While fragment-based and general descriptors such as molecular weight are easier to interpret and can directly guide linker design, some descriptors are more abstract but nonetheless crucial to consider. These 'abstract' descriptors build-off of the seminal Lipinski's 'rule-of-five' , wherein it was proposed that highly desirable drug-like compounds have properties that fall within specific ranges. For instance, compounds having hydrogen-bond donors > 5, an octanol-water partition coefficient > 5, a molecular mass > 500 Da, and hydrogen-bond acceptors > 10are more likely to have poor permeability, and are thus, less desirable. One such abstract descriptor is the quantitative estimate of drug-likeness (QED) developed by Bickerton et al. for evaluating the desirability of drug-like compounds through underlying properties such as solubility, permeability, metabolic stability, and transporter effects. The QED is seen to vary for the different classes of molecules both in the original dataset and for the predictions. Notably, it was observed that molecules with the highest QED values belong pre-dominantly to the toxic category (with an average value of 0.62 compared to safe and fatal molecules having average values of 0.50 and 0.49 respectively). Thus, relying on the QED value appears to be a viable strategy from a biocompatibility perspectiveas it on average can discriminate between toxic and fatal molecules. Similarly, it has been proposed that increasing the saturation, i.e. the number of sp carbon atoms in a molecule, and reducing the aromatic ring count, increases the chances of clinical success. It should however be noted that there are several exceptions to these 'rules', with estimates suggesting that only 51% of FDA-approved small-molecule drugs complying with the 'rule-of-five'. 61 It is due to these deviations that it becomes necessary for models to be locally interpretable. In such cases, we may rely on local explanations from the associated SHAP analysis. SHAP is a game-theory based approach from explaining the output of ML models by decomposing this output into the sum of the impact of individual features. Features with positive SHAP values increase the probability of a particular classification, while those with negative values decrease the probability. The magnitude of the SHAP value highlights the relative importance of a feature for a particular classification (which may differ from the global feature rankings). As depicted in Figure , from SHAP analysis, it can be inferred that the toxicity classification of 2methylimidazole (mIm) linker in ZIF-8, has large positive contributions from its electro-topological state (refer to Supporting Information, Figure ) and the absence of fragments such as carbonyl oxygens and aromatic heterocycles. On the other hand, the classification has large negative contributions from features such as its molar refractivity (MolMR) and the molecular weight (ExactMolWt). Possibly due to structural similarities, the linker in ZIF-90 too has contributions to its toxicity classification from similar features (EState_VSA1, MolMR). Additionally, features such as the aromatic nitrogen fragments (fr_Ar_N) and QED have significant contributions to the toxicity classification. Notably, in such cases, relatively simple functionalizationssuch as the addition of an amide functionality, significantly changed the toxicity profile. To aid the interpretation of the forceplots depicted in Figure , we refer the reader to a detailed schematic in Supporting Information, Figure .