LLMs are becoming of great advantage to the process of de novo drug design (in-silico design of drugs, usually proteins, from scratch). A transformer model proposed by depended on protein sequences to predict binding affinities between generated molecules and their biological targets; avoiding all the obstacles that arise in structure-based de novo drug design that requires prior knowledge of the three-dimensional structure of proteins. This transformer model was composed of an encoder-decoder architecture, taking the protein sequence as an input to generate the corresponding Simplified Molecular-Input Line-Entry System (SMILES) string. The model is based only on self-attention mechanisms with no Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN), with that it works with long-range inputs and is much faster than RNN-based models. Most of the generated SMILES strings were valid and unique, with more than 17% novel compound matching the ZINC15 database. Two proteins were selected; Insulin-like growth factor 1 receptor (IGF-1R) and Vascular endothelial growth factor receptor 2 (VEGFR2), both are from the receptor tyrosine kinases family and contribute to major diseases, such as cancer, arthritis, and diabetes. Most of the generated molecules had acceptable drug-like molecule boundaries with proper reproducibility of the property distribution of molecules as in the training set. However, this must be checked in experiment. 51% of generated molecules had a Tanimoto score lower than 0.5; suggesting major differences than molecules in the training dataset, this leads to a high variation of their functionalities.