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 |
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| Hlavní autori: | , , , , , , , , , , |
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01.11.2024
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| ISSN: | 0935-9648, 1521-4095, 1521-4095 |
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| Abstract | 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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| AbstractList | 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 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. 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 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. |
| Author | Zheng, Jiajia Gao, Xingfa Gao, Xuejiao J. Ciura, Krzesimir Zhong, Shengliang Wan, Yuxin Ma, Yuanjie Mikolajczyk, Alicja Jagiello, Karolina Gao, Yurou Puzyn, Tomasz |
| Author_xml | – sequence: 1 givenname: Xuejiao J. surname: Gao fullname: Gao, Xuejiao J. organization: Jiangxi Normal University – sequence: 2 givenname: Krzesimir surname: Ciura fullname: Ciura, Krzesimir organization: Medical University of Gdansk – sequence: 3 givenname: Yuanjie surname: Ma fullname: Ma, Yuanjie organization: Jiangxi Normal University – sequence: 4 givenname: Alicja surname: Mikolajczyk fullname: Mikolajczyk, Alicja organization: University of Gdansk – sequence: 5 givenname: Karolina surname: Jagiello fullname: Jagiello, Karolina organization: University of Gdansk – sequence: 6 givenname: Yuxin surname: Wan fullname: Wan, Yuxin organization: Jiangxi Normal University – sequence: 7 givenname: Yurou surname: Gao fullname: Gao, Yurou organization: University of Chinese Academy of Sciences – sequence: 8 givenname: Jiajia surname: Zheng fullname: Zheng, Jiajia organization: National Center for Nanoscience and Technology of China – sequence: 9 givenname: Shengliang surname: Zhong fullname: Zhong, Shengliang email: slzhong@jxnu.edu.cn organization: Jiangxi Normal University – sequence: 10 givenname: Tomasz surname: Puzyn fullname: Puzyn, Tomasz email: tomasz.puzyn@ug.edu.pl organization: University of Gdansk – sequence: 11 givenname: Xingfa orcidid: 0000-0002-1636-6336 surname: Gao fullname: Gao, Xingfa email: gaoxf@nanoctr.cn organization: National Center for Nanoscience and Technology of China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39252670$$D View this record in MEDLINE/PubMed |
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| Snippet | The pioneering work on liposomes in the 1960s and subsequent research in controlled drug release systems significantly advances the development of nanocarriers... |
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| SubjectTerms | 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 |
| Title | Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers |
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