Automation of this iterative process, combined with Machine Learning (ML) algorithms, has the potential to accelerate new biomedical developments by several orders of magnitude, reduce error rates, foster reproducibility, and enable rational optimization of molecules . Recent advancements in chemistry, biology, ML, and robotics have allowed, for the first time, the construction of automated closed-loop Design-Make-Test (DMT) environments for designing bioactive molecules for life-science and clinical applications . Guided by ML algorithms, future platforms would integrate a fully automated closed-loop DMT environment for molecular design, synthesis, purification, and activity testing to enable iterative variation and optimization of bioactive molecules .