The efficacy, novelty, and uniqueness of generated molecules are the most critical criteria for evaluating molecular generation models . Efficacy refers to the percentage of generated SMILES strings that are chemically valid; uniqueness pertains to the percentage of unique SMILES strings (after canonicalization) within the generated set; and novelty denotes the percentage of generated SMILES strings that represent molecules not included in the training set. These three metrics collectively reflect the model's capacity to learn molecular structures, explore chemical space, and innovate in molecular design.