Dataset creation for training size analysis. From ChEMBL, we created several subsets to investigate the effect of the training data size. 50,000 SMILES strings were randomly sampled for follow-up clustering. A spectral clustering algorithm 50 was used to cluster the SMILES strings based on their generic Bemis-Murcko 45 scaffolds. Stratified sampling by cluster assignation was used to create the datasets of different sizes (25000, 10000, 7500, 5000, 2500, and 1000) and ensure that smaller datasets were included in the bigger ones for comparability. Each dataset was randomly divided into a training (90%) and a validation (10%) set. From the remaining 25000 SMILES strings, a test set (1000 SMILES strings) was obtained via cluster-based stratification. The SMILES strings were then tokenized, and the start-of-the-sequence ('G') and end-of-the-sequence tokens ('E') were added. The tokenized SMILES strings were padded to the maximum length (150 tokens) and one-hot encoded. ) Fine-tuning. Three macromolecular targets were chosen from the MoleculeACE 52 repository: Peroxisome Proliferator Activated Receptor-d (PPARd), Serine/threonine-protein kinase (PIM1), and Janus kinase 2 (JAK2). These datasets were pre-processed as mentioned before. Afterwards, similar, and dissimilar sets of two different sizes (10, 100) were created. Datasets of similar molecules were created by performing agglomerative clustering as previously reported. To reach high similarity, 20 parent clusters 40 subclusters among the parents clusters were determined. Afterwards, the clusters and subclusters having more than the target number of molecules (10 or 100) were analysed for their Tanimoto similarity on Extended Connectivity Fingerprints (ECFPs, length = 1024 bits, radius = 2 bonds). Molecules with high pairwise similarity with each other (³ 0.8) were assigned to the fine-tuning set of highly similar molecules. To obtain low-similarity datasets, we used the function Leader_Picker of RDKit, to identify molecules with a Tanimoto similarity lower than or equal to 0.4.