For the metallic centers, we constructed a database of toxicity considering multiple oxidation states, normalized for the chloride salt as an ionic control and considering oral administration. For the organic linkers, we developed robust and interpretable ML models based on SVMs, RFs, and GBMs. The bestperforming model evaluated against multiple metrics gave an accuracy of over 83% on unseen data. Using the constructed database and ML models, we screened the CSD containing over 86,000 non-disordered MOF structures for potential biocompatible MOFs for drug delivery, identifying 809 candidates, most of which have well-defined synthesis protocols. Notably, our findings, especially for the metallic center toxicity, largely agree with recent research concerned with the clinical potential of MOFs. • By employing interpretable ML models, we have uncovered crucial chemical features in organic linker molecules strongly linked to toxicity, both at global and local levels. These features encompass the presence or absence of specific molecular fragments, surface areas, partial charges, and more abstract characteristics like drug-likeness. This analysis has allowed us to propose general design strategies for creating MOFs with enhanced biocompatibility. At a local level, we were able to analyze the features that had large contributions to the toxicity classification and propose design choices for making the linker safer.