chain to loop: cost-aware value-of-information scheduling for cyclic-peptide discovery
Year: 2026
Type: article
Abstract: Drug discovery screens candidate molecules through a cascade of assays rather than a single test — from cheap property predictors through docking, molecular dynamics and free-energy perturbation to synthesis and a wet-lab measurement — whose costs differ by orders of magnitude. As these pipelines become automated, a platform must decide not only which candidate to test next but which assay to spend the next unit of budget on — a scheduling question that is usually ignored. What little evidence exists is measured across repeated runs of a single method rather than across the laboratories or targets to which a result must generalise. Here we ask whether a cost-aware value-of-information (VOI) policy can do better: at each step it chooses the candidate-and-assay pair that most reduces advance-or-kill uncertainty per unit of cost. The preliminary experimental results are promising. On two real cyclic-peptide discovery cascades — PAMPA permeability for 715 molecules across 27 laboratories, and Kᵢ binding affinity for 1,964 compounds across 40 targets — VOI scheduling beat the standard cascade that runs each candidate from the cheapest assay to the dearest, raising cost-weighted top-decile yield by 0.201 for permeability and by a positive margin for binding (0.080), each beating a random baseline. Significance is judged across whole laboratories and targets rather than individual molecules, so the gains reflect whether the advantage carries over to new laboratories and targets. Choosing what to measure next, not only what to make, turns a fixed validation cascade into an adaptive one — and as discovery moves toward self- driving laboratories, deciding where to spend each experiment may come to matter as much as the molecules proposed.
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225646396
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