Literature DB >> 30070281

Solubility prediction from first principles: a density of states approach.

Simon Boothroyd1, Andy Kerridge, Anders Broo, David Buttar, Jamshed Anwar.   

Abstract

Solubility is a fundamental property of widespread significance. Despite its importance, its efficient and accurate prediction from first principles remains a major challenge. Here we propose a novel method to predict the solubility of molecules using a density of states (DOS) approach from classical molecular simulation. The method offers a potential route to solubility prediction for large (including drug-like) molecules over a range of temperatures and pressures, all from a modest number of simulations. The method was employed to predict the solubility of sodium chloride in water at ambient conditions, yielding a value of 3.77(5) mol kg-1. This is in close agreement with other approaches based on molecular simulation, the consensus literature value being 3.71(25) mol kg-1. The predicted solubility is about half of the experimental value, the disparity being attributed to the known limitation of the Joung-Cheatham force field model employed for NaCl. The proposed method also accurately predicted the NaCl model's solubility over the temperature range 298-373 K directly from the density of states data used to predict the ambient solubility.

Entities:  

Year:  2018        PMID: 30070281     DOI: 10.1039/c8cp01786g

Source DB:  PubMed          Journal:  Phys Chem Chem Phys        ISSN: 1463-9076            Impact factor:   3.676


  4 in total

1.  Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN.

Authors:  Xianfang Wang; Yifeng Liu; Zhiyong Du; Mingdong Zhu; Aman Chandra Kaushik; Xue Jiang; Dongqing Wei
Journal:  Interdiscip Sci       Date:  2021-07-08       Impact factor: 2.233

2.  SolTranNet-A Machine Learning Tool for Fast Aqueous Solubility Prediction.

Authors:  Paul G Francoeur; David R Koes
Journal:  J Chem Inf Model       Date:  2021-05-26       Impact factor: 6.162

3.  Optimizing peptide inhibitors of SARS-Cov-2 nsp10/nsp16 methyltransferase predicted through molecular simulation and machine learning.

Authors:  John R Hamre; M Saleet Jafri
Journal:  Inform Med Unlocked       Date:  2022-02-28

4.  Three machine learning models for the 2019 Solubility Challenge.

Authors:  John B O Mitchell
Journal:  ADMET DMPK       Date:  2020-06-15
  4 in total

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