The use of free energy perturbation (FEP) methods to study protein-ligand complexes is one of the most important tools in structure-based drug design. Because FEP methods typically rely on force fields, they may suffer from force field parameter-related issues. Herein, we present a quantum mechanics/molecular mechanics (QM/MM) hybrid method to overcome deficiencies in force-field models by using QM bookending approaches on both classical and quantum hardware. In the MM part of this QM/MM FEP method, AMBER is used to simulate the protein receptor and the unperturbed moiety of the ligand, with the ff19SB and GAFF2 force fields. In the QM part, QUICK was used to conduct Hartree-Fock (HF) calculations, followed by heat-bath configuration interaction (HCI) as a benchmark on classical devices. To enable the HCI function in QUICK, we developed a Python-based interface to execute HCI from IBM's qiskit-addon-dice-solver. Moreover, the same interface also enabled this work to execute QM/MM FEP calculations on quantum hardware using the Local Unitary Cluster Jastrow (LUCJ) ansatz, followed by sample-based diagonalization (SQD) and extended-SQD (extSQD) post-processing. Using a series of thermolysis inhibitors as an example, we find reasonable agreement with experiment between the classical HCI method and the LUCJ-SQD/extSQD method, with the latter yielding a result closer to the experimental value. The execution time between the HCI-based FEP method and the LUCJ-SQD/extSQD-based FEP method is also comparable, indicating a high potential for utility in the noisy intermediate-scale quantum (NISQ) era.
The use of free energy perturbation (FEP) methods to study protein-ligand complexes is one of the most important tools in structure-based drug design. Because FEP methods typically rely on force fields, they may suffer from force field parameter-related issues. Herein, we present a quantum mechanics/molecular mechanics (QM/MM) hybrid method to overcome deficiencies in force-field models by using QM bookending approaches on both classical and quantum hardware. In the MM part of this QM/MM FEP method, AMBER is used to simulate the protein receptor and the unperturbed moiety of the ligand, with the ff19SB and GAFF2 force fields. In the QM part, QUICK was used to conduct Hartree-Fock (HF) calculations, followed by heat-bath configuration interaction (HCI) as a benchmark on classical devices. To enable the HCI function in QUICK, we developed a Python-based interface to execute HCI from IBM's qiskit-addon-dice-solver. Moreover, the same interface also enabled this work to execute QM/MM FEP calculations on quantum hardware using the Local Unitary Cluster Jastrow (LUCJ) ansatz, followed by sample-based diagonalization (SQD) and extended-SQD (extSQD) post-processing. Using a series of thermolysis inhibitors as an example, we find reasonable agreement with experiment between the classical HCI method and the LUCJ-SQD/extSQD method, with the latter yielding a result closer to the experimental value. The execution time between the HCI-based FEP method and the LUCJ-SQD/extSQD-based FEP method is also comparable, indicating a high potential for utility in the noisy intermediate-scale quantum (NISQ) era.
Accurately predicting protein-ligand binding free energies (BFEs) remains a central challenge in drug discovery, particularly because the most reliable methods, such as free energy perturbation (FEP), are computationally intensive and difficult to scale. Here, we introduce a hybrid quantum-classical framework that combines Mining Minima sampling with quantum mechanically refined ligand partial charges, QM/MM interaction evaluation, and variational quantum eigensolver (VQE)-based electronic energy correction. This design enables explicit treatment of polarization, charge redistribution, and electronic correlation effects that are often underestimated in purely classical scoring schemes, while retaining computational efficiency. Across 23 protein targets and 543 ligands, the method achieves a mean absolute error of about 1.10 kcal/mol with strong rank-order fidelity (Pearson R = 0.75, Spearman rho = 0.76, Kendall tau = 0.57), consistent with the performance of contemporary FEP protocols. Notably, the workflow requires only about 25 minutes per ligand on standard compute resources, resulting in an approximate 20-fold reduction in computational cost relative to alchemical free energy approaches. This level of accuracy and efficiency makes the method well-suited for high-throughput lead optimization and iterative design cycles in pharmaceutical discovery. The framework also provides a natural foundation for future integration with machine learning models to enable predictive, large-scale, and adaptive screening strategies.
We present a large-scale experimental study of quantum-computing-based molecular simulation carried out on IQM's Sirius 24-qubit superconducting processor, utilizing up to 16 operational qubits. The work employs Sample-based Quantum Diagonalization (SQD) together with the Local Unitary Cluster Jastrow (LUCJ) ansatz to estimate ground-state energies for a set of benchmark molecules, including H2, LiH, BeH2, H2O, and NH3. In addition, we introduce a Linear-CNOT variant of the Unitary Coupled-Cluster Singles and Doubles (LCNot-UCCSD) ansatz within the SQD workflow, trading higher circuit depth for reduced classical preprocessing. A comparison between these ansätze is provided, clarifying their respective strengths, limitations, and suitability for near-term quantum hardware. We further explore potential energy landscapes through 1D scans for H2 and HeH+ using both STO-3G and 6-31G basis sets, and for LiH and BeH2 in STO-3G. Extending beyond this, we demonstrate the experimental construction of a full 2D potential energy surface for the water molecule on quantum hardware, mapped over a 32 × 32 grid in bond length and bond angle. To move beyond small benchmark systems, we combine SQD(LUCJ) with Density Matrix Embedding Theory (DMET) to compute active-space energies for a set of ligand-like molecules, as well as the pharmacologically relevant amantadine system. Across all studies, the majority of quantum-computed energies agree with reference FCI results, as well as with DMET-CASCI energies for embedded systems, to within chemical accuracy for the chosen basis sets. These results demonstrate the reliability of sample-based diagonalization approaches and underscore the potential of hybrid embedding strategies for extending quantum simulations to increasingly complex molecular systems, while also highlighting their practicality on current IQM quantum hardware.
