Data-Driven Prediction of Protein Adsorption on Self-Assembled Monolayers toward Material Screening and Design.

We attempt to predict the water contact angle (WCA) of self-assembled monolayers (SAMs) and protein adsorption on the SAMs from the chemical structures of molecules constituting the SAMs using machine learning with an artificial neural network (ANN) model. After training the ANN with data of 145 SAMs, the ANN became capable of predicting the WCA and protein adsorption accurately. The analysis of the trained ANN quantitatively revealed the importance of each structural parameter for the WCA and protein adsorption, providing essential and quantitative information for material design.