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Force fields form the basis for classical molecular simulations, and their accuracy is crucial for the quality of, for instance, protein-ligand binding simulations in drug discovery. The huge diversity of small-molecule chemistry makes it a challenge to build and parameterize a suitable force field. The Open Force Field Initiative is a combined industry and academic consortium developing a state-of-the-art small-molecule force field. In this report, industry members of the consortium worked together to objectively evaluate the performance of the force fields (referred to here as OpenFF) produced by the initiative on a combined public and proprietary dataset of 19,653 relevant molecules selected from their internal research and compound collections. This evaluation was important because it was completely blind; at most partners, none of the molecules or data were used in force field development or testing prior to this work. We compare the Open Force Field "Sage" version 2.0.0 and "Parsley" version 1.3.0 with GAFF-2.11-AM1BCC, OPLS4, and SMIRNOFF99Frosst. We analyzed force-field-optimized geometries and conformer energies compared to reference quantum mechanical data. We show that OPLS4 performs best, and the latest Open Force Field release shows a clear improvement compared to its predecessors. The performance of established force fields such as GAFF-2.11 was generally worse. While OpenFF researchers were involved in building the benchmarking infrastructure used in this work, benchmarking was done entirely in-house within industrial organizations and the resulting assessment is reported here. This work assesses the force field performance using separate benchmarking steps, external datasets, and involving external research groups. This effort may also be unique in terms of the number of different industrial partners involved, with 10 different companies participating in the benchmark efforts.

The increasing availability of high-quality experimental data and first-principles calculations creates opportunities for developing more accurate empirical force fields for simulation of proteins. We developed the AMBER-FB15 protein force field by building a high-quality quantum chemical data set consisting of comprehensive potential energy scans and employing the ForceBalance software package for parameter optimization. The optimized potential surface allows for more significant thermodynamic fluctuations away from local minima. In validation studies where simulation results are compared to experimental measurements, AMBER-FB15 in combination with the updated TIP3P-FB water model predicts equilibrium properties with equivalent accuracy, and temperature dependent properties with significantly improved accuracy, in comparison with published models. We also discuss the effect of changing the protein force field and water model on the simulation results.

We present a molecular dynamics study of the effect of core chemistry on star polymer structural and kinetic properties. This work serves to validate the choice of a model adamantane core used in previous simulations to represent larger star polymeric systems in an aqueous environment, as well as to explore how the choice of size and core chemistry using a dendrimer or nanogel core may affect these polymeric nanoparticle systems, particularly with respect to thermosensitivity and solvation properties that are relevant for applications in drug loading and delivery.

We use small angle X-ray scattering (SAXS) to characterise a class of star diblock polymers with a nanogel core on which the outer block arms are comprised of random copolymers of temperature sensitive PEGMA with pH sensitive basic (PDMAEMA) and acidic (PMAA) monomers. The acquired SAXS data show that many of the nanogel star polymers undergo a sharp structural transition over a narrow range of pH, but with unexpectedly large shifts in the apparent pKa with respect to that of the acidic or basic monomer unit, the linear polymer form or even an alternate star polymer with a tightly cross-linked core chemistry. We have demonstrated a distinct and quantifiable structural response for the nanogel star copolymers by altering the core or by pairing the monomers PDMAEMA–PEGMA and PMAA–PEGMA to achieve structural transitions that have typically been observed in stars through changes in arm length and number.

We present a molecular simulation study of star polymers consisting of 16 diblock copolymer arms bound to a small adamantane core by varying both arm length and the outer hydrophilic block when attached to the same hydrophobic block of poly-δ-valerolactone. Here we consider two biocompatible star polymers in which the hydrophilic block is composed of polyethylene glycol (PEG) or polymethyloxazoline (POXA) in addition to a polycarbonate-based polymer with a pendant hydrophilic group (PC1). We find that the different hydrophilic blocks of the star polymers show qualitatively different trends in their interactions with aqueous solvent, orientational time correlation functions, and orientational correlation between pairs of monomers of their polymeric arms in solution, in which we find that the PEG polymers are more thermosensitive compared with the POXA and PC1 star polymers over the physiological temperature range we have investigated.

A model was developed for drilling holes with focused Gaussian laser beams taking into consideration the beam divergence near the focus which significantly affects the hole profile. The model was studied and compared with experimental results. It was shown that the model can faithfully simulate experimental results which include the effect of moving the beam waist away from the sample surface and the dependence of the hole profile on beam divergence.

