Decoding Nanomaterial-Biosystem Interactions through Machine Learning.

Journal: Angewandte Chemie (International ed. in English)
PMID:

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.

Authors

  • Sagar Dhoble
    Department of Pharmacology and Toxicology, R. Ken Coit College of Pharmacy, University of Arizona, Tucson, AZ 85721, USA.
  • Tzu-Hsien Wu
    Department of Pharmacology and Toxicology, R. Ken Coit College of Pharmacy, University of Arizona, Tucson, AZ 85721, USA.
  • Kenry
    Department of Chemical and Biomolecular Engineering, National University of Singapore , 4 Engineering Drive 4, Singapore 117585.