General Force Field Research Articles

Development of All-Atom Empirical Potentials for Phloroglucinol (Phg) and Hexachlorocyclotriphosphazene (HCCP) Based on their Crystal Phase Structures.

Two existing generic force fields have been augmented with partial charges and tuned in order to give intercompatible all-atom empirical potentials that can satisfactorily represent the known crystal phase structures of the organic phloroglucinol (Phg) (C6H6O3) and inorganic hexachlorocyclotriphosphazene (HCCP) (N3P3Cl6) molecules at several temperatures. It has been proposed that HCCP-Phg network polymers could act as efficient H2 barrier layers in hydrogen storage tanks for cars. However, essential requirements for modeling such networks are adequate representations of both monomers in their pure dense phases using a common form of force field. Tests of their ability to maintain stable crystal structures have been made using classical molecular dynamics (MD) simulations on large 800-molecule supercells. The force fields have been optimized to match the densities calculated from the experimental unit cell dimensions at ambient conditions as well as the intermolecular potential energy, as estimated from experimental enthalpies of sublimation. For Phg, the crystal structure is stabilized by a network of hydrogen bonds and the Coulombic interactions contribute to over 55% of the total intermolecular potential energy. In contrast, the crystal structure of HCCP is intrinsically stabilized by the van der Waals terms. Both optimized force fields reproduce very well the orthorhombic symmetry of their respective crystals under constant-pressure NPT conditions. The model parameters tuned at ambient temperature also give reasonable agreement with crystallographic data at lower temperatures.

Benchmarking Quantum Mechanical Levels of Theory for Valence Parametrization in Force Fields.

A wide range of density functional methods and basis sets are available to derive the electronic structure and properties of molecules. Quantum mechanical calculations are too computationally intensive for routine simulation of molecules in the condensed phase, prompting the development of computationally efficient force fields based on quantum mechanical data. Parametrizing general force fields, which cover a vast chemical space, necessitates the generation of sizable quantum mechanical data sets with optimized geometries and torsion scans. To achieve this efficiently, choosing a quantum mechanical method that balances computational cost and accuracy is crucial. In this study, we seek to assess the accuracy of quantum mechanical theory for specific properties such as conformer energies and torsion energetics. To comprehensively evaluate various methods, we focus on a representative set of 59 diverse small molecules, comparing approximately 25 combinations of functional and basis sets against the reference level coupled cluster calculations at the complete basis set limit.

Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential.

We present a comprehensive study investigating the potential gain in accuracy for calculating absolute solvation free energies (ASFE) using a neural network potential to describe the intramolecular energy of the solute. We calculated the ASFE for most compounds from the FreeSolv database using the Open Force Field (OpenFF) and compared them to earlier results obtained with the CHARMM General Force Field (CGenFF). By applying a nonequilibrium (NEQ) switching approach between the molecular mechanics (MM) description (either OpenFF or CGenFF) and the neural net potential (NNP)/MM level of theory (using ANI-2x as the NNP potential), we attempted to improve the accuracy of the calculated ASFEs. The predictive performance of the results did not change when this approach was applied to all 589 small molecules in the FreeSolv database that ANI-2x can describe. When selecting a subset of 156 molecules, focusing on compounds where the force fields performed poorly, we saw a slight improvement in the root-mean-square error (RMSE) and mean absolute error (MAE). The majority of our calculations utilized unidirectional NEQ protocols based on Jarzynski's equation. Additionally, we conducted bidirectional NEQ switching for a subset of 156 solutes. Notably, only a small fraction (10 out of 156) exhibited statistically significant discrepancies between unidirectional and bidirectional NEQ switching free energy estimates.

Broadening access to small-molecule parameterization with the force field toolkit.

