Learning the relationship between nanoscale chemical patterning and hydrophobicity.

ABSTRACT: The hydrophobicity of proteins and similar surfaces, which display chemical heterogeneity at the nanoscale, drives countless aqueous interactions and assemblies. However, predicting how surface chemical patterning influences hydrophobicity remains a challenge. Here, we address this challenge by using molecular simulations and machine learning to characterize and model the hydrophobicity of a diverse library of patterned surfaces, spanning a wide range of sizes, shapes, and chemical compositions. We find that simple models, based only on polar content, are inaccurate, whereas complex neural network models are accurate but challenging to interpret. However, by systematically incorporating chemical correlations between surface groups into our models, we are able to construct a series of minim

SUBMITTER: Rego NB 

PROVIDER: S-EPMC9860318 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature