Central nervous system (CNS) drugs have had a significant impact on treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models have shown great potential for accelerating drug discovery and improving efficacy. However, specific applications of these techniques in CNS drug discovery have not been widely reported. In this study, we developed the CNSMolGen model, which uses a framework of bidirectional recurrent neural networks (Bi-RNNs) for de novo molecular design of CNS drugs. Results showed that the pretrained model was able to generate more than 90% of completely new molecular structures, which possessed the properties of CNS drug molecules and were synthesizable. In addition, transfer learning was performed on small data sets with specific biological activities to evaluate the potential application of the model for CNS drug optimization. Here, we used drugs against the classical CNS disease target serotonin transporter (SERT) as a fine-tuned data set and generated a focused database against the target protein. The potential biological activities of the generated molecules were verified by using the physics-based induced-fit docking study. The success of this model demonstrates its potential in CNS drug design and optimization, which provides a new impetus for future CNS drug development.