Department of Pharmacology and Toxicology, R. Ken Coit College of Pharmacy, University of Arizona, Tucson, AZ 85721, USA.
University of Arizona Cancer Center, University of Arizona, Tucson, AZ 85721, USA.
Angew Chem Int Ed Engl. 2024 Apr 15;63(16):e202318380. doi: 10.1002/anie.202318380. Epub 2024 Feb 12.
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.
生物系统和纳米材料之间的相互作用调节了它们在治疗和纳米医学中的大多数应用。这些纳米材料-生物系统的相互作用非常复杂,并受到许多纠缠因素的影响,包括但不限于纳米材料的物理化学特性、相互作用的生物系统的类型和特征,以及周围微环境的性质。多年来,不同的实验方法结合计算建模已经揭示了这些相互作用的重要见解,尽管仍有许多悬而未决的问题。机器学习的出现为重新审视纳米材料-生物系统的相互作用提供了一个及时而独特的机会,并进一步推动了这一领域的发展。这篇综述强调了机器学习在解码纳米材料-生物系统相互作用中的发展和应用,并提供了我们对该领域当前挑战和潜在机遇的看法。