Cited by Endocrine disruptor identification and multitoxicity level assessment of organic chemicals: an example of multiple machine learning models

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

Xuejiao J. Gao,

Krzesimir Ciura,

Yuanjie Ma

et al.

Advanced Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 10, 2024

Abstract The pioneering work on liposomes in the 1960s and subsequent research controlled drug release systems significantly advances development of nanocarriers (NCs) for delivery. This field is evolved to include a diverse array such as liposomes, polymeric nanoparticles, dendrimers, more, each tailored specific therapeutic applications. Despite significant achievements, clinical translation limited, primarily due low efficiency delivery an incomplete understanding nanocarrier interactions with biological systems. Addressing these challenges requires interdisciplinary collaboration deep nano‐bio interface. To enhance design, scientists employ both physics‐based data‐driven models. Physics‐based models provide detailed insights into chemical reactions at atomic molecular scales, while leverage machine learning analyze large datasets uncover hidden mechanisms. integration presents harmonizing different modeling approaches ensuring model validation generalization across However, this crucial developing effective targeted By integrating enhanced data infrastructure, explainable AI, computational advances, potentials, researchers can develop innovative nanomedicine solutions, ultimately improving outcomes.

Language: Английский

Citations

Advances in metal-organic framework-based drug delivery systems DOI

M T Khulood,

U. S. Jijith,

Punnoth Poonkuzhi Naseef

et al.

International Journal of Pharmaceutics, Journal Year: 2025, Volume and Issue: unknown, P. 125380 - 125380

Published: Feb. 1, 2025

Language: Английский

Citations

Application of nanoscale metal-organic coordination polymers in cancer therapy DOI

Hongli Yu, Zhipeng Li, Wenyu Wang

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 496, P. 154342 - 154342

Published: July 27, 2024

Language: Английский

Citations

Evaluating the Drug Delivery Capacity of 3D Coordination Polymer for Anticancer Drugs DOI

Madiha Saqlain,

Hafiz Muhammad Zohaib,

Maroof Ahmad Khan

et al.

Chemistry - An Asian Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 4, 2025

Abstract We synthesized {[Cd 2 (dTMP) (4,4'‐azpy) (H O) ] ⋅ 3(O)} n a novel three‐dimensional metal nucleotide coordination polymer (CP‐1). An assessment of the CP‐1 binding affinity for anticancer drugs was conducted using molecular dynamic simulations. The virtual screening results depict that has lot potential encapsulating anthracycline drug doxorubicin (DOX). It hasn′t yet been investigated how to accomplish high loading capacity, efficiency, and controlled release DOX in dTMP‐based 3D polymers. Utilizing as model our system drug‐loading vehicle, we used UV‐visible circular dichroism titrations examine effects its encapsulation release. mechanism through pH‐responsive behavior by adjusting pH value 8, 7, 6, 5. indicate robust at which facilitates on porous polymer. However, maximum cumulative 87.11 % observed higher correlation coefficient (R ) obtained 5 with Higuchi equation. indicated released primarily diffusion mechanism. polymer‘s ability encapsulate while also permitting possible controlled‐release is confirmed combined insights from experimental findings, energy graphs, RMSD analysis, radius gyration (Rg) data MD

Language: Английский

Citations

Endocrine disruptor identification and multitoxicity level assessment of organic chemicals: an example of multiple machine learning models DOI

Ning Hao, Yuanyuan Zhao,

Peixuan Sun

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 485, P. 136896 - 136896

Published: Dec. 15, 2024

Language: Английский

Citations