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TuNa-AI: a hybrid kernel machine to design tunable nanoparticles for drug delivery

18 March 2025, Version 1
Working Paper
This content is an early or alternative research output and has not been peer-reviewed by Cambridge University Press at the time of posting.

Abstract

Artificial intelligence (AI) stands to accelerate the development of nanoparticles for drug delivery, but current methodologies either focus on the identification of materials or adjusting of relative ratios of multi-component systems. Here, we developed a bespoke hybrid kernel machine integrating molecular learning and relative composition inference to engineer nanoparticles with new components and tunable composition. Our approach identified nanoformulations that encapsulate previously inaccessible drugs and can also guide excipient reduction.

Keywords

machine learning
nanoparticle drug delivery
kernel machine

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Supplementary figures referenced in the main document.
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Associated GitHub
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Code for model training and testing.
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Now Published

TuNa-AI: A Hybrid Kernel Machine To Design Tunable Nanoparticles for Drug Delivery [opens in a new tab]
Zilu Zhang, Yan Xiang, Joe Laforet, Ivan Spasojevic, Ping Fan, Ava Heffernan, Christine E. Eyler, Kris C. Wood, Zachary C. Hartman, Daniel Reker journal article
ACS Nano
Online publication date: Sep 11, 2025

Version History

Mar 18, 2025 Version 1

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The content is available under CC BY NC 4.0 [opens in a new tab]

DOI

10.26434/chemrxiv-2025-r8mvz
D O I: 10.26434/chemrxiv-2025-r8mvz [opens in a new tab]

Funding

National Institute of General Medical Sciences
R35GM151255

Author’s competing interest statement

K.C.W. is a co-founder, consultant, and equity holder at Tavros Therapeutics and Celldom, a scientific advisor and/or equity holder at Simple Therapeutics, Decrypt Biomedicine, Retroviral Therapeutics, and Stelexis Biosciences, and has performed consulting work for Guidepoint Global, Bantam Pharmaceuticals, and Apple Tree Partners. D.R. acts as a consultant to the pharmaceutical and biotechnology industry, as a mentor for Start2, and on the scientific advisory board of Areteia Therapeutics.

Ethics

The author(s) declare that they have sought and gained approval from the relevant ethics committee/IRB for this research and its publication.

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