CNSMolGen: a bidirectional recurrent neural networks based generative model for de novo central nervous system drug design

Central nervous system (CNS) drugs have had a significant impact on human health, e.g., treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models, particularly those for designing drugs from scratch, have shown great potential for accelerating drug discovery, reducing costs 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 bidirectional recurrent neural networks (Bi-RNNs) system for de novo molecular design of CNS drugs by learning from compounds with CNS drug properties. Result shown that the pre-trained model was able to generate more than 90% of completely new molecular structures, and these new molecules possessed the properties of CNS drug molecules and synthesizable. In addition, transfer learning was performed on small datasets 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 dataset and generated a Focused database against the target protein. The potential biological activities of the generated molecules were verified 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.