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

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, dendri...

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Vydané v:Advanced materials (Weinheim) Ročník 36; číslo 45; s. e2407793 - n/a
Hlavní autori: Gao, Xuejiao J., Ciura, Krzesimir, Ma, Yuanjie, Mikolajczyk, Alicja, Jagiello, Karolina, Wan, Yuxin, Gao, Yurou, Zheng, Jiajia, Zhong, Shengliang, Puzyn, Tomasz, Gao, Xingfa
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Germany Wiley Subscription Services, Inc 01.11.2024
Predmet:
Biological effects
Biological models (mathematics)
Chemical reactions
data‐driven models
Dendrimers
Drug carriers
Drug Carriers - chemistry
Drug Delivery Systems
Drug Design
Explainable artificial intelligence
Humans
Liposomes
Machine Learning
Models, Molecular
molecular modeling
nanocarriers
Nanoparticles - chemistry
physics‐based models
data‐driven models
machine learning
molecular modeling
nanocarriers
physics‐based models
ISSN:0935-9648, 1521-4095, 1521-4095
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Shrnutí: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. The integration of physics‐based models from traditional simulation methods with data‐driven models from artificial intelligence and data analytics paves the way for the rational design of nanocarriers for drug delivery.
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ISSN:0935-9648
1521-4095
1521-4095
DOI:10.1002/adma.202407793