DockMaster-Songdo™ is a web-based AI and quantum-inspired protein preprocessing framework designed to automate and standardize critical preparation workflows for structure-based drug discovery. The platform integrates rule-based automation, LLM-driven workflows, and a future-ready three-level quantum adoption architecture for protein and small-molecule analysis. This work introduces a scalable framework consisting of: Level 0: Classical baseline preprocessing Level 1: Bio-physics-inspired enhancement Level 2: Quantum-optional hybrid architecture The platform automates essential preprocessing tasks including water removal, hydrogen addition, charge assignment, ligand and metal handling, validation, and PDBQT export. In addition, the framework maps practical near-term quantum algorithms such as QAOA, fragment-VQE, quantum-kernel SVM, and FMO/VQE into real-world molecular preprocessing workflows. DockMaster-Songdo™ aims to provide a reproducible, transparent, and regulatory-aware preprocessing environment aligned with international drug discovery standards including FDA, EMA, PMDA, NMPA, and K-FDA expectations. The architecture is designed to operate even without quantum hardware availability, enabling graceful transition from classical AI pipelines to future hybrid quantum-classical systems. This research represents an independent effort toward practical Quantum-AI adoption in computational biology, automated protein preprocessing, and next-generation drug discovery infrastructure.
DockMaster-Songdo™ is a web-based AI and quantum-inspired protein preprocessing framework designed to automate and standardize critical preparation workflows for structure-based drug discovery. The platform integrates rule-based automation, LLM-driven workflows, and a future-ready three-level quantum adoption architecture for protein and small-molecule analysis. This work introduces a scalable framework consisting of: Level 0: Classical baseline preprocessing Level 1: Bio-physics-inspired enhancement Level 2: Quantum-optional hybrid architecture The platform automates essential preprocessing tasks including water removal, hydrogen addition, charge assignment, ligand and metal handling, validation, and PDBQT export. In addition, the framework maps practical near-term quantum algorithms such as QAOA, fragment-VQE, quantum-kernel SVM, and FMO/VQE into real-world molecular preprocessing workflows. DockMaster-Songdo™ aims to provide a reproducible, transparent, and regulatory-aware preprocessing environment aligned with international drug discovery standards including FDA, EMA, PMDA, NMPA, and K-FDA expectations. The architecture is designed to operate even without quantum hardware availability, enabling graceful transition from classical AI pipelines to future hybrid quantum-classical systems. This research represents an independent effort toward practical Quantum-AI adoption in computational biology, automated protein preprocessing, and next-generation drug discovery infrastructure.
We present a computational platform for modeling chemical reactions in complex molecular environments, focused on ligand-protein binding in drug discovery. The platform implements our new quantum-in-quantum-in-classical (QM/QM/MM) multiscale embedding model that integrates molecular dynamics with a quantum-information-enhanced density matrix embedding theory and quantum chemistry solvers, including explicit solvent. Quantum-information metrics are utilized to generate entanglement-consistent orbitals, enabling a high-accuracy description of strongly correlated regions. The framework supports multiple computational backends, including multi-CPU, NVIDIA multi-GPU architectures, and quantum hardware (IQM, IonQ, IBM) integrated under CUDA-Q, and is designed for compatibility with future fault-tolerant quantum systems. The new platform's capabilities are demonstrated by modeling covalent docking of zanubrutinib to Bruton's tyrosine kinase via a Michael addition mechanism, computing the full reaction energy profiles and energy barriers at a reduced computational cost relative to existing methods. As a 2nd-generation anticancer agent, zanubrutinib serves as a proof of concept for covalent inhibitor discovery. Accurate first-principles reaction barrier estimations provided by our method can contribute to reducing false positive and negative rates in drug discovery pipelines. Scalability is validated through benchmarks on GPU clusters, cloud-based CPU infrastructures. We demonstrate integration with quantum devices (up to 20 qubits), alongside resource estimates for fault-tolerant quantum computing, indicating potential speedups of up to 20x. Beyond single reactions, the platform supports the construction of reaction networks in chemical metric space, facilitating ligand screening and systematic exploration of reactive pathways.
Quantum machine learning (QML) has attracted significant interest in drug discovery due to its potential to model complex molecular interactions and improve prediction accuracy for protein-ligand binding affinity. However, most existing hybrid quantum-classical approaches rely on static architectures that apply quantum computation uniformly across all samples, regardless of molecular complexity or prediction confidence. Such static execution strategies lead to inefficient quantum resource utilization and limit scalability on near-term noisy intermediatescale quantum (NISQ) devices. In this paper, we propose an uncertainty-aware adaptive hybrid quantum-classical learning framework for protein-ligand binding affinity prediction that treats quantum computation as a selectively invoked resource rather than a constant component of the learning pipeline. The proposed framework first employs a classical ensemble-based regression model to generate binding affinity predictions along with an associated uncertainty estimate derived from ensemble variance. This uncertainty information is then used by a decision mechanism to dynamically select between classical-only inference, shallow quantum circuit execution, or deep quantum circuit execution. By adapting the quantum circuit depth and invocation frequency to the predicted uncertainty, the framework enables decision-centric allocation of quantum resources. Experimental evaluation of binding affinity data demonstrates that the recommended adaptive framework utilizes around 76 % less quantum resources than static hybrid quantum learning methods that use fixed-depth quantum circuits for all samples. The framework also preserves decision-level functionality and a steady inference workflow. These findings highlight the effectiveness of uncertainty-guided adaptivity as a practical strategy and offer a scalable foundation for future quantum-enhanced molecular learning.method for incorporating QML into drug development pipelines while taking hardware constraints into consideration.
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.
Calculation of binding energies for protein-ligand molecular systems requires accurate treatment of the electronic structure, a quantum chemistry problem that scales exponentially on classical hardware, while current quantum hardware remains too noisy for the required circuit depths. This report presents a hybrid quantum-classical workflow performed on the Fujitsu FX700 ideal state-vector simulator using QARP that addresses two structural inefficiencies in quantum-sampling-based diagonalization workflows. First, we integrate the Linear Scaling CNOT UCCSD (LCNot-UCCSD) ansatz into the QSCI framework, replacing the O(N6) CCSD parameter initialization of the competing LUCJ ansatz approach with O(N4) MP2-amplitude initialization. Second, we introduce QSCI-RBM, a variant that replaces the configuration recovery of the SQD framework with a Restricted Boltzmann Machine (RBM) acting as a compact generative subspace expansion model. Both are evaluated on eight different molecules in STO-3G across 14 controlled artificial error levels with 100 independent runs each, validated on potential energy surface scans of the N2 molecule in cc-pVDZ, and embedded within DMET to treat the FDA-approved antiviral Amantadine (C10H17N, 11 DMET fragments) and the active region of the SARS-CoV-2 main protease complexed with its covalent inhibitor Carmofur (PDB: 7BUY, C15H28N4O5S, 10 fragments). To our knowledge, this is the first deployment of LCNot-UCCSD within QSCI on a quantum computing simulator, and the first DMET-QSCI(LCNot-UCCSD)-RBM application to an industry-relevant protein-ligand system. By utilizing a fraction of the classical computing resources required by the current state-of-the-art work by Cleveland Clinic, RIKEN, and IBM Quantum, this approach enables more efficient and economical drug discovery simulations for the industry.
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.
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.