Theoretically nanoscale study on ionization of muscimol nano drug in aqueous solution

In the present work, acid dissociation constant (pKa) values of muscimol derivatives were calculated using the Density Functional Theory (DFT) method. In this regard, free energy values of neutral, protonated and deprotonated species of muscimol were calculated in water at the B3LYP/6-31G(d) basis sets. The hydrogen bond formation of all species had been analyzed using the Tomasi's method. It was revealed that the theoretically calculated pKa values were in a good agreement with the existing experimental pKa values, which were determined from capillary electrophoresis, potentiometric titration and UV-visible spectrophotometric measurements.No presente trabalho, calculou-se a constante de dissociação do ácido (pKa) dos derivados de muscimol, utilizando-se o método da teoria do funcional de densidade (DFT). Com esse objetivo, calcularam-se os valores das espécies neutra, protonada e desprotonada do muscimol em água em base B3LYP/6-31G(d). A formação da ligação de hidrogênio de todas as espécies foi analisada utilizando o método de Tomasi. Demonstrou-se que os valores de pKa calculados teoricamente estavam em boa concordância com os valores experimentais disponíveis, determinados por eletroforese capilar, titulação potenciométrica e medidas por espectrofotometria UV-visível

Identifying optimal solvents for reactions using quantum mechanics and computer-aided molecular design

A new iterative hybrid methodology, incorporating quantum mechanics (QM) calculations and a computationally inexpensive computer-aided molecular design (CAMD) methodology, QM-CAMD, for identification of optimal solvents for reactions is presented. The methodology has been applied to a Menschutkin reaction, where pyridine and phenacyl bromide are the reactants. The QM calculations take on the form of density functional theory calculations with a given solvent treated using continuum solvation models. The accuracy of the solvent QM calculations is assessed by computing free energies of solvation for different solvation models; the IEF-PCM, SM8 and SMD models are studied and SMD is identified as the best model. Rate constants kQM, determined from QM calculations, are calculated based on conventional transition state theory (Eyring 1935, Evans & Polanyi 1935). By using the SMD solvation model and a statistical mechanics derivation of kQM, rate constant predictions within an order of magnitude are achieved. For a small set of solvents investigated by QM, selected solvent properties are predicted using group contribution (GC) methods. 38 structural groups are considered in this approach. The QM-computed rate constants and solvent properties determined by GC are used to obtain a computationally inexpensive reaction model, based on an empirical linear free energy relationship, which is used to predict reaction rate constants. This predictive reaction model is incorporated into an optimisation-based CAMD methodology. With an objective function of maximising the reaction rate constant subject to molecular and reaction condition constraints, optimal solvent candidates are identified. By considering a design space of over 1000 solvent molecules, solvent candidates containing nitro-groups are predicted to be optimal for the Menschutkin reaction. This outcome supports experimental results for a related reaction available in the literature (Lassau & Jungers 1968). For verification purposes, Ganase et al. (2011) have measured (based on 1H NMR data and kinetic analysis) the rate constant for the reaction of interest in a number of solvents and report a significant increase in the rate constant with nitromethane as the solvent

Modelling the free energy of solvation: from data-driven to statistical mechanical approaches

The Gibbs free energy of solvation for a given solute in a solvent, usually considered at infinite dilution, provides a simple thermodynamic description of the solution and is related to numerous solvation properties. In the context of solution chemistry, it provides a route to understanding the effect of solvents on equilibrium constants and reaction rates. In the discovery of new drugs, the effectiveness of a drug depends in part on solubility and permeability, leading to the prediction of Gibbs free energy of solvation values to be used frequently in quantitative drug design. Given the importance of the Gibbs free energy of solvation, many predictive tools were developed, spanning quantum mechanical (QM) methods, empirical methods, and classical methods. Of note, empirical methods are data-driven approaches through statistical learning. In this work, we assembled a database of experimental Gibbs free energies of solvation and a corresponding set of 9 quantum mechanical (QM) solute descriptors and 12 bulk solvent descriptors. We also partitioned the Gibbs free energy of solvation into an electrostatic term and a nonelectrostatic term. The electrostatic term is the difference between the electronic energies of a solute in a vacuum and solvent obtained though using the X3LYP/6-31 G(d,p) electronic structure method and the Polarizable Continuum Model (PCM). We then obtain a separate database of derived nonelectrostatic energies alongside the Gibbs free energy of solvation database which are used to develop models using statistical and regression methodologies such as partial least squares (PLS), quadratic partial least squares (QPLS) and automatic learning of algebraic models for optimisation (ALAMO). We then carry out a systematic comparison of various activity coefficients, data-driven models, an equation of state, and a hybrid QM/activity coefficient model. Notable models include the Dortmund version of UNIFAC model (modUNIFAC (Do)), the statistical associating fluid theory (SAFT- γ Mie), and the conductor-like screening model segmented activity coefficients (COSMO-SAC). We carry out calculations for the free energy of solvation on a common data set of 404 solute/solvent pairs with examples such as alcohols, alkanes, and aromatic molecules as solutes and alkanes, alcohols and water as solvents. We also assess the strengths and weaknesses of each method based on the overall data set and for specific subsets of solute/solvent pairs (e.g., aqueous/nonaqueous pairs.)Open Acces