Data mining, HTE and Bayesian flow optimisation shorten fragment elaboration chemistry
- The workflow chains literature data mining for starting conditions, high-throughput experimentation to prune parameter space, then closed-loop flow runs guided by multi-objective Bayesian optimisation.
- It was demonstrated on two two-step syntheses of fragment-derived protein kinase C inhibitor precursors, each needing C–C and C–X bond formation.
- Yields improved while waste was reduced, and selectivity was optimised to ease purification; QM reactivity prediction helped redesign one route.
- An accelerated variant found good conditions within a week and used much less substrate.
💡 Points Based on the abstract
Fragment elaboration lives or dies on how fast you can make the next vector, and substrate-starved, awkward couplings are exactly where this helps. The test I'd run is a campaign with unfamiliar chemistry, not two sequences the authors chose. Fitting a flow rig into a typical medchem lab is the realistic barrier.
📄 Paper · chemRxiv ↗ A Staged Reaction Optimisation Workflow for Fragment-to-Lead Elaboration