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Cryogenic Solutions as a Tool to Characterize Red-and Blue-Shifting C–H···X Hydrogen Bonding
Published in Leonid Khriachtchev, Physics and Chemistry at Low Temperatures, 2019
Wouter A. Herrebout, Benjamin J. van der Veken
Although solutions in liquefied rare gases have been described as a pseudogas phase,73–75 it is now generally accepted that in the cryosolutions significant solute-solvent interactions can occur, and that these interactions can influence the relative stability of the complexes studied. The ab initio complexation energies and the vapor phase complexation enthalpies therefore cannot directly be compared with the experimental values derived from cryosolutions. To estimate the solvent effects, and to predict the complexation enthalpies in the cryosolutions, for each species involved, the Gibbs energy and the enthalpy of solvation are derived using Monte Carlo free energy perturbation (MC-FEP) calculations.76–78 In these simulations, the solute–solvent interactions are described using a Lennard–Jones function between each solvent atom and each atom of the solute molecule, and an additional term that accounts for the polarization of the solvent atoms by the solute. The latter is calculated in a noniterative first-order approximation as79,80 (
Cloud Application in Drug Development
Published in Rishabha Malviya, Pramod Kumar Sharma, Sonali Sundram, Rajesh Kumar Dhanaraj, Balamurugan Balusamy, Bioinformatics Tools and Big Data Analytics for Patient Care, 2023
Nitu Singh, Urvashi Sharma, Deepika Bairagee, Neelam Jain
Quantum Molecular Design, in collaboration with Genomeon, designs medication candidates and identifies companion biomarkers using molecular modelling in the cloud. In three ways, Quantum Molecular Design outperforms traditional methods. For starters, it provides a fresh method for discovering new compounds. Using a proprietary AI/“big data” technique, it detects compounds that are inferred directly from the properties of the binding site of the biological target. Second, it can predict the binding affinity of a protein to a tiny chemical with a high degree of accuracy (or peptide). The technique involves one or more of the following methods, with parametric editions: quantum mechanics/molecular mechanics (QM/MM), molecular dynamics (MD), linear interaction energy (LIE), and/or free energy perturbation (FEP). Correlations of 0.7–0.9 have been discovered on a regular basis [39,40]. It specifically depicts the protein, molecule, and aqueous environment in atomic detail. It includes a high sampling rate and properly portrays the role of water in solvation and the active site of proteins. It takes measurements of the conformation of the protein binding pocket, as well as the confirmation of tiny molecules and water molecules, and precisely envisages how they will interact. Third, it examines molecules for chemical features that are useful in the development of pharmaceuticals. Only molecules that pass all of the filters are found in the final results. You can screen compounds on the basis of their solubility, synthetic tractability, or whether or not they penetrate the blood–brain barrier, for example. Some designs may need to be filtered due to molecular weight limits. Or, for biological reasons, there can be side chains that are uneven or undesirable that can be filtered out of specific designs. Quantum Molecular Design’s property-filtering methods help to produce only “excellent” molecules, reducing the medicinal chemist’s effort during optimization.
Towards sustainable micro-pollutants’ removal from wastewaters: caffeine solubility, self-diffusion and adsorption studies from aqueous solutions into hydrochars
Published in Molecular Physics, 2018
S. Román, B. Ledesma, A. Álvarez, C. Herdes
Currently, the estimation of self-diffusion coefficients and free energies using molecular simulation techniques has attracted much interest in areas such as drug design and material science – it is worth noticing that this work solely refers to the diffusion of CAF as its self-diffusion coefficient. Given an appropriate description of the molecular interactions (i.e. the Hamiltonian), the diffusion of selected species can be calculated from molecular dynamics (MD) simulations by tracking the mean square displacement of such compounds as a function of time, without the limiting constraint of infinite dilution of experimental systems. Common free energy types include the solvation, transfer, binding and conformational free energy. The ability to calculate accurate estimates of the free energy from molecular simulations overcomes the difficult experimental measurement of these relevant thermodynamic properties of a system. Conversely, to obtain a reliable estimate of the free energy of a system from molecular simulations, some challenges must be met [15,16]. The most common methods to estimate free energy are thermodynamic integration, free energy perturbation, umbrella sampling and potential of mean force [15,16].
Mesoscale simulation of aggregation of imogolite nanotubes from potential of mean force interactions
Published in Molecular Physics, 2019
Hejian Zhu, Andrew J. Whittle, Roland J.-M. Pellenq, Katerina Ioannidou
After initial setup, the system was let to evolve in a canonical ensemble (NVT) under constant temperature 300 K with fixed volume using Molecular Dynamic (MD) simulation with LAMMPS [12]. Temperature control was achieved through the Nosé–Hoover thermostat [13,14]. Interactions between atoms were defined by the CLAYFF force field [15], which has been previously used for molecular simulations of clay minerals [7,16]. In this model, the potential energy contains four parts: harmonic bond and angle terms for bonded atoms, and van der Waals term of the Lennard–Jones (LJ) type and long-range Coulombic electrostatic term for non-bonded atoms (see Appendix 1). Short-range (LJ) terms were calculated with a potential cut-off of 8.5 Å; while long-range Coulombic terms were calculated with the particle–particle–particle–mesh (PPPM) Ewald summation method [17,18]. Timestep was set to 1 fs (). The system was allowed to relax for 500 ps followed by a 2500 ps production period. We recorded 501 configuration, at time intervals of 5 ps, during the production stage for each spacing , for the calculation of the potential of mean force. In the NVT simulations, the imogolite tubes were held fixed and rigid. Bond length and angle of water molecules were constrained with the SHAKE algorithm [19]. The centre-to-centre distance ranged from 25 to 60 Å, with 0.25 Å increment. The potential of mean force (PMF), corresponding to the Gibbs free energy, between the two tubes was calculated through a free energy perturbation. At each separation, the configurations were perturbed towards Å and to Å . Differences in potential energy of the perturbed and original states were calculated. The free energy differences between adjacent states were calculated through the following equations [20–22]: where is the starting state, is the perturbed state, ( is the Boltzmann constant and T is the temperature), and . The average was taken over the 501 configurations mentioned above.