Computation of Absolute Binding Free Energies for Noncovalent Inhibitors with SARS-CoV-2 Main Protease.

Accurate, routine calculation of absolute binding free energies (ABFEs) for protein-ligand complexes remains a key goal of computer-aided drug design since it can enable screening and optimization of drug candidates. For development and testing of related methods, it is important to have high-quality datasets. To this end, from our own experimental studies, we have selected a set of 16 inhibitors of the SARS-CoV-2 main protease (Mpro) with structural diversity and well-distributed BFEs covering a 5 kcal/mol range. There is also minimal structural uncertainty since X-ray crystal structures have been deposited for 12 of the compounds. For methods testing, we report ABFE results from 2 µs molecular dynamics (MD) simulations using free energy perturbation (FEP) theory. The correlation of experimental and computed results is encouraging, with a Pearson's r2 of 0.58 and a Kendall τ of 0.24. The results indicate that current FEP-based ABFE calculations can be used for identification of active compounds (hits). While their accuracy for lead optimization is not yet sufficient, this activity remains addressable in separate lead series by relative BFE calculations.

Assessing Metadynamics and Docking for Absolute Binding Free Energy Calculations Using Severe Acute Respiratory Syndrome Coronavirus 2 Main Protease Inhibitors.

Absolute binding free energy (ABFE) calculations can be an important part of the drug discovery process by identifying molecules that have the potential to be strong binders for a biomolecular target. Recent work has used free energy perturbation (FEP) theory for these calculations, focusing on a set of 16 inhibitors of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). Herein, the same data set is evaluated by metadynamics (MetaD), four different docking programs, and molecular mechanics with generalized Born and surface area solvation. MetaD yields a Kendall τ distance of 0.28 and Pearson r2 of 0.49, which reflect somewhat less accuracy than that from the ABFE FEP results. Notably, it is demonstrated that an ensemble docking protocol by which each ligand is docked into the 13 crystal structures in this data set provides improved performance, particularly when docking is carried out with Glide XP (Kendall τ distance = 0.20, Pearson r2 = 0.71), Glide SP (Kendall τ distance = 0.19, Pearson r2 = 0.66), or AutoDock 4 (Kendall τ distance = 0.21, Pearson r2 = 0.55). The best results are obtained with "superconsensus" docking by averaging the 52 results for each compound using the 4 docking protocols and all 13 crystal structures (Kendall τ distance = 0.18, Pearson r2 = 0.73).

Ensemble Geometric Deep Learning of Aqueous Solubility.

Geometric deep learning is one of the main workhorses for harnessing the power of big data to predict molecular properties such as aqueous solubility, which is key to the pharmacokinetic improvement of drug candidates. Two ensembles of graph neural network architectures were built, one based on spectral convolution and the other on spatial convolution. The pretrained models, denoted respectively as SolNet-GCN and SolNet-GAT, significantly outperformed the existing neural networks benchmarked on a validation set of 207 molecules. The SolNet-GCN model demonstrated the best performance on both the training and validation sets, with RMSE values of 0.53 and 0.72 log molar unit and Pearson r2 values of 0.95 and 0.75, respectively. Further, the ranking power of the SolNet models agreed well with a QM-based thermodynamic cycle approach at the PBE-vdW level of theory on a series of benzophenylurea derivatives and a series of benzodiazepine derivatives. Nevertheless, testing the resultant models on a set of inhibitors of the macrophage migration inhibitory factor (MIF) illustrated that the inclusion of atomic attributes to discriminate atoms with a higher tendency to form intermolecular hydrogen bonds in the crystalline state and to identify planar or nonplanar substructures can be beneficial for the prediction of aqueous solubility.

A potential for molecular simulation of compounds with linear moieties.

