In order to accelerate the screening of molecular catalysts, we take the help of machine learning models to make guiding predictions in the search for the most promising catalysts. We utilize several 2-D and 3-D descriptors to featurize the relevant chemical space for revealing hidden structure-property relationships within this data. To generate 2-D descriptors, we use the SMILES strings of the catalysts as the input, which is useful in capturing all information related to atom types and their connections to neighboring atoms. From the RDKit code, we use the following fingerprints: Morgan fingerprints with circular radius 2 , hashed atom pair (HAP) fingerprints , hashed topological torsion (HTT) fingerprints , and MACCS keys. Additionally, we compute the synthetic feasibility of the catalysts in our library using two scoring functions: SA-score from RDKit and SC-score .