Eight compound libraries summarized in Table were used as training sets for alvaBuilder, including five commercial libraries (two of which are epigenetic-focused), DNMT1 inhibitors, food chemicals from FooDB, and natural products. As the number of compounds available for each chemical library is highly variable, a filtration was made to match the number of 285 DNMT1 active compounds in ChEMBL. The first step after data curation was the selection of the molecules with MW greater than 300 to avoid fragment-like compounds . The MW descriptor was computed and sorted in descending order with alvaMolecule 1.0.4 after representing all the libraries with the Aromatic form available in the software. Then, a diverse subset of 285 molecules was selected with the MaxMin algorithm implemented in Molecular Operating Environment (MOE) 2022.02 , using the MACCS Keys fingerprint (166-bits) and the Tanimoto coefficient . Details of the remaining number of compounds for each stage are in Table in the Supplementary Material.