Information-Dense Synthesis cuts molecular discovery experiments from O(d) to O(log d)
anshulkundaje · x · 2026-10-07
A new preprint (arXiv:2610.08495) proposes algorithmically controlled stochastic synthesis for molecular discovery: instead of designing and testing single molecules, it designs complex mixtures, tests them as a pool, then deconvolves the molecule-activity map.
- By optimizing synthesis to encode maximal information, the approach theoretically reduces experiments needed to find the best molecule among d candidates from O(d) to O(log d) or even O(1).
- In simulation on estimated protein fitness landscapes, it finds active molecules with an order of magnitude fewer experiments than existing Bayesian optimization.
- Targets sparse settings where desired properties are extremely rare and prior algorithms barely beat random guessing.
Related event: New preprint introduces information-dense synthesis for molecular discovery(2 posts)→
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