However, as will be discussed in upcoming sections, most linkers are toxic. In view of this, we propose three customs categories: i) safe, ii) toxic, and iii) fatal, formed by combining relevant categories as developed in the GHS. Upon curation and processing, we deployed strategies to represent the chemical data in a machinereadable format. A robust and reliable technique of representation is calculating a list of molecular features that are relevant to the problem at handin our case, toxicity. In order to build accurate models, it is essential to select features that can collectively capture trends in toxicity across different classes of organic linkers. For featurization, we used an open-source library for generating 197 cheminformatics-based descriptors at different hierarchical levels (Figure ). These were a combination of physical molecular descriptorssuch as molecular weight -, fragment-based descriptorssuch as the number of carbonyl groups presentand more complex descriptors based on polarity, partial charges, and surface areas. Supporting Information, Section S3 shows the full list of descriptors calculated, with brief descriptions of their physical implications. The choice of descriptors was motivated by the broader goal of capturing experimentally implementable chemical features that are correlated to toxicity. In this regard, descriptors that can encode information about the entire atomic structure -known as 'global descriptors'are more useful than descriptors representing localized regions within a structure -known as 'local descriptors'. The inclusion of descriptors such as those related to molar refractivity, van der Waal's surface areas and partial charges may seem peculiar at first. These descriptors, however, are widely used in computational chemistry as they introduce orthogonality, i.e. they capture different aspects of the molecule that traditional descriptors cannotallowing us to train models on uncorrelated data, which is more desirable. Additionally, these descriptors allow us to discriminate between molecules that have small structural differences. While these correlations are complexand not necessarily experimentally accessible, these descriptors help rationalize the ability of molecules to bind to receptors and cross biological membranes. Figure shows the variation of these features across different categories of toxicity. From these distributions, it is evident that not all calculated descriptors are correlated to toxicity. In such a scenario, it is important to retain only those features that are relevant to the target. Here, we relied on the simple, effective technique of selecting the 'K' (K  {20, 30, … , 180}) features most correlated to toxicity, discussed in the following sectionsand further in Supporting Information, S5.