In 2016, Gómez-Bombarelli et al. first introduced a method which converts discrete representation of molecules into a multi-dimensional continuous representation, representing an early application of the variational autoencoder (VAE) model in the field of molecular generation . Subsequently, an array of different deep learning models has been developed in this field . Among them, recurrent neural networks (RNNs) (Fig. ) have shown remarkable performance. This is because RNN can obtain high precision and good performance when processing time series predictions based on a large number of data sets . In particular, Bjerrum and Threlfall introduced RNN to generate chemically plausible and novel molecules by incorporating the Long Short-Term Memory (LSTM) network to overcome the challenges associated with vanishing gradient or explosion problems . In addition, Wu et al. proposed bidirectional long short-term memory (BiLSTM) attention network (BAN) to enhance the extraction of crucial features from the simplified molecular input line entry specification (SMILES) strings, leading to improved performance in molecular property prediction . However, no deep learning-based model specifically for generating molecules with CNS drug properties has been reported yet. In this work, we present CNSMolGen, a generative model specifically constructed for de novo design of CNS drugs. The model employs a bidirectional RNNs (Bi-RNNs) based on SMILES string representation to design target molecules from scratch. First, an RNN model based on LSTM units to generate small molecule datasets with specific properties was pre-trained using the large dataset of ChemBridge-MPO which contains 504,853 compounds with good CNS drug properties and synthetic accessibilities. Then, using the representative CNS diseases molecular drug target SERT as an example , transfer learning technique was employed to fine-tune the pre-trained model with the aim of generating target-specific small molecule datasets under the condition of limited data size. Finally, the physics-based induced fit docking (IFD) was used to verify the binding mode and binding affinities of de novo designed molecules to SERT.