In this research, we extend the methodology for generating novel molecular datasets by leveraging a pretrained Generative Pre-trained Transformer (GPT)-like model, incorporating specific conditions to guide the generation process. To evaluate the efficacy of this conditional generation approach, we initially sampled 7.2 million combinations from cross distributions of multiple molecular descriptors-Molecular Weight (MolWt), Heavy Atom Count, Ring Count, Molecular LogP (MolLogP), and synthetic accessibility score (SAS). These descriptors were selected from a comprehensive dataset comprising 77 million molecules, each characterized by a calculated Quantitative Estimate of Drug-likeness (QED) exceeding 0.85, emphasizing the focus on potentially high-quality drug-like molecules. For the generation phase, we employed these sampled descriptor values as input conditions, setting the QED condition explicitly to unity (1.0) to simulate an ideal scenario in drug design where maximum drug-likeness is desired. This approach allowed us to systematically assess how well the GPT-like model can adhere to and reproduce these optimal conditions in the generated molecules. By integrating these stringent conditions, we aim to demonstrate that our model can not only recreate molecules resembling those in the high-quality subset of the training data but also potentially innovate by exploring the chemical space around these high-condition benchmarks. This strategy highlights the model's capability to generate viable molecular candidates tailored to specific pharmacological profiles, thereby enhancing the utility of generative models in drug discovery and development.