Note that target-specific information was contained only in the input to ML models, and not used for training ML models themselves. In practical applications, one would not have to retrain an ML model to make predictions for a new protein target (though in this work we had to retrain a model from scratch for each target to make sure that the training set does not include molecules active against the given target for the purpose of the method validation). Surprisingly, these target-naïve ML models managed to generate not just reasonable molecular structures, but molecular structures some of which are highly active ligands against each of the three protein targets, or at least provide ideas for such ligands (Fig. ). Some of the generated molecules coincided with the known highly active ligands, while some other generated molecules have minor differences from known actives (e.g., they may differ by -Cl, -OH, -CH3 groups, etc.). This ability to generate, among others, highly active ligands from moderately active ones, or ideas for such highly active ligands, was not an ability that these models were explicitly trained for, because the pairs of molecules in the training sets were not ordered by bioactivity. Generated molecules could in principle be less active or more active as input molecules, but the results reported in this section demonstrate that a sufficiently large fraction of output molecules is highly active.