Application of molecular dynamics simulations in molecular property prediction II: Diffusion coefficient

Junmei Wang, Tingjun Hou

Research output: Contribution to journalArticlepeer-review

116 Scopus citations


In this work, we have evaluated how well the general assisted model building with energy refinement (AMBER) force field performs in studying the dynamic properties of liquids. Diffusion coefficients (D) have been predicted for 17 solvents, five organic compounds in aqueous solutions, four proteins in aqueous solutions, and nine organic compounds in nonaqueous solutions. An efficient sampling strategy has been proposed and tested in the calculation of the diffusion coefficients of solutes in solutions. There are two major findings of this study. First of all, the diffusion coefficients of organic solutes in aqueous solution can be well predicted: the average unsigned errors and the root mean square errors are 0.137 and 0.171 × 10 -5 cm -2 s -1, respectively. Second, although the absolute values of D cannot be predicted, good correlations have been achieved for eight organic solvents with experimental data (R 2 = 0.784), four proteins in aqueous solutions (R 2 = 0.996), and nine organic compounds in nonaqueous solutions (R 2 = 0.834). The temperature dependent behaviors of three solvents, namely, TIP3P water, dimethyl sulfoxide, and cyclohexane have been studied. The major molecular dynamics (MD) settings, such as the sizes of simulation boxes and with/without wrapping the coordinates of MD snapshots into the primary simulation boxes have been explored. We have concluded that our sampling strategy that averaging the mean square displacement collected in multiple short-MD simulations is efficient in predicting diffusion coefficients of solutes at infinite dilution.

Original languageEnglish (US)
Pages (from-to)3505-3519
Number of pages15
JournalJournal of Computational Chemistry
Issue number16
StatePublished - Dec 2011


  • MD simulations
  • diffusion coefficient
  • general AMBER force field (GAFF)
  • molecular property prediction

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Mathematics


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