negative training examples (NTEs, non-binders) or positive training examples (PTEs, binders). To preserve the confidentiality of the proprietary DEL library, we applied chemical fingerprinting, a widely used technique that encodes chemical compounds as 2048-bit vectors. This method edectively masks the chemical structures while allowing the development of ML models . The final DEL dataset consisted of all binders and a subset of non-binders, ten times larger than the number of binders (details of non-binder selection are provided in the next section). This dataset was then used for binary classification in ML model development. Of note, masking chemical structures via fingerprint representations precludes the use of structure-dependent methods such as molecular docking or graph convolutional neural networks . However, fingerprint representations have been widely employed in cheminformatics and are compatible with ML models, including feed-forward neural networks or tree based learning algorithms .