Access to accurate force-field parameters for small molecules is crucial for computational studies of their interactions with proteins. Although a number of general force fields for small molecules exist, e.g., CGenFF, GAFF, and OPLS, they do not cover all common chemical groups and their combinations. The Force Field Toolkit (ffTK) provides a comprehensive graphical interface that streamlines the development of classical parameters for small molecules directly from quantum mechanical (QM) calculations, allowing for force-field generation for almost any chemical group and validation of the fit relative to the target data. ffTK relies on supported external software for the QM calculations, but it can generate the necessary QM input files and parse and analyze the QM output. In previous ffTK versions, support for Gaussian and ORCA QM packages was implemented. Here, we add support for Psi4, an open-source QM package free for all users, thereby broadening user access to ffTK. We also compare the parameter sets obtained with the new ffTK version using Gaussian, ORCA, and Psi4 for three molecules: pyrrolidine, n-propylammonium cation, and chlorobenzene. Despite minor differences between the resulting parameter sets for each compound, most prominently in the dihedral and improper terms, we show that conformational distributions sampled in molecular dynamics simulations using these parameter sets are quite comparable.

Acetonitrile real gas phase behavior from quasi-ideal gas to nanodroplets: A microscopical view.

We have applied a recently developed general purpose acetonitrile force field based on first-principles calculations to simulate acetonitrile in the gas phase at different temperatures and densities. These conditions range from nearly ideal to real gas phase behavior and condensation. The molecular dynamics simulation results agree fairly well with the experimental studies available in the literature on the gas samples. The structural analysis of aggregates and their associated interaction energies is examined and related to the early model proposed on molecular association and equilibrium determining the non-ideal behavior. The formation of dimers is mainly responsible for the non-ideal behavior of the gas at very low density, confirming suggested models based on previous experimental studies. However, when the density of the sample rises, the level of aggregation increases and the simple concept of dimerization does not hold anymore. The real behavior adopted by the gas is related to the distribution of molecular structures observed. The macroscopical view of a real gas as a generic interparticle interaction system without a defined form may then be rationalized on the basis of a defined molecular association originated by a distribution of aggregates at the low density regime. The sample with the highest density (∼1.4 × 103 molm-3) at the lowest temperature exhibits a massive aggregation where most of the acetonitrile (ACN) molecules in the simulation box form a big cluster. Its radial distribution function is similar to that of the liquid ACN. This strongly inhomogeneous distribution in the box can be considered a condensation in the gas phase under specific density-T conditions. This formation opens the door to the potential tuning of its solvent properties as a function of its size in these nanodroplets that in turn are controlled by the density-T conditions.

Exploring the Limits of the Generalized CHARMM and AMBER Force Fields through Predictions of Hydration Free Energy of Small Molecules.

Accurate force field parameters, potential energy functions, and receptor-ligand models are essential for modeling the solvation and binding of drug-like molecules to a receptor. A large and ever-growing chemical space of medicinally relevant scaffolds has also required these factors, especially force field parameters, to be highly transferable. Generalized force fields such as the CHARMM General Force Field (CGenFF) and the generalized AMBER force field (GAFF) have accomplished this feat along with other contemporaneous ones like OPLS. Here, we analyze the limits in the parametrization of drug-like small molecules by CGenFF and GAFF in terms of the various functional groups represented within them. Specifically, we link the presence of specific functional groups to the error in the absolute hydration free energy of over 600 small molecules, predicted by alchemical free energy methods implemented in the CHARMM program. Our investigation reveals that molecules with (i) a nitro group in CGenFF and GAFF are, respectively, over- or undersolubilized in aqueous medium, (ii) amine groups are undersolubilized more so in CGenFF than in GAFF, and (iii) carboxyl groups are more oversolubilized in GAFF than in CGenFF. We present our analyses of the potential factors underlying these trends. We also showcase the use of a machine-learning-based approach combined with the SHapley Additive exPlanations framework to attribute these trends to specific functional groups, which can be easily adopted to explore the limits of other general force fields.

Conservative and nonconservative forces for Mie particles in acoustic trapping

A general acoustic force field can be decomposed into a conservative gradient force (GF) and a non-conservative scattering force (SF), which have very different physical and mathematical properties. However, the profiles of such forces for Mie particles are unknown, let alone their underlying physics. Here, by using a fast Fourier transform approach, we calculated the GF and SF for spherical particle of various sizes and various incident waves. For the same focused incident waves, the normalized GF and SF are similar for different particle sizes, while the total force can be quite different owing to the varying relative strength between the GF and SF. GF and SF possess symmetries that are not found in the incident waves, indicating that these physically and mathematically distinct forces have symmetries that are hidden from the beam profile. For a vortex beam carrying a well-defined topological charge, acoustic forces alone cannot trap particles.

