biocompatibility, these rules are difficult to generalize for the efficient screening of biocompatible MOF candidates. With these constraints in mind, here we present a systematic analysis of factors influencing MOF toxicity and attempt to guide the rational design of biocompatible MOFs for drug delivery applications using interpretable machine learning (ML). Inspired by the drug discovery process, we propose a computational pipeline (outlined in Scheme 1) capable of screening large MOF libraries for their inherent biocompatibility in a high-throughput manner. Our methodology utilizes the distinctive chemistries of MOF building blocks to guide inferences surrounding prospective biocompatibility. Similar approaches to leveraging MOF precursor chemistry have been reported by Pétuya et al., for predicting guest-pore accessibility, and by Batra et al., for predicting water stability. As elucidated by Bencherif and colleagues, the chemistry of the precursors emerges as a pivotal determinant of MOF toxicity. For the metallic center, correlations of intrinsic chemical features with the toxicity of the MOF are straightforward, while for the organic linker, these correlations are convoluted, owing to conflicting effects of different chemical features. To address the toxicity concerns of the metal center, we curated a database of reported median lethal dosage (LD50) values of corresponding ions in oxidation states commonly found in MOFs, maintaining respective chloride salts as ionic control. For the organic linker, we developed predictive algorithms (classification models) using a database of LD50 values of over 35,000 organic molecules, when administered using the intraperitoneal (i.p.) route. For each molecule in the database, a vector of chemical features was calculated, using which a three-class classification model was developed, capable of classifying linkers as 'safe', 'toxic', or 'fatal'. Three unique predictive algorithms were developed, optimized, and evaluated against several metrics. The best-performing model showed an 83% accuracy against previously unseen data and showed good generalization capabilities when tested on MOF linkers with known toxicity profiles.