Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers.

Autor: Gao XJ; Jiangxi Province Key Laboratory of Porous Functional Materials, College of Chemistry and Materials, Jiangxi Normal University, Nanchang, 330022, P. R. China., Ciura K; Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk, 80-308, Poland.; Department of Physical Chemistry, Medical University of Gdansk, Al. Gen. Hallera 107, Gdansk, 80-416, Poland., Ma Y; Jiangxi Province Key Laboratory of Porous Functional Materials, College of Chemistry and Materials, Jiangxi Normal University, Nanchang, 330022, P. R. China., Mikolajczyk A; Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk, 80-308, Poland., Jagiello K; Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk, 80-308, Poland., Wan Y; Jiangxi Province Key Laboratory of Porous Functional Materials, College of Chemistry and Materials, Jiangxi Normal University, Nanchang, 330022, P. R. China., Gao Y; Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100049, China., Zheng J; Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing, 100190, P. R. China., Zhong S; Jiangxi Province Key Laboratory of Porous Functional Materials, College of Chemistry and Materials, Jiangxi Normal University, Nanchang, 330022, P. R. China., Puzyn T; Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk, 80-308, Poland., Gao X; Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing, 100190, P. R. China.
Jazyk: angličtina
Zdroj: Advanced materials (Deerfield Beach, Fla.) [Adv Mater] 2024 Nov; Vol. 36 (45), pp. e2407793. Date of Electronic Publication: 2024 Sep 10.
DOI: 10.1002/adma.202407793
Abstrakt: The pioneering work on liposomes in the 1960s and subsequent research in controlled drug release systems significantly advances the development of nanocarriers (NCs) for drug delivery. This field is evolved to include a diverse array of nanocarriers such as liposomes, polymeric nanoparticles, dendrimers, and more, each tailored to specific therapeutic applications. Despite significant achievements, the clinical translation of nanocarriers is limited, primarily due to the low efficiency of drug delivery and an incomplete understanding of nanocarrier interactions with biological systems. Addressing these challenges requires interdisciplinary collaboration and a deep understanding of the nano-bio interface. To enhance nanocarrier design, scientists employ both physics-based and data-driven models. Physics-based models provide detailed insights into chemical reactions and interactions at atomic and molecular scales, while data-driven models leverage machine learning to analyze large datasets and uncover hidden mechanisms. The integration of these models presents challenges such as harmonizing different modeling approaches and ensuring model validation and generalization across biological systems. However, this integration is crucial for developing effective and targeted nanocarrier systems. By integrating these approaches with enhanced data infrastructure, explainable AI, computational advances, and machine learning potentials, researchers can develop innovative nanomedicine solutions, ultimately improving therapeutic outcomes.
(© 2024 Wiley‐VCH GmbH.)
Databáze: MEDLINE
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