Machine learning-guided high throughput nanoparticle design-Reference-Cited by-同舟云学术

Machine learning-guided high throughput nanoparticle design

Published:2024 Issue:7 Volume:3 Page:1280-1291
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Ortiz-Perez Ana¹^ORCID,van Tilborg Derek¹²^ORCID,van der Meel Roy¹^ORCID,Grisoni Francesca¹²^ORCID,Albertazzi Lorenzo¹^ORCID

Affiliation:

1. Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands

2. Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Princetonlaan 6, 3584 CB, Utrecht, The Netherlands

Abstract

Designing nanoparticles with desired properties is challenging due to the large combinatorial space and complex structure–function relationships. This process can be accelerated by combining microfluidics, high content imaging and active learning.

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D4DD00104D

Reference49 articles.

1. Diverse Applications of Nanomedicine

2. Advanced Optical Imaging-Guided Nanotheranostics towards Personalized Cancer Drug Delivery

3. Emerging vaccine nanotechnology: From defense against infection to sniping cancer

4. Cancer nanomedicine for combination cancer immunotherapy

5. Engineering precision nanoparticles for drug delivery

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Self-Driving Laboratories for Chemistry and Materials Science;Chemical Reviews;2024-08-13

2. Leveraging machine learning to streamline the development of liposomal drug delivery systems;2024-07-04