Accurately predicting transport properties like diffusion, viscosity, and conductivity is critical for biomedical applications, from drug delivery to electrolyte design.
This article explores the cutting-edge integration of diffusion models with symbolic regression (SR) for predictive modeling in biomedical research.
Nonlinearities in Mean Squared Displacement (MSD) plots present a significant challenge in molecular dynamics simulations, potentially leading to inaccurate diffusion coefficients and misinterpretation of molecular behavior.
This article provides a critical evaluation of the General AMBER Force Field (GAFF) for predicting diffusion coefficients and other transport properties in biomolecular simulations.
This article provides a comprehensive guide for researchers and scientists on the critical process of validating molecular dynamics (MD) simulations against experimental diffusion data.
This article provides a comprehensive guide for researchers and drug development professionals on the accurate estimation and statistical analysis of diffusion coefficients.
Molecular dynamics simulations are powerful tools for predicting diffusion coefficients crucial for drug development, but results are skewed by finite-size effects where computed diffusivities artificially depend on simulated system size.
This article provides researchers, scientists, and drug development professionals with a comprehensive framework for selecting, applying, and validating molecular force fields to achieve accurate predictions of transport properties.
This article addresses the critical challenge of poor sampling of solute molecules in solution, a major bottleneck in drug development and materials science.
Accurately calculating diffusion coefficients with molecular dynamics is crucial for predicting molecular transport in drug delivery and pharmaceutical development.