arXiv Machine Learning August 7, 2026 By Kai Dahms, Eilien Heinrich, Jochen Schmid, Michael Bortz, Iryna Savych, Regina Bleul

Accelerating nanodrug development in continuous flow systems using informed prediction models based on low-cost surrogate nanoparticles

Read the original on arXiv Machine Learning →

arXiv:2608. 05761v1 Announce Type: new Abstract: The development of nanotherapeutics often involves extensive empirical optimization due to the sensitivity of nanoparticle properties, such as size and polydispersity index (PDI), to minor changes in process parameters.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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