The harmonic angle bending potential is used in many force fields for (bio)molecular simulation. The force associated with this potential is discontinuous at angles close to 180°, which can lead to numeric instabilities. Angle bending of linear groups, such as alkynes or nitriles, or linear molecules, such as carbon dioxide, can be treated by a simple harmonic potential if we describe the fluctuations as a deviation from a reference position of the central atom, the position of which is determined by the flanking atoms. The force constant for the linear angle potential can be derived analytically from the corresponding force constant in the traditional potential. The new potential is tested on the properties of alkynes, nitriles, and carbon dioxide. We find that the angles of the linear groups remain about 2° closer to 180° using the new potential. The bond and angle force constants for carbon dioxide were tuned to reproduce the experimentally determined frequencies. An interesting finding was that simulations of liquid carbon dioxide under pressure with the new flexible model were stable only when explicitly modeling the long-range Lennard-Jones (LJ) interactions due to the very long-range nature of the LJ interactions (>1.7 nm). In the other tested liquids, we find that a Lennard-Jones cutoff of 1.1 nm yields similar results as the particle mesh Ewald algorithm for LJ interactions. Algorithmic factors influencing the stability of liquid simulations are discussed as well. Finally, we demonstrate that the linear angle potential can be used in free energy perturbation calculations.

OPLS/2020 Force Field for Unsaturated Hydrocarbons, Alcohols, and Ethers.

The OPLS all-atom force field was updated and applied to modeling unsaturated hydrocarbons, alcohols, and ethers. Testing has included gas-phase conformational energetics, properties of pure liquids, and free energies of hydration. Monte Carlo statistical mechanics (MC) calculations were used to model 60 liquids. In addition, a robust, automated procedure was devised to compute the free energies of hydration with high precision via free-energy perturbation (FEP) calculations using double annihilation. Testing has included larger molecules than in the past, and parameters are reported for the first time for some less common groups including alkynes, allenes, dienes, and acetals. The average errors in comparison with experimental data for the computed properties of the pure liquids were improved with the modified force field (OPLS/2020). For liquid densities and heats of vaporization, the average unsigned errors are 0.01 g/cm3 and 0.2 kcal/mol. The average error and signed error for free energies of hydration are both 1.2 kcal/mol. As noted before, this reflects a systematic overestimate of the hydrophobicity of organic molecules when the parametrization is done to minimize the errors for properties of pure liquids. Implications for the modeling of biomolecular systems with standard force fields are considered.

Refinement of the Optimized Potentials for Liquid Simulations Force Field for Thermodynamics and Dynamics of Liquid Alkanes.

Torsion and Lennard-Jones parameters of the optimized potentials for liquid simulations (OPLS) all-atom force field have been refined for describing thermodynamics and dynamics of a wide range of liquid alkanes. Monte Carlo statistical mechanics (MC) and molecular dynamics (MD) simulations were carried out. For thermodynamics properties, MC simulations with truncated electrostatic interactions performed very closely to MD simulations with a Verlet neighbor list and the particle mesh Ewald algorithm. The average errors in comparison with experimental data for computed properties were improved with the modified force field (OPLS/2020), especially for long-chain alkanes. For liquid densities, heats of vaporization, and free energies of hydration, the average errors are 0.01 g/cm3, 0.2 kcal/mol, and ca. 0.5 kcal/mol, respectively; significant gains were made for relative heats of vaporization of isomeric series. Results for self-diffusion coefficients also reproduce experimental data well for linear alkane liquids up to hexadecane. The new force field is suitable for use in improved modeling of myriad systems of importance in chemistry, biology, and materials science.

Potent Noncovalent Inhibitors of the Main Protease of SARS-CoV-2 from Molecular Sculpting of the Drug Perampanel Guided by Free Energy Perturbation Calculations.

Starting from our previous finding of 14 known drugs as inhibitors of the main protease (Mpro) of SARS-CoV-2, the virus responsible for COVID-19, we have redesigned the weak hit perampanel to yield multiple noncovalent, nonpeptidic inhibitors with ca. 20 nM IC50 values in a kinetic assay. Free-energy perturbation (FEP) calculations for Mpro-ligand complexes provided valuable guidance on beneficial modifications that rapidly delivered the potent analogues. The design efforts were confirmed and augmented by determination of high-resolution X-ray crystal structures for five analogues bound to Mpro. Results of cell-based antiviral assays further demonstrated the potential of the compounds for treatment of COVID-19. In addition to the possible therapeutic significance, the work clearly demonstrates the power of computational chemistry for drug discovery, especially FEP-guided lead optimization.

