The Mol2Mol is a sequence to sequence transformer that produces close analogues to input molecules. This approach is essential for expanding the training data into unexplored chemical spaces, enhancing the model's ability to learn and predict in these regions. The process begins with SMILES standardization using RDKit, converting molecules to a canonical form and extracting molecular scaffolds. New molecules are then generated by modifying side chains or functional groups while retaining the core scaffold structure. To ensure practical relevance and drug-likeness of the synthesized molecules, several constraints are imposed: generation within a range of ± 100 Daltons from the template's molecular weight, a modified Lipinski's Rule of Five allowing molecules up to 550 Daltons, and a configurable Tanimoto similarity filter (default 0.3) to maintain a desired level of similarity to the target molecule. The system incorporates a scaffold hopping mechanism that shifts to a new molecular scaffold if the current one fails to produce viable candidates after a set number of iterations or if too many molecules with the same scaffold have been generated.