Polymer design <i>via</i> SHAP and Bayesian machine learning optimizes pDNA and CRISPR ribonucleoprotein delivery-Reference-Cited by-同舟云学术

Polymer design via SHAP and Bayesian machine learning optimizes pDNA and CRISPR ribonucleoprotein delivery

Published:2024 Issue:19 Volume:15 Page:7219-7228
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Dalal Rishad J.¹,Oviedo Felipe²,Leyden Michael C.³,Reineke Theresa M.¹^ORCID

Affiliation:

1. Department of Chemistry, University of Minnesota, Minneapolis, Minnesota 55455, USA

2. Nanite Inc., Boston, Massachusetts 02109, USA

3. Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, USA

Abstract

We present the facile synthesis of a clickable polymer library with systematic variations in length, binary composition, pKa, and hydrophobicity (clog P) to optimize intracellular pDNA and CRISPR-Cas9 ribonucleoprotein (RNP) performance.

Funder

University of Minnesota

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SC/D3SC06920F

Reference54 articles.

1. Non-viral is superior to viral gene delivery

2. Large gene delivery to the retina with AAV vectors: are we there yet?

3. Progress and prospects: immune responses to viral vectors

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