hardware.