Optimization of CHARMM force field parameters for ryanodine receptor inhibitory drug dantrolene using FFTK and FFParam.

Ryanodine receptors (RyRs) are large intracellular ligand-gated calcium release ion channels. Mutations in human RyR1 in combination with a volatile anesthetic or muscle relaxant are known to cause leaky RyRs resulting in malignant hyperthermia (MH). This has long been primarily treated with the RyR inhibitory drug dantrolene. Alternatives to dantrolene as a RyR inhibitor may be found through computer-aided drug design. Additionally, molecular dynamics (MD) studies of dantrolene interacting with RyRs may reveal its full mechanism of action. The availability of accurate force field parameters is important for the success of both. In this study, force field parameters for dantrolene were obtained from the CHARMM General Force Field (CGenFF) program and optimized using the force field toolkit (FFTK) and FFParam programs. The obtained parameters were then validated by a comparison between calculated and experimental IR spectra and normal mode analysis, among other techniques.

A Fast, Convenient, Polarizable Electrostatic Model for Molecular Dynamics.

We present an efficient polarizable electrostatic model, utilizing typed, atom-centered polarizabilities and the fast direct approximation, designed for efficient use in molecular dynamics (MD) simulations. The model provides two convenient approaches for assigning partial charges in the context of atomic polarizabilities. One is a generalization of RESP, called RESP-dPol, and the other, AM1-BCC-dPol, is an adaptation of the widely used AM1-BCC method. Both are designed to accurately replicate gas-phase quantum mechanical electrostatic potentials. Benchmarks of this polarizable electrostatic model against gas-phase dipole moments, molecular polarizabilities, bulk liquid densities, and static dielectric constants of organic liquids show good agreement with the reference values. Of note, the model yields markedly more accurate dielectric constants of organic liquids, relative to a matched nonpolarizable force field. MD simulations with this method, which is currently parametrized for molecules containing elements C, N, O, and H, run only about 3.6-fold slower than fixed charge force fields, while simulations with the self-consistent mutual polarization average 4.5-fold slower. Our results suggest that RESP-dPol and AM1-BCC-dPol afford improved accuracy relative to fixed charge force fields and are good starting points for developing general, affordable, and transferable polarizable force fields. The software implementing these approaches has been designed to utilize the force field fitting frameworks developed and maintained by the Open Force Field Initiative, setting the stage for further exploration of this approach to polarizable force field development.

Quantifying the Nearly Random Microheterogeneity of Aqueous tert-Butyl Alcohol Solutions Using Vibrational Spectroscopy.

The microheterogeneous structure of aqueous tert-butyl alcohol (TBA) solutions is quantified by combining experimental, simulations, and theoretical results. Experimental Raman multivariate curve resolution (Raman-MCR) C-H frequency shift measurements are compared with predictions obtained using combined quantum mechanical and effective fragment potential (QM/EFP) calculations, as well as with molecular dynamics (MD), random mixture (RM), and finite lattice (FL) predictions. The results indicate that the microheterogeneous aggregation in aqueous TBA solutions is slightly less than that predicted by MD simulations performed using either CHARMM generalized force field (CGenFF) or optimized parameters for liquid simulations all atom (OPLS-AA) force fields but slightly more than that in a self-avoiding RM of TBA-like molecules. The results imply that the onset of microheterogeneity in aqueous solutions occurs when solute contact free energies are about an order of magnitude smaller than thermal fluctuations, thus suggesting a fundamental bound of relevance to biological self-assembly.

Intermolecular Non-Bonded Interactions from Machine Learning Datasets

Accurate determination of intermolecular non-covalent-bonded or non-bonded interactions is the key to potentially useful molecular dynamics simulations of polymer systems. However, it is challenging to balance both the accuracy and computational cost in force field modelling. One of the main difficulties is properly representing the calculated energy data as a continuous force function. In this paper, we employ well-developed machine learning techniques to construct a general purpose intermolecular non-bonded interaction force field for organic polymers. The original ab initio dataset SOFG-31 was calculated by us and has been well documented, and here we use it as our training set. The CLIFF kernel type machine learning scheme is used for predicting the interaction energies of heterodimers selected from the SOFG-31 dataset. Our test results show that the overall errors are well below the chemical accuracy of about 1 kcal/mol, thus demonstrating the promising feasibility of machine learning techniques in force field modelling.

