FSM-DDTR (End-to-end feedback strategy for multi-objective De Novo drug design using transformers) , a transformer model that uses SMILES as an input to generate molecules with proper physicochemical and pharmacological properties. The transformer's architecture is a basic encoder predictor and a decoder generator, along with a feedback loop to optimize generated compounds; top-scoring chemical compounds take place of least favorable inputs of the decoder. The model could generate valid SMILES strings with a great level of novelty, approximately 100% uniqueness. To estimate the unbiased decoder performance MOSES dataset was utilized, the decoder had superior performance in novelty rate (> 97%) and comparable internal diversity, uniqueness, and validity rate, with reduced chance of overfitting due to the novelty of generated compounds. A model trained on multiple features (pIC50, SAS, LogP, MW, and TPSA) achieved the overall best performance, with 99.36% of the generated chemical entities following Lipinski's rule of five; potentially orally available. Moreover, mostly they were valid and novel, and the average value of pIC50 toward the AA2AR receptor (6.81) outperformed the unbiased model result (5.81). DGFN (Double Generative Flow Networks) , a concept introduced to advance the training stability and the exploration abilities of large state spaces of GFN (Generative Flow Networks). DGFN is developed with the influence of both reinforcement learning and generative models. The enhancement of the exploration capabilities serves as a great chance to improve the sampling of molecules for DD and small molecules generation purposes. DGFN was put into comparison with conventional GFN on two benchmark tasks: hypergrid and molecule generation; DGFN finds modes across the hypergrid faster than conventional GFN. The DD task was about generating small molecules, molecules with low binding energy to the soluble epoxide hydrolase (sEH) protein, three models were trained DGFNTB along with two baseline models, GFNTB and GFNSubTB. GFNTB exhibited more pronounced fluctuations in the training, and DGFNTB had lower variance. DGFNTB had also surpassed GFNSubTB in the discovery of modes with rewards > 0.9.