Multi-Platform Quantum Computing Simulation Pipeline for ADC Drug Design: HER2–Trastuzumab Interface Analysis presents a quantum-classical hybrid framework for next-generation antibody-drug conjugate (ADC) discovery. This work integrates photonic quantum sampling, neutral-atom optimization, and variational quantum chemistry workflows with AI-assisted molecular design to analyze the HER2–Trastuzumab interface and generate optimized linker–conjugate candidates. The proposed pipeline addresses key limitations of conventional molecular dynamics (MD) and quantum mechanics (QM) simulations in large-scale protein interface analysis, introducing a scalable multi-platform quantum computing architecture for structure-based drug discovery. The study demonstrates a proof-of-concept workflow spanning target definition, quantum-derived residue prioritization, linker rule generation, conjugate assembly, docking validation, and downstream efficacy evaluation. Key highlights include:• 49 interface residues analyzed using photonic boson-sampling simulation• 162 conjugate candidates computationally generated and screened• Quantum-assisted ADC linker optimization workflow• HER2-targeted ADC feasibility validation using hybrid quantum-classical methods• Integration of AI, quantum computing, and molecular modeling for accelerated drug discovery This study represents an independent personal R&D initiative exploring the practical convergence of quantum computing and AI-driven pharmaceutical research, with applications in ADC design, automated protein preprocessing, and next-generation computational therapeutics.
Multi-Platform Quantum Computing Simulation Pipeline for ADC Drug Design: HER2–Trastuzumab Interface Analysis presents a quantum-classical hybrid framework for next-generation antibody-drug conjugate (ADC) discovery. This work integrates photonic quantum sampling, neutral-atom optimization, and variational quantum chemistry workflows with AI-assisted molecular design to analyze the HER2–Trastuzumab interface and generate optimized linker–conjugate candidates. The proposed pipeline addresses key limitations of conventional molecular dynamics (MD) and quantum mechanics (QM) simulations in large-scale protein interface analysis, introducing a scalable multi-platform quantum computing architecture for structure-based drug discovery. The study demonstrates a proof-of-concept workflow spanning target definition, quantum-derived residue prioritization, linker rule generation, conjugate assembly, docking validation, and downstream efficacy evaluation. Key highlights include:• 49 interface residues analyzed using photonic boson-sampling simulation• 162 conjugate candidates computationally generated and screened• Quantum-assisted ADC linker optimization workflow• HER2-targeted ADC feasibility validation using hybrid quantum-classical methods• Integration of AI, quantum computing, and molecular modeling for accelerated drug discovery This study represents an independent personal R&D initiative exploring the practical convergence of quantum computing and AI-driven pharmaceutical research, with applications in ADC design, automated protein preprocessing, and next-generation computational therapeutics.
The exorbitant costs, protracted timelines, and elevated attrition rates associated with traditional drug-discovery pipelines underscore the pressing necessity for computational frameworks that can elucidate biomolecular interactions with quantum-level precision and practical scalability. This study introduces a Hybrid Quantum Classical (HQC) framework that combines quantum variational algorithms, classical molecular dynamics, and quantum-assisted machine-learning optimization into a single drug-screening workflow. It builds on the basic idea of Quantum Molecular Simulation (QMS). In the suggested design, quantum processors are used to selectively fix high-fidelity electronic interactions in reactive binding sites, while classical engines model the large-scale conformational dynamics of biomolecular environments. An adaptive quantum-machine-learning layer speeds up convergence even more by learning how structure and energy are related from quantum-refined descriptors. Benchmark tests against oncogenic targets like EGFR, BCR-ABL1, and p53 show that our method is up to 27% more accurate at converging binding energy and takes 4.3 times less time to compute than standard density-functional-theory and standalone QMS methods. The HQC framework makes quantum drug simulation possible for real-world drug discovery by reducing the limitations of current quantum hardware while keeping electronic-scale accuracy. This study demonstrates that hybrid quantum-classical modeling is a scalable and hardware-compatible approach for advancing next-generation computational drug design and precision therapeutics.
Quantum AI, the integration of quantum computing and artificial intelligence, is revolutionizing drug design by addressing the limitations of traditional pharmaceutical development. This review explores its transformative potential in enhancing molecular simulations, structure-based, target-based, and ligand-based drug discovery, and personalized medicine. Quantum computing leverages principles like superposition, entanglement, and interference to perform complex calculations exponentially faster than classical systems, enabling precise modeling of molecular interactions. Algorithms such as the Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE) outperform classical methods like Density Functional Theory (DFT) in accuracy and efficiency, facilitating the identification of promising drug candidates and optimizing their pharmacokinetic properties. Quantum AI also enhances personalized medicine by analyzing vast genomic datasets to tailor treatments, improving efficacy and minimizing adverse effects. Despite challenges, including noisy intermediate-scale quantum (NISQ) device limitations and the need for advanced error correction, ongoing research and interdisciplinary collaboration are driving progress. Quantum AI promises to reduce development costs, accelerate drug discovery, and democratize access to life-saving medications. By enabling simulation-based discovery and expanding drug-candidate libraries to include peptides and antibodies, it paves the way for breakthroughs in treating complex diseases. This review underscores the need for continued investment in quantum technology to fully harness its potential, positioning the pharmaceutical industry for a future where drug development is faster, more precise, and patient-centric, ultimately improving global healthcare outcomes.