Digitisation offers significant opportunities for the formulated product industry to transform the way it works. Recent developments in the fields of artificial intelligence, machine learning, robotics and high performance computing present disruptive and transformative methods to accelerate the R&D process. These methods include physics‐based chemical simulation, data driven models and hybrid approaches, each of which has been shown to provide new physical and chemical insights that can augment experimental R&D. However, successfully exploiting these technologies in R&D organisations at scale is challenging due to the high level of domain specialisation required and the need for novel models for the formulation domain. In this paper we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. These demonstrators focus on computational modelling methods. In particular we discuss how we tackled the issue of domain specialisation and the challenges we encountered in creating accurate models for formulation problems. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Dynamical averages based on functionals of dynamical trajectories, such as time-correlation functions, play an important role in determining kinetic or transport properties of matter. At temperatures of interest, the expectations of these quantities are often dominated by contributions from rare events, making the precise calculation of these quantities by molecular dynamics simulation difficult. Here, we present a reweighting method for combining simulations from multiple temperatures (or from simulated or parallel tempering simulations) to compute an optimal estimate of the dynamical properties at the temperature of interest without the need to invoke an approximate kinetic model (such as the Arrhenius law). Continuous and differentiable estimates of these expectations at any temperature in the sampled range can also be computed, along with an assessment of the associated statistical uncertainty. For rare events, aggregating data from multiple temperatures can produce an estimate with the desired precision at greatly reduced computational cost compared with simulations conducted at a single temperature. Here, we describe use of the method for the canonical (NVT) ensemble using four common models of dynamics (canonical distribution of Hamiltonian trajectories, Andersen thermostatting, Langevin, and overdamped Langevin or Brownian dynamics), but it can be applied to any thermodynamic ensemble provided the ratio of path probabilities at different temperatures can be computed. To illustrate the method, we compute a time-correlation function for solvated terminally-blocked alanine peptide across a range of temperatures using trajectories harvested using a modified parallel tempering protocol. [ABSTRACT FROM AUTHOR]

Parallel tempering (PT) molecular dynamics simulations have been extensively investigated as a means of efficient sampling of the configurations of biomolecular systems. Recent work has demonstrated how the short physical trajectories generated in PT simulations of biomolecules can be used to construct the Markov models describing biomolecular dynamics at each simulated temperature. While this approach describes the temperature-dependent kinetics, it does not make optimal use of all available PT data, instead estimating the rates at a given temperature using only data from that temperature. This can be problematic, as some relevant transitions or states may not be sufficiently sampled at the temperature of interest, but might be readily sampled at nearby temperatures. Further, the comparison of temperature-dependent properties can suffer from the false assumption that data collected from different temperatures are uncorrelated. We propose here a strategy in which, by a simple modification of the PT protocol, the harvested trajectories can be reweighted, permitting data from all temperatures to contribute to the estimated kinetic model. The method reduces the statistical uncertainty in the kinetic model relative to the single temperature approach and provides estimates of transition probabilities even for transitions not observed at the temperature of interest. Further, the method allows the kinetics to be estimated at temperatures other than those at which simulations were run. We illustrate this method by applying it to the generation of a Markov model of the conformational dynamics of the solvated terminally blocked alanine peptide. [ABSTRACT FROM AUTHOR]

To meet the challenge of modeling the conformational dynamics of biological macromolecules over long time scales, much recent effort has been devoted to constructing stochastic kinetic models, often in the form of discrete-state Markov models, from short molecular dynamics simulations. To construct useful models that faithfully represent dynamics at the time scales of interest, it is necessary to decompose configuration space into a set of kinetically metastable states. Previous attempts to define these states have relied upon either prior knowledge of the slow degrees of freedom or on the application of conformational clustering techniques which assume that conformationally distinct clusters are also kinetically distinct. Here, we present a first version of an automatic algorithm for the discovery of kinetically metastable states that is generally applicable to solvated macromolecules. Given molecular dynamics trajectories initiated from a well-defined starting distribution, the algorithm discovers long lived, kinetically metastable states through successive iterations of partitioning and aggregating conformation space into kinetically related regions. The authors apply this method to three peptides in explicit solvent—terminally blocked alanine, the 21-residue helical Fs peptide, and the engineered 12-residue β-hairpin trpzip2—to assess its ability to generate physically meaningful states and faithful kinetic models. [ABSTRACT FROM AUTHOR]

We have carried out extensive all atom explicit solvent simulations of the high-temperature folding and unfolding of the trpzip2 β-hairpin peptide and examined the resulting trajectories for evidence of folding via a reptation mechanism. Over 300 microcanonical simulations of 10 ns each were initiated from a Boltzmann ensemble of conformations at 425 K. Though we observed numerous folding and unfolding events, no evidence of reptation was found. The diffusional dynamics of the peptide are orders of magnitude faster than any observed reptation-like motion. Our data suggest that the dominant mechanisms for β-hairpin folding under these conditions are hydrophobic collapse and turn formation, and that rearrangements occur via significant expansion of the polypeptide chain. [ABSTRACT FROM AUTHOR]

