Regarding the architecture of the deep generative model, we decided to use one of the widely used approaches in the field, namely REINVENT . Recently, many architectures of generative models for de novo design have been published such as recurrent neural network (RNNs) , convolutional neural network (CNNs) and graph convolutional neural network (GCNN) . In the drug discovery field, although there are many models including variational auto encoder (VAE) , and Generative Adversarial Networks (GAN) , due to the success of NLP, which was driven by many techniques like long-short time memory (LSTM) , gated recurrent unit (GRU) and attention mechanism , language models have found great resonance . We chose REINVENT as the generative model in this study, an RNN-type language model with the ability to perform goal-directed optimization through fine-tuning and reinforcement learning, due to its availability, flexibility and wide use making results herein more relatable .