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 NH4-form nanoporous zeolite with merlinoite (MER) framework (NH4-MER) is discovered for the treatment of hyperkalemia. In three different animal models, NH4-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. A synergistic machine learning accelerates the discovery of high-capacity, high-selectivity, and stable inorganic nanoporous crystals as non-absorbable oral drugs (NODs). NODs can remove unwanted molecules or ions from the gastrointestinal tract of the human body without directly entering the bloodstream. NH4-form merlinoite (NH4-MER) discovered by synergistic machine learning can prevent the Na+ release from ZS-9 in the treatment of hyperkalemia. image