Minimal Peptoid Dynamics Inform Self-Assembly Propensity.

Peptoids are structural isomers of natural peptides, with side chain attachment at the amide nitrogen, conferring this class of compounds with the ability to access both cis and trans ω torsions as well as an increased diversity of ψ/φ states with respect to peptides. Sampling within these dimensions is controlled through side chain selection, and an expansive set of viable peptoid residues exists. It has been shown recently that "minimal" di- and tripeptoids with aromatic side chains can self-assemble into highly ordered structures, with size and morphological definition varying as a function of sequence pattern (e.g., XFF and FXF, where X = a nonaromatic peptoid monomer). Aromatic groups, such as phenylalanine, are regularly used in the design of minimal peptide assemblers. In recognition of this, and to draw parallels between these compounds classes, we have developed a series of descriptors for intramolecular dynamics of aromatic side chains to discern whether these dynamics, in a preassembly condition, can be related to experimentally observed nanoscale assemblies. To do this, we have built on the atomistic peptoid force field reported by Weiser and Santiso (CGenFF-WS) through the rigorous fitting of partial charges and the collation of Charmm General Force Field (CGenFF) parameters relevant to these systems. Our study finds that the intramolecular dynamics of side chains, for a given sequence, is dependent on the specific combination of backbone ω torsions and that homogeneity of sampling across these states correlates well with the experimentally observed ability to assemble into nanomorphologies with long-range order. Sequence patterning is also shown to affect sampling, in a manner consistent for both tripeptoids and tripeptides. Additionally, sampling similarities between the nanofiber forming tripeptoid, Nf-Nke-Nf in the cc state, and the nanotube forming dipeptide FF, highlight a structural motif which may be relevant to the emergence of extended linear assemblies. To assess these properties, a variety of computational approaches have been employed.

Development of CHARMM compatible force field parameters and molecular dynamics simulations for the pesticide flupyradifurone.

Flupyradifurone (FLU) is a novel butenolide insecticide with partial agonist activity for insect nicotinic acetylcholine receptors. Its safety for non-target organisms has been questioned in the literature, despite initial claims of its harmlessness. Detailed understanding of its toxicity and related molecular mechanisms remain under discussion. Thus, in this work, an optimized set of CHARMM compatible parameters for FLU is presented. CHARMM General Force Field program was used as a starting point while the non-bonded and bonded parameters were adjusted and optimized to reproduce MP2/6-31G(d) accuracy level results. For the validity assessment of these parameters, infrared spectrum, water-octanol partition coefficient, and normal modes were computed and compared to experimental values found in the literature. Several MD simulations of FLU in water and FLU in complex with an acetylcholine-binding protein were performed to estimate the ability of the optimized parameters to correctly describe its torsional space and reproduce observed crystallographic trends respectively.

Use of molecular simulation for evaluating adsorption equilibrium of inhalation anaesthetic agents on metal–organic frameworks

AbstractMetal–organic frameworks (MOFs) were studied as alternatives to zeolites and activated carbon for adsorptive removal of wasted inhalation anaesthetic agents (IAA). Monte Carlo simulation was used to predict equilibrium adsorption isotherms of IAA on selected MOFs. Rather than generic forcefields (FFs), the all‐atom FF parameters published by Arcario were used for IAA modelling. Continuous fractional component Monte Carlo (CFCMC) proved crucial for speedy simulation of large molecules. We found that allocating 70% probability to the CFlambdaSwap move gave optimum fits between simulation and experiment. The simulations provided us with an insight into the adsorption mechanisms of IAA in these structures. Heats of adsorption, Brauner‐Emmet‐Teller (BET) surface area, and total pore volume were deduced to be the crucial parameters for low, medium, and high range of relative pressures in the isotherm. Therefore, the chromium atoms in MIL‐101‐Cr are better adsorbers of IAA than MIL‐100‐Al at lower pressures despite the similarities in terms of the type of linkers and topology. Our simulation results corroborated the earlier published studies on the self‐association behaviour of sevoflurane molecules based on the experimental isotherms reported for MOF‐177‐Zn. Finally, the high polarity of IAA is thought to explain good low‐pressure simulation/experiment data agreement for the MOFs possessing coordinatively unsaturated sites (CUS) despite using generic DREIDING FF for the framework atoms. Our in‐house parsing code helped realize that the grand‐canonical Monte‐Carlo simulation speed is not the same for all pressure points but decreases for higher pressure points. This can be explained by increased density of the adsorbates making successful trial moves less probable.

