Chemogenomic Data for Epigenetic Targets. Quantitative compound-protein associations were extracted from ChEMBL 27 and PubChem to build epigenetic target-associated compound datasets meeting the following criteria: (a) containing at least 30 compounds with a quantitative measure of biological activity (IC 50 , EC 50 , K i or K d ) lower or equal to 10 µM ("active") and at least 30 compounds with a quantitative measure of biological activity higher than 10 µM ("inactive"), and (b) modelability index (MODI) higher than 0.7 for at least one of the three molecular fingerprints selected as compound representation (see Experimental Section for further details). As illustrated in Figure The compiled chemogenomic dataset contained 26318 unique compounds and 38129 compound-protein associations, with 28750 of them being labeled as active and 9379 labeled as inactive (due to the natural, although not the best practice of reporting mostly active compounds and not negative -inactive-data in ChEMBL). Consistently with the compound/compound-protein associations ratio, 20318 compounds (77.2%) in the dataset had known associations to a single target, and only 196 compounds (0.7%) had known associations to at least 10 targets, with a maximum of 15 targets for four compounds (Table ). k-nearest neighbors (k-NN) , Random Forest (RF) , Gradient Boosting Trees (GBT) , Support Vector Machines (SVM) , and Feed-Forward Neural Networks (FFNN) , and three molecular fingerprints of different design used as compound representations: Molecular ACCess System (MACCS) Keys (166-bit), Morgan fingerprint with radius 2 (2048-bit), and RDK fingerprint (2048-bit). Each model is denoted as a combination of fingerprint and algorithm (fingerprint::algorithm). For each algorithm and target, hyperparameters were optimized from an exhaustive search detailed in the Experimental Section, using the mean balanced accuracy (BA) over a 10-fold cross-validation as the performance metric to select the best set of hyperparameters. Figure shows the number of targets for which each model was identified as the best performing, considering the mean BA over the ten folds as a point metric. Under this approach, there is no model, fingerprint, nor machine learning algorithm that could be identified as the best performing for all 55 target datasets considered in this work. Figure shows that RDK::GBT had the highest mean BA for 14 out of the 55 targets, making them the most frequent choice. However, in terms of compound representations only, Morgan fingerprint was the best choice for 28 targets, followed by 24 for RDK fingerprint and three for MACCS. Nevertheless, t-tests comparing the sets of BA scores calculated from the ten validation folds revealed that for all the targets, there is at least another model with no significant difference of performance to the one with the highest mean BA (Table in the Supporting Information). Moreover, the t-test comparison revealed that for 35 out of the 55 targets, there are at least 9 other models with no significant difference of performance to the one with the highest BA (at 95% confidence level), a surprising quantity considering the number of algorithms and compound representations included.