The liquid-vapor-phase equilibrium properties of the previously developed TIP4P-Ew water model have been studied using thermodynamic integration free-energy simulation techniques in the temperature range of 274–400 K. We stress that free-energy results from simulations need to be corrected in order to be compared to the experiment. This is due to the fact that the thermodynamic end states accessible through simulations correspond to fictitious substances (classical rigid liquids and classical rigid ideal gases) while experiments operate on real substances (liquids and real gases, with quantum effects). After applying analytical corrections the vapor pressure curve obtained from simulated free-energy changes is in excellent agreement with the experimental vapor pressure curve. The boiling point of TIP4P-Ew water under ambient pressure is found to be at 370.3±1.9 K, about 7 K higher than the boiling point of TIP4P water (363.7±5.1 K; from simulations that employ finite range treatment of electrostatic and Lennard-Jones interactions). This is in contrast to the approximately +15 K by which the temperature of the density maximum and the melting temperature of TIP4P-Ew are shifted relative to TIP4P, indicating that the temperature range over which the liquid phase of TIP4P-Ew is stable is narrower than that of TIP4P and resembles more that of real water. The quality of the vapor pressure results highlights the success of TIP4P-Ew in describing the energetic and entropic aspects of intermolecular interactions in liquid water. [ABSTRACT FROM AUTHOR]

A re-parameterization of the standard TIP4P water model for use with Ewald techniques is introduced, providing an overall global improvement in water properties relative to several popular nonpolarizable and polarizable water potentials. Using high precision simulations, and careful application of standard analytical corrections, we show that the new TIP4P-Ew potential has a density maximum at ∼1 °C, and reproduces experimental bulk-densities and the enthalpy of vaporization, DHvap , from -37.5 to 127 °C at 1 atm with an absolute average error of less than 1%. Structural properties are in very good agreement with x-ray scattering intensities at temperatures between 0 and 77 °C and dynamical properties such as self-diffusion coefficient are in excellent agreement with experiment. The parameterization approach used can be easily generalized to rehabilitate any water force field using available experimental data over a range of thermodynamic points. [ABSTRACT FROM AUTHOR]

Quantitative free energy computation involves both using a model that is sufficiently faithful to the experimental system under study (accuracy) and establishing statistically meaningful measures of the uncertainties resulting from finite sampling (precision). We use large-scale distributed computing to access sufficient computational resources to extensively sample molecular systems and thus reduce statistical uncertainty of measured free energies. In order to examine the accuracy of a range of common models used for protein simulation, we calculate the free energy of hydration of 15 amino acid side chain analogs derived from recent versions of the OPLS-AA, CHARMM, and AMBER parameter sets in TIP3P water using thermodynamic integration. We achieve a high degree of statistical precision in our simulations, obtaining uncertainties for the free energy of hydration of 0.02–0.05 kcal/mol, which are in general an order of magnitude smaller than those found in other studies. Notably, this level of precision is comparable to that obtained in experimental hydration free energy measurements of the same molecules. Root mean square differences from experiment over the set of molecules examined using AMBER-, CHARMM-, and OPLS-AA-derived parameters were 1.35 kcal/mol, 1.31 kcal/mol, and 0.85 kcal/mol, respectively. Under the simulation conditions used, these force fields tend to uniformly underestimate solubility of all the side chain analogs. The relative free energies of hydration between amino acid side chain analogs were closer to experiment but still exhibited significant deviations. Although extensive computational resources may be needed for large numbers of molecules, sufficient computational resources to calculate precise free energy calculations for small molecules are accessible to most researchers. © 2003 American Institute of Physics. [ABSTRACT FROM AUTHOR]

A guanidine-based catalyst, first involving acetyl transfer to the catalyst and the second involving only hydrogen bonding to the catalyst was used to study two alternative mechanisms for the ring-opening polymerization of L-lactide. The results showed that the hydrogen bonding pathway is favored over the acetyl transfer pathway.

A coarse-grain molecular dynamics simulations study was carried out to comprehend the undulations and thickness fluctuations of a model block copolymer bilayer. The results from simulation shows that line edge roughness could be reduced by raising the Flory-Huggins parameter or addition of strong specific interactions at block interface.

An approach is developed to support the interpretation of molecular dynamics trajectories toward understanding of peptide folding thermodynamics and kinetics in the context of Markov modeling. The above formula is applied in two example systems, namely, an alanine dipeptide in a vacuum and the beta-hairpin from protein G in water.

We present a method for the calculation of the chemical potential of a liquid or solid using molecular dynamics or Monte Carlo computer simulations of the material. Like several previously presented methods, it is based on the insertion and deletion of particles from the system. It is based on what we call a ‘‘bicanonical ensemble,’’ which is an equilibrium statistical mechanical ensemble characterized by a temperature T and a volume V and in which all systems have either N or N-1 particles. The chemical potential is simply related to the fraction of the systems of the ensemble that have N particles. It is possible to construct algorithms, combining either molecular dynamics motion or Monte Carlo moves with the insertion and deletion of particles, such that the set of states generated by the algorithm constitute a Markov chain whose stationary distribution is that of the bicanonical ensemble. The key to the success of the method is a feature that leads to a high success rate for the insertion and deletion steps, even at high densities, thus giving good statistics for the chemical potential. The method is applied to a two-dimensional liquid of particles with a pairwise additive inverse 12th power repulsive potential, for which it gives accurate results for the chemical potential for the entire equilibrium liquid range of densities. The method is applicable and accurate for the two-dimensional solid of the same particles, and so it can be used in studies of the phase diagram for two-dimensional materials. © 1995 American Institute of Physics. [ABSTRACT FROM AUTHOR]