Insilico Medicine and Eli Lilly published a vision for a fully autonomous “prompt‑to‑drug” pipeline that integrates generative AI across design, synthesis and testing—outlining technical and regulatory steps toward large‑scale automation in drug discovery. The framework, detailed in ACS Central Science, presents an integrated stack from molecular generation to candidate prioritization for experimental follow‑up. In parallel, teams are deploying AI‑generated synthetic data to accelerate oncology trial design and site readiness, using synthetic cohorts to augment datasets for model training and feasibility assessments. Synthetic-data proponents argue the approach can preserve patient privacy while improving trial simulations and powering early decision making. Both developments accelerate industry momentum behind AI‑driven R&D but raise validation, reproducibility and regulatory questions. Companies will need robust benchmarks, prospective validations and transparency to convert AI workflows into regulated drug‑development decisions.