• A study published on arXiv presents a hybrid framework combining VQE, QM/MM, and statistical thermodynamics to predict protein-ligand binding free energies (BFEs) accurately and rapidly. • Explicitly handles polarization, charge redistribution, and electron correlation—often missed by purely classical methods—achieving an outstanding mean absolute error (MAE) of 1.10 kcal/mol. • Reduces computational costs 20-fold compared to conventional alchemical free energy perturbation (FEP) approaches (taking approximately 25 minutes per ligand), establishing a robust foundation for high-throughput lead optimization and future ML-integrated screening. [Quantum Biology Society] Predicting protein-ligand binding free energy (BFE), a core challenge in drug discovery, has long faced a dilemma: increasing accuracy requires massive amounts of computing resources. Traditional high-precision techniques, such as free energy perturbation (FEP), are computationally intensive and difficult to scale for large-scale applications. Recently, a groundbreaking study has been published that breaks through these limitations using a hybrid quantum-classical algorithm based on a variational quantum eigensolver (VQE), reducing computational costs by 20-fold and drawing intense interest from the pharmaceutical industry. ■ Perfect Synergy Between Quantum Mechanics and Statistical Thermodynamics The study titled "Synergistic Computational Approaches for Accelerated Drug Discovery: Integrating Quantum Mechanics, Statistical Thermodynamics, and Quantum Computing," released on the preprint repository arXiv, combines the advantages of classical and quantum computing. A research team led by Farzad Molani and Art E. Cho (from inCerebro and Korea University) designed a new framework that maximizes both computational efficiency and precision. The researchers integrated quantum mechanically refined ligand partial charges and QM/MM interaction evaluations based on the Mining Minima sampling technique. Notably, they added an electronic energy correction step using a variational quantum eigensolver (VQE), significantly boosting precision without compromising efficiency. ■ Overcoming the Blind Spots of Polarization and Electron Correlation The greatest strength of this hybrid model is its ability to explicitly handle effects such as polarization, charge redistribution, and electron correlation, which are frequently underestimated in purely classical scoring systems. By applying this system to 23 protein targets and 543 diverse ligands, the team successfully achieved an excellent MAE of 1.10 kcal/mol along with high fidelity in ranking predictions. ■ 20x Faster Computation: A Cornerstone for Machine Learning (ML) Integration The most encouraging achievement is the overwhelming computational speed. Using standard computing resources, the workflow completed calculations in approximately 25 minutes per ligand, representing a 20-fold reduction in costs compared to traditional FEP methods. The researchers emphasized that this level of accuracy and efficiency is highly suitable for the high-throughput lead optimization process in pharmaceutical drug discovery. Furthermore, this framework is expected to provide an excellent basis for realizing large-scale adaptive predictive screening strategies through seamless integration with future machine learning (ML) models.
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.
This work presents the implementation of a fragment-based, quantum-centric supercomputing workflow for computing molecular electronic structure using quantum hardware. The workflow is applied to predict the relative energies of two conformers of the 300-atom Trp-cage miniprotein. The methodology employs wave function-based embedding (EWF) as the underlying fragmentation framework, in which all atoms in the system are explicitly included in the CI treatment. CI calculations for individual fragments are performed using either sample-based quantum diagonalization (SQD) for challenging fragments or full configuration interaction (FCI) for trivial fragments. To assess the accuracy of SQD for fragment CI calculations, EWF-(FCI,SQD) results are compared against EWF-MP2 and EWF-CCSD benchmarks. Overall, the results demonstrate that large-scale electronic configuration interaction (CI) simulations of protein systems containing hundreds or even thousands of atoms can be realized through the combined use of quantum and classical computing resources.
We analyzed the performance of quantum algorithms for studying protein-ligand systems, using both simulator and real hardware. To achieve this, we selected thrombin and 5 ligands as a test case and employed a decomposition strategy using density matrix embedded theory, dividing the ligand systems into three fragments each. First, the energy of one of the fragments was calculated using the Variational Quantum Eigensolver (VQE) using a state vector simulator, while the energy of the remaining fragments was calculated using Couple Cluster Singles and Doubles (CCSD), with the protein treated as point charges located at the respective atomic sites. Through these noiseless simulations, we evaluated the results under ideal conditions using the state-of-the-art Unitary CCSD ansatz to validate the efficacy of the strategy in a controlled setting without quantum noise. Subsequently, we evaluated the impact of approximations in the ansatz, optimizer, and active space size, which are necessary to decrease the computational cost, in order to target real hardware during the current Noisy Intermediate-Scale Quantum era. Finally, we performed the VQE calculations using a superconducting quantum computer developed by the RIKEN RQC-Fujitsu Collaboration Center and also analyzed the noise effect through simulations. The results demonstrated that the use of quantum algorithms can enhance the binding energy correlation value, offering potential applications in the workflow of computer-aided drug design.