Literature DB >> 28595415

Computational methodology for solubility prediction: Application to the sparingly soluble solutes.

Lunna Li1, Tim Totton2, Daan Frenkel1.   

Abstract

The solubility of a crystalline substance in the solution can be estimated from its absolute solid free energy and excess solvation free energy. Here, we present a numerical method, which enables convenient solubility estimation of general molecular crystals at arbitrary thermodynamic conditions where solid and solution can coexist. The methodology is based on standard alchemical free energy methods, such as thermodynamic integration and free energy perturbation, and consists of two parts: (1) systematic extension of the Einstein crystal method to calculate the absolute solid free energies of molecular crystals at arbitrary temperatures and pressures and (2) a flexible cavity method that can yield accurate estimates of the excess solvation free energies. As an illustration, via classical Molecular Dynamic simulations, we show that our approach can predict the solubility of OPLS-AA-based (Optimized Potentials for Liquid Simulations All Atomic) naphthalene in SPC (Simple Point Charge) water in good agreement with experimental data at various temperatures and pressures. Because the procedure is simple and general and only makes use of readily available open-source software, the methodology should provide a powerful tool for universal solubility prediction.

Entities:  

Year:  2017        PMID: 28595415     DOI: 10.1063/1.4983754

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  10 in total

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Authors:  Gihan Panapitiya; Michael Girard; Aaron Hollas; Jonathan Sepulveda; Vijayakumar Murugesan; Wei Wang; Emily Saldanha
Journal:  ACS Omega       Date:  2022-04-25

3.  Machine learning with physicochemical relationships: solubility prediction in organic solvents and water.

Authors:  Samuel Boobier; David R J Hose; A John Blacker; Bao N Nguyen
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5.  Computational and experimental investigation of the effect of cation structure on the solubility of anionic flow battery active-materials.

Authors:  Benjoe Rey B Visayas; Shyam K Pahari; Tugba Ceren Gokoglan; James A Golen; Ertan Agar; Patrick J Cappillino; Maricris L Mayes
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6.  Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks.

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Journal:  ACS Omega       Date:  2022-04-04

7.  Challenges in the use of atomistic simulations to predict solubilities of drug-like molecules.

Authors:  Guilherme Duarte Ramos Matos; David L Mobley
Journal:  F1000Res       Date:  2018-05-31

8.  Molecular Simulation of Chemical Reaction Equilibrium by Computationally Efficient Free Energy Minimization.

Authors:  William R Smith; Weikai Qi
Journal:  ACS Cent Sci       Date:  2018-08-23       Impact factor: 14.553

Review 9.  Calculation Methods of Solution Chemical Potential and Application in Emulsion Microencapsulation.

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Journal:  Molecules       Date:  2021-05-18       Impact factor: 4.411

Review 10.  From adaptive resolution to molecular dynamics of open systems.

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Journal:  Eur Phys J B       Date:  2021-09-23       Impact factor: 1.500

  10 in total

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