Although hypothetical and open to more extensive details and nuance, such a workflow represents a realistic use of automation and ML in the design of nanomedicines. Moreover, it emphasizes several important considerations. The availability of high-quality datasets underpins the successful adoption of AI and ML in nanomedicine. While combined ML methods and more intricate approaches like inverse design can greatly improve the way that data is processed for drug design and development, there continues to be a lack of high-quality data in this space . Therefore, automation is vital for producing these datasets more reliably. An early step can include generating in-house experimental data, but the curation of more extensible datasets and open-source libraries will require collaborations between research laboratories, external agencies, and industry partners. Interdisciplinary research teams with specialties across chemistry, biology, engineering, and computer science can also help to advance the development of nanomedicines by contributing their respective expertise to the integrated workflow. This form of collaboration will also be instrumental in training and uplifting the next generation of pharmaceutical scientists with a skillset in statistics, automation, and computational techniques.