A Staged Reaction Optimisation Workflow for Fragment-to-Lead Elaboration

Here, we present a reaction optimisation workflow that integrates data mining to generate unbiased, data-led starting points, high-throughput experimentation (HTE) to narrow the reaction parameter space, and closed-loop flow experiments guided by multi-objective Bayesian optimisation for fine-tuning. Designed for early-stage medicinal chemistry, particularly fragment-based drug discovery (FBDD), the workflow was demonstrated in two direct two-step syntheses of potent fragment-derived protein kinase C inhibitor precursors, involving sequential C–C and C–X bond formations. Quantum-mechanical (QM) reactivity prediction was used to inform route redesign improving synthetic efficiency and safety. HTE identified promising categorical starting points, which were subsequently refined within 20-40 autonomous continuous flow experiments per campaign, across 4 reaction steps. The workflow improved reaction yields in both syntheses while minimising simple E-factor and incorporating selectivity as an additional optimisation objective to facilitate downstream purification. An accelerated variant identified comparably productive conditions within a single working week, reducing substrate consumption by approximately 60%. This work demonstrates how staged, data-informed optimisation can accelerate material delivery in early-stage drug discovery while reducing experimental burden and providing valuable insights for subsequent process optimisation.