CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design

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.