In RedDB, the molecules are represented in SMILES notation. In order to train ML models, first, it is required to convert the SMILES representations to latent space variables. To find the best-suited representation for our aim, we compared the available molecular encoders. For this purpose, we employed various molecular encoders which are structural fingerprints (ECFC , ECFP , SECFP , and MACCS), NLP embeddings (Mol2vec ), and phsico-chemical descriptors (Mordred 36 ). To compare the performances of the molecular encoders, we trained separate Light Gradient Boosting Machine (LGBM) models under the same conditions by encoding the training set using the different molecular encoders. The results are shown in Table for each molecular encoder. Among the models, the one encoded by ECFC showed the best performance on both Test-