Identification of 14 Known Drugs as Inhibitors of the Main Protease of SARS-CoV-2.

A consensus virtual screening protocol has been applied to ca. 2000 approved drugs to seek inhibitors of the main protease (M pro ) of SARS-CoV-2, the virus responsible for COVID-19. 42 drugs emerged as top candidates, and after visual analyses of the predicted structures of their complexes with M pro , 17 were chosen for evaluation in a kinetic assay for M pro inhibition. Remarkably 14 of the compounds at 100-µM concentration were found to reduce the enzymatic activity and 5 provided IC 50 values below 40 µM: manidipine (4.8 µM), boceprevir (5.4 µM), lercanidipine (16.2 µM), bedaquiline (18.7 µM), and efonidipine (38.5 µM). Structural analyses reveal a common cloverleaf pattern for the binding of the active compounds to the P1, P1', and P2 pockets of M pro . Further study of the most active compounds in the context of COVID-19 therapy is warranted, while all of the active compounds may provide a foundation for lead optimization to deliver valuable chemotherapeutics to combat the pandemic.

Identification of 14 Known Drugs as Inhibitors of the Main Protease of SARS-CoV-2.

A consensus virtual screening protocol has been applied to ca. 2000 approved drugs to seek inhibitors of the main protease (Mpro) of SARS-CoV-2, the virus responsible for COVID-19. 42 drugs emerged as top candidates, and after visual analyses of the predicted structures of their complexes with Mpro, 17 were chosen for evaluation in a kinetic assay for Mpro inhibition. Remarkably 14 of the compounds at 100-µM concentration were found to reduce the enzymatic activity and 5 provided IC50 values below 40 µM: manidipine (4.8 µM), boceprevir (5.4 µM), lercanidipine (16.2 µM), bedaquiline (18.7 µM), and efonidipine (38.5 µM). Structural analyses reveal a common cloverleaf pattern for the binding of the active compounds to the P1, P1', and P2 pockets of Mpro. Further study of the most active compounds in the context of COVID-19 therapy is warranted, while all of the active compounds may provide a foundation for lead optimization to deliver valuable chemotherapeutics to combat the pandemic.

The Alexandria library, a quantum-chemical database of molecular properties for force field development.

Data quality as well as library size are crucial issues for force field development. In order to predict molecular properties in a large chemical space, the foundation to build force fields on needs to encompass a large variety of chemical compounds. The tabulated molecular physicochemical properties also need to be accurate. Due to the limited transparency in data used for development of existing force fields it is hard to establish data quality and reusability is low. This paper presents the Alexandria library as an open and freely accessible database of optimized molecular geometries, frequencies, electrostatic moments up to the hexadecupole, electrostatic potential, polarizabilities, and thermochemistry, obtained from quantum chemistry calculations for 2704 compounds. Values are tabulated and where available compared to experimental data. This library can assist systematic development and training of empirical force fields for a broad range of molecules.

Phase-Transferable Force Field for Alkali Halides.

A longstanding goal of computational chemistry is to predict the state of materials in all phases with a single model. This is particularly relevant for materials that are difficult or dangerous to handle or compounds that have not yet been created. Progress toward this goal has been limited, as most work has concentrated on just one phase, often determined by particular applications. In the framework of the development of the Alexandria force field, we present here new polarizable force fields for alkali halides with Gaussian charge distributions for molecular dynamics simulations. We explore different descriptions of the van der Waals interaction, like the commonly applied 12-6 Lennard-Jones (LJ), and compare it to "softer" ones, such as the 8-6 LJ, Buckingham, and a modified Buckingham potential. Our results for physicochemical properties of the gas, liquid, and solid phases of alkali halides are compared to experimental data and calculations with reference polarizable and nonpolarizable force fields. The new polarizable force field that employs a modified Buckingham potential predicts the tested properties for gas, liquid, and solid phases with a very good accuracy. In contrast to reference force fields, this model reproduces the correct crystal structures for all alkali halides at low and high temperature. Seeing that experiments with molten salts may be tedious due to high temperatures and their corrosive nature, the models presented here can contribute significantly to our understanding of alkali halides in general and melts in particular.