ArticleNature communications2026
On-demand design of controlled-release systems using an expert-mimic AI framework.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Navigating a vast formulation-preparation design space that is highly sensitive to perturbations makes trial-and-error approaches inefficient and costly for diverse clinical needs. Here, we introduce an expert-mimic AI framework called E-MAF that integrates a process-aware surrogate with an optimized genetic algorithm to emulate coarse-to-fine reasoning, autonomously generating lab-ready designs tailored to desired release profiles. We show that E-MAF delivers on-demand release and shortens design cycles from 6-12 months to approximately 1 hour. We demonstrate that in vitro release, in vivo pharmacokinetics and therapeutic effects match or surpass commercial benchmarks across 7 active pharmaceutical ingredients. We show that E-MAF also enables release profiles that are hard to achieve by conventional approaches, exemplified by ≤2% risperidone burst within 1 h and near-zero-order release over ~28 days in vivo. These results demonstrate E-MAF's potential to accelerate polymeric controlled-release system development and drug-delivery industrialization.
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