AsianScientist (Oct. 20, 2020) – Researchers at Tokyo Institute of Technology (Tokyo Tech) have used artificial intelligence (AI) to predict the properties of hypothetical organic materials, publishing their results in ACS Biomaterials Science and Engineering.
The ability to accurately predict the degree of water repulsion and protein adsorption could be used to design materials for artificial blood vessels, opening up new possibilities for the screening and design of organic materials with desired functions.
Using informatics in the field of inorganic material design has led to the rise of new types of catalysts, batteries and semiconductors. In contrast, informatics-based design of biomaterials (i.e. organic as opposed to inorganic solid-state materials) is only just beginning to be explored.
Now, a team of researchers at Tokyo Tech led by Associate Professor Tomohiro Hayashi has used machine learning with an artificial neural network (ANN) model to predict two key properties of ultra-thin organic materials known as self-assembled monolayers (SAMs). SAMs have been widely used to create model organic surfaces to explore the interaction between proteins and materials due to their ease of preparation and versatility.
By training the ANN using a literature-based database of 145 SAMs, the ANN became capable of predicting water repulsion (measured in terms of the degree of water contact angle) and protein adsorption accurately. The team went on to demonstrate the prediction of water repulsion and protein adsorption even for hypothetical SAMs. These two properties are of enormous interest to biomedical engineers.
“For example, implant materials that exhibit low water contact angle enable fast integration with the surrounding hard tissues,” Hayashi said. “In the case of artificial blood vessels, the resistance to the adsorption of blood proteins, in particular fibrinogen, is a critical factor to prevent platelet adhesion and blood clotting.”
Overall, the study opens the door to advanced material screening and design of SAMs with potentially greatly reduced costs and time scales. The researchers plan to continue scaling up their database and expand their approach to include polymers, ceramics and metals.
The article can be found at: Kwaria et al. (2020) Data-driven Prediction of Protein Adsorption on Self-assembled Monolayers Toward Material Screening and Design.
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Source: Tokyo Institute of Technology; Photo: Shutterstock.
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