This study focus on a GPT-like transformer model, specifically designed for molecule generation, considering six pivotal physicochemical properties: molecular weight, number of non-hydrogen atoms, ring count, hydrophobicity, quantitative estimation of drug-likeness (QED) , and synthetic accessibility score (SAS). Developing a model that can efficiently manipulate these dimensions in molecular design can immensely benefit synthetic chemistry, reducing the time and cost associated with experimental methodologies. This conditional GPT-like model for molecule generation, specifically configured to incorporate six selected physicochemical properties as mentioned above. While these properties were chosen due to their significant relevance in molecules designing, it is important to underscore that the architecture of the model is inherently flexible. The conditional nature of this GPT-like generator is not limited to these six attributes alone; indeed, the framework can be readily adapted to integrate a wide array of other molecular properties and conditions. This versatility allows for customization to meet specific research needs and objectives, making it an invaluable tool in the exploration and design of novel compounds.