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions.

A key goal of molecular modeling is the accurate reproduction of the true quantum mechanical potential energy of arbitrary molecular ensembles with a tractable classical approximation. The challenges are that analytical expressions found in general purpose force fields struggle to faithfully represent the intermolecular quantum potential energy surface at close distances and in strong interaction regimes; that the more accurate neural network approximations do not capture crucial physics concepts, e.g., nonadditive inductive contributions and application of electric fields; and that the ultra-accurate narrowly targeted models have difficulty generalizing to the entire chemical space. We therefore designed a hybrid wide-coverage intermolecular interaction model consisting of an analytically polarizable force field combined with a short-range neural network correction for the total intermolecular interaction energy. Here, we describe the methodology and apply the model to accurately determine the properties of water, the free energy of solvation of neutral and charged molecules, and the binding free energy of ligands to proteins. The correction is subtyped for distinct chemical species to match the underlying force field, to segment and reduce the amount of quantum training data, and to increase accuracy and computational speed. For the systems considered, the hybrid ab initio parametrized Hamiltonian reproduces the two-body dimer quantum mechanics (QM) energies to within 0.03 kcal/mol and the nonadditive many-molecule contributions to within 2%. Simulations of molecular systems using this interaction model run at speeds of several nanoseconds per day.

SwissParam 2023: A Modern Web-Based Tool for Efficient Small Molecule Parametrization.

Most steps of drug discovery are now routinely supported and accelerated by computer-aided drug design tools. Among them, structure-based approaches use the three-dimensional structure of the targeted biomacromolecule as a major source of information. When it comes to calculating the interactions of small molecules with proteins using the equations of molecular mechanics, topologies, atom typing, and force field parameters are required. However, generating parameters for small molecules remains challenging due to the large number of existing chemical groups. The SwissParam web tool was first released in 2011 with the aim of generating parameters and topologies for small molecules based on the Merck molecular force field (MMFF) while being compatible with the CHARMM22/27 force field. Here, we present an updated version of SwissParam, providing various new features, including the possibility to setup covalent ligands. Molecules can now be imported from different file formats or via a molecular sketcher. The MMFF-based approach has been updated to provide parameters and topologies compatible with the CHARMM36 force field. An option was added to generate small molecule parametrizations following the CHARMM General Force Field via the multipurpose atom-typer for CHARMM (MATCH) approach. Additionally, SwissParam now generates information on probable alternative tautomers and protonation states of the query molecule so that the user can consider all microspecies relevant to its compound. The new version of SwissParam is freely available at www.swissparam.ch and can also be accessed through a newly implemented command-line interface.

Mechanism Analysis and Property Prediction of Extended Surfactants Based on the Respectively Optimized Force Field.

Extended surfactants represent a novel class of anionic-nonionic surfactants with exceptional performance and unique application value in chemically enhanced oil recovery. Although molecular dynamics (MD) simulations can efficiently screen these surfactants, the current research is limited. Here, it is proven for the first time that existing generic force fields (GAFF and CHARMM) cannot accurately describe extended surfactants, and traditional approaches are insufficient for obtaining precise charge parameters. The concept of the respectively optimized force field (ROFF) with the purports of specialization and accuracy is proposed to construct high-accuracy models for MD simulations, and a new approach is developed to simulate the interface model. By combining the newly specialized alkane model, ROFF-based surfactant models, and the innovative simulation protocol, high accuracy and reliability can be obtained in predicting hydration free energies, minimum of area per molecule, and critical micelle concentration of extended surfactants. Key properties of the newly designed extended surfactants in conventional oil-water interfaces and oil reservoir environments are comprehensively predicted by using advanced analytical and characterization methods. Furthermore, the more rigorous mechanism underlying the special amphiphilicity of the extended surfactant is revealed, potentially offering significant improvements over previous empirical perspectives.

