Decoding Nanomaterial‐Biosystem Interactions through Machine Learning

Nanomaterial-biosystem interactions are highly complex and influenced by numerous entangled factors. The emergence of machine learning has provided a timely and unique opportunity to revisit these interactions. This minireview highlights the development and use of machine learning to decode the interactions of nanomaterials with biosystems and provides some perspectives on the current challenges and potential opportunities in this field.

Abstract

The interactions between biosystems and nanomaterials regulate most of their theranostic and nanomedicine applications. These nanomaterial-biosystem interactions are highly complex and influenced by a number of entangled factors, including but not limited to the physicochemical features of nanomaterials, the types and characteristics of the interacting biosystems, and the properties of the surrounding microenvironments. Over the years, different experimental approaches coupled with computational modeling have revealed important insights into these interactions, although many outstanding questions remain unanswered. The emergence of machine learning has provided a timely and unique opportunity to revisit nanomaterial-biosystem interactions and to further push the boundary of this field. This minireview highlights the development and use of machine learning to decode nanomaterial-biosystem interactions and provides our perspectives on the current challenges and potential opportunities in this field.

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