The application of quantum computing to drug discovery can enable an enhanced level of accuracy in estimating molecular properties and conducting chemical simulations. However, there is a considerable distance between real drug-like molecular data and their accessibility for quantum computational analysis. Here, we present an automated, scalable framework that can bridge this gap by extracting drug-like molecules from publicly available biomedical databases, optimizing their molecular geometries, and producing quantum-ready Hamiltonians using widely accepted techniques in quantum chemistry. This method uses the ChEMBL v28 database to retrieve molecules with zero Rule-of-5 violations, maintaining pharmaceutical applicability. These molecules are then prepared using RDKit to convert SMILES notations into chemically valid 3D geometries, enabling force-field-based optimization and downstream quantum analysis. Once these geometries are ready, we use the quantum chemistry module of PennyLane to prepare an electronic Hamiltonian for Variational Quantum Eigensolver (VQE) simulations. This work describes an automated workflow that converts classical molecular information into quantum-ready formats, developed for advanced simulations on noisy quantum simulators and nearterm quantum hardware. Although classical methods remain essential due to their speed and scalability, our study demonstrates that quantum methods can offer competitive accuracy in estimating molecular energies-an important factor in evaluating drug behavior. In conclusion, this paper represents a data-driven link between current chemical databases and next-generation quantum techniques, while also offering an initial roadmap for quantum-accelerated molecular design in the drug discovery arena.
Cancer drug discovery is a resource-intensive process characterized by low success rates, protracted timelines, and significant cost implications. Conventional screening methods—including high-throughput assays and classical molecular modeling—struggle to capture the quantum nature of biomolecular interactions critical to binding affinity and drug specificity. In response, quantum molecular simulation (QMS) has emerged as a transformative approach that leverages the principles of quantum mechanics to enhance the accuracy and efficiency of drug-target interaction modeling. This review explores the theoretical foundations, computational methodologies, and real-world applications of QMS in cancer drug discovery. It discusses key quantum approaches such as Density Functional Theory (DFT), Hartree-Fock (HF), and hybrid QM/MM methods, while evaluating the role of quantum algorithms— including Variational Quantum Eigensolvers (VQE) and Quantum Phase Estimation (QPE)—in elucidating biomolecular structures and energetics. The integration of QMS with next-generation quantum hardware platforms (e.g., superconducting qubits and quantum annealers) and open-source software ecosystems is also reviewed. Comparative performance analyses highlight the advantages of QMS over classical methods in terms of precision, scalability, and its potential for personalized oncology applications. Nonetheless, significant challenges remain, including issues of decoherence, algorithmic noise, regulatory integration, and reproducibility. This paper presents a forward-looking perspective on how QMS, when synergized with artificial intelligence and omics data, could fundamentally reshape the paradigm of cancer therapeutic development by enabling faster, more accurate, and mechanism-driven drug discovery.
Quantum computing is poised to revolutionize pharmaceutical research through its integration with artificial intelligence for drug discovery applications. This article examines how quantum computational approaches address fundamental limitations in traditional drug development pipelines, particularly in molecular modeling and simulation, where classical computing faces exponential scaling challenges. By leveraging quantum mechanical phenomena like superposition and entanglement, quantum algorithms such as the Variational Quantum Eigen solver and the Quantum Approximate Optimization Algorithm offer unprecedented accuracy for simulating protein-ligand interactions and predicting molecular behavior. Strategic industry partnerships between quantum technology companies and pharmaceutical giants are establishing frameworks to translate theoretical quantum advantages into practical applications. The synergistic relationship between AI's pattern recognition capabilities and quantum computing's physical simulation prowess creates a powerful paradigm for accelerating drug development while reducing costs. Applications extend to personalized medicine, where quantum approaches enable the analysis of complex genomic datasets to optimize treatments for individual genetic profiles. While technical challenges persist, the trajectory toward quantum-enhanced drug discovery is clear, with significant benefits anticipated as quantum hardware capabilities continue to advance.
Accurate prediction of binding affinities and efficient lead optimization remain central challenges in computational drug discovery, particularly for complex biological targets characterized by rugged energy landscapes, conformational heterogeneity, and high-dimensional interaction spaces. Classical computational methods—such as molecular docking, force-field–based simulations, and deep learning models—have made substantial progress, yet they often struggle to capture quantum-mechanical effects and long-range correlations critical to molecular recognition. This study explores a quantum-enhanced framework that integrates Variational Quantum Algorithms (VQAs), quantum-inspired Hamiltonian modeling, and hybrid quantum–classical optimization workflows to improve the fidelity and scalability of drug discovery pipelines. By encoding molecular interactions into parameterized quantum circuits, the framework leverages quantum superposition and entanglement to explore complex chemical space more efficiently, enabling refined estimation of binding energies and accelerated identification of high-quality leads. Preliminary simulations indicate that VQA-based estimators outperform classical baselines for challenging protein–ligand systems, reducing prediction error while maintaining computational tractability. These findings highlight the emerging potential of quantum technologies to enable more accurate, data-efficient, and mechanistically grounded drug discovery for next-generation therapeutics.