Calculations of Absolute Solvation Free Energies with Transformato─Application to the FreeSolv Database Using the CGenFF Force Field.

We recently introduced transformato, an open-source Python package for the automated setup of large-scale calculations of relative solvation and binding free energy differences. Here, we extend the capabilities of transformato to the calculation of absolute solvation free energy differences. After careful validation against the literature results and reference calculations with the PERT module of CHARMM, we used transformato to compute absolute solvation free energies for most molecules in the FreeSolv database (621 out of 642). The force field parameters were obtained with the program cgenff (v2.5.1), which derives missing parameters from the CHARMM general force field (CGenFF v4.6). A long-range correction for the Lennard-Jones interactions was added to all computed solvation free energies. The mean absolute error compared to the experimental data is 1.12 kcal/mol. Our results allow a detailed comparison between the AMBER and CHARMM general force fields and provide a more in-depth understanding of the capabilities and limitations of the CGenFF small molecule parameters.

Theoretical process of rubber nano-coating on calcium silicate hydrate film for durable cementitious materials

The lifetime of building materials is a significant challenge and is strongly related to their surface state and impermeability. This work is dedicated to an in-depth analysis of rubber coating process on Calcium-Silicate-Hydrate (CSH) surface. By means of molecular dynamics simulations and using a combination potential of a general force field (CLAYFF) and the consistent-valence force field (CVFF), it is found that the efficiency of coating with TPI (1,4-trans-Polyisoprene) and CPB (1,4-cis-Polybutadiene) than CPI (1,4-cis-Polyisoprene) and TPB (1,4-trans-Polybutadiene). The obtained surface energy of TPI and CPB coated CSH drastically drops from the range of 698–740 mJ/m2 to 46.3–48.4 mJ/m2. Consequently, the averaged contact angle between water nanodroplet and rubber coated CSH surface is obtained in the range of 92.85° and 98.11°, making hydrophobic the nano-coated CSH surface. The interfacial adhesion analysis between rubber film and CSH surface gives a work of adhesion in the range of 49.42 mJ/m2 to 92.76 mJ/m2, in agreement with experimental measurements.

Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.

Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fields. Currently, molecular mechanical (MM) force fields are mainly utilized in MD simulations because of their low computational cost. Quantum mechanical (QM) calculation has high accuracy, but it is exceedingly time consuming for protein simulations. Machine learning (ML) provides the capability for generating accurate potential at the QM level without increasing much computational effort for specific systems that can be studied at the QM level. However, the construction of general machine learned force fields, needed for broad applications and large and complex systems, is still challenging. Here, general and transferable neural network (NN) force fields based on CHARMM force fields, named CHARMM-NN, are constructed for proteins by training NN models on 27 fragments partitioned from the residue-based systematic molecular fragmentation (rSMF) method. The NN for each fragment is based on atom types and uses new input features that are similar to MM inputs, including bonds, angles, dihedrals, and non-bonded terms, which enhance the compatibility of CHARMM-NN to MM MD and enable the implementation of CHARMM-NN force fields in different MD programs. While the main part of the energy of the protein is based on rSMF and NN, the nonbonded interactions between the fragments and with water are taken from the CHARMM force field through mechanical embedding. The validations of the method for dipeptides on geometric data, relative potential energies, and structural reorganization energies demonstrate that the CHARMM-NN local minima on the potential energy surface are very accurate approximations to QM, showing the success of CHARMM-NN for bonded interactions. However, the MD simulations on peptides and proteins indicate that more accurate methods to represent protein-water interactions in fragments and non-bonded interactions between fragments should be considered in the future improvement of CHARMM-NN, which can increase the accuracy of approximation beyond the current mechanical embedding QM/MM level.