With 61% of the computational designs successfully synthesized and 12 novel LXR agonists with low-micromolar to nanomolar activity identified, the integrated de novo design platform shows promise for automating the DMTA cycle in drug discovery. By tailoring the model optimization to the available experimental pipeline, the benefits of 'rule-free' de novo design, virtual reaction specification, and automated synthesis were combined. The results further validated the ability of generative molecular design approaches to capture desired molecular properties such as chemical synthesizability and on-target bioactivity, and their potential to support automation. The proposed DMTA framework offers the promise of fast iterations through the molecular design cycle and data-driven compound optimization. Owing to its modular character, the approach can be tailored to other de novo design applications by replacing the computational molecule generator, reactions used for filtering, or synthesis technology. For the purpose of this proof-of-concept study, computational tools were used to obtain a structurally balanced set of bioactive hits, as demonstrated in the design of both the analogs of known LXRα agonists and novel molecular scaffolds. The three novel bioactive scaffolds (with EC50 values ranging from 0.24 to 4.5 µM) highlight the capability of the computational pipeline to explore narrow regions of the relevant chemical space, while at the same time providing unexplored molecular cores. To further explore the permissive structural diversity of the designs, several strategies can be adopted; for example, choosing different optimization criteria for the generative deep learning model, or including other artificial intelligence models in the definition of compatible organic reactions.