Efficient Force Evaluation via Fragmentation: Toward Geometry Optimization of Protein-Ligand Systems with Quantum Mechanical Accuracy.

HessFit: Deriving Molecule-Specific Force Fields from Quantum Mechanical Hessians.

Quantum-Driven High-Throughput Docking: Advances in Molecular Scoring and Drug Discovery.

Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.

Fast intermolecular interaction energy calculation with the OPLS-AA force field

A fast pipeline based on the OPLS-AA force field is presented that enables the automated calculation of non-bonding intermolecular (dimer) interaction energies derived from a set of small organic (monomer) molecules. To calculate the non-bonding contributions, optimized geometries of the monomer molecules as well as their OPLS-AA van der Waals and atomic partial charge parameters are required. The key advantage of the new pipeline lies in its fast, highly parallelized in-memory computations without slow I/O operations, which make it possible to thoroughly process comparatively large numbers of (more than a hundred) monomer molecules within acceptable time frames (hours and days): Compared to an analogous, more versatile, and comprehensive approach, the new computational scheme delivers comparable results while being more than two orders of magnitude faster. For locally estimating the required OPLS-AA force field parameters with the LigParGen and BOSS software packages, a user-friendly graphical user interface for the Windows operating system is provided. Scientific contribution Rapid calculation of mutual intermolecular energies for a set of monomer molecules based on the widely used OPLS-AA force field enables a considerable expansion of this type of calculation for practical purposes. The performance improvement can be used to achieve significant improvements of interaction energy averages as well as considerable expansions in the size of the monomer molecule set.

Quantum Computing Calculations of Protein–Ligand Binding Energies Using Decomposition Methods on Simulated and Real Quantum Hardware

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.

Quantum Chemistry in Drug Design: Applications, Challenges, and the Need for Speed.

Benchmark Study for Calculations of pK a Values of Metal Ligands in Proteins

We have compared the performance of 64 different computational methods, based on combined quantum mechanical (QM) and molecular mechanical (QM/MM) or QM-cluster calculations in a continuum solvent, to estimate the acid constant (pK a) of metal-bound ligands in proteins. As a calibration set, we use 12 experimental pK a values from six different proteins that involve Zn2+, Fe3+, or Fe4+. We employ two different density functional theory (DFT) methods (TPSS and B3LYP), two basis sets (def2-SV(P) and def2-TZVPD), QM regions of three different sizes (∼40, ∼100, and ∼350 atoms), relaxed or fixed surroundings, and three different values of the dielectric constant of the continuum-solvation model (ε = 4, 20, or 80). The results clearly show that QM-cluster+continuum-solvation is much better than QM/MM. In general, the most accurate results are obtained with ε = 80 and the minimal QM region. The two DFT methods, the two basis sets, and relaxing or fixing the surroundings give similar results. The best-performing method is TPSS with the minimal QM region, def2-TZVPD, relaxed surroundings, and ε = 80, yielding a mean absolute deviation (after removal of a systematic error of 11.6 pK a units, pu) of 2.0 pu and a maximum deviation of 5.0 pu. The coefficient of determination (R 2) and Kendall’s τ with respect to the experimental pK a values are both 0.64, while Spearman’s rank correlation coefficient is 0.78. This level of accuracy should be sufficient to reliably determine the protonation states of metal-bound ligands in QM-based studies of enzymatic reaction mechanisms.