Synergistic Machine Learning Accelerated Discovery of Nanoporous Inorganic Crystals as Non‐Absorbable Oral Drugs

Abstract Machine learning (ML) has taken drug discovery to new heights, where effective ML training requires vast quantities of high‐quality experimental data as input. Non‐absorbable oral drugs (NODs) have unique safety advantage for chronic diseases due to their zero systemic exposure, but their empirical discovery is still time‐consuming and costly. Here, a synergistic ML method, integrating small data‐driven multi‐layer unsupervised learning, in silico quantum‐mechanical computations, and minimal wet‐lab experiments is devised to identify the finest NODs from massive inorganic materials to achieve multi‐objective function (high selectivity, large capacity, and stability). Based on this method, a NH 4 ‐form nanoporous zeolite with merlinoite (MER) framework (NH 4 ‐MER) is discovered for the treatment of hyperkalemia. In three different animal models, NH 4 ‐MER shows a superior safety and efficacy profile in reducing blood K + without Na + release, which is an unmet clinical need in chronic kidney disease and Gordon's syndrome. This work provides a synergistic ML method to accelerate the discovery of NODs and other shape‐selective materials.