RNN based molecule generation for developing novel therapeutics for neurodegenerative diseases

Abstract

Neurodegenerative diseases pose a significant global health challenge, necessitating innovative approaches for drug discovery. This study explores the application of recurrent neural networks (RNNs) in the generation of novel molecules with therapeutic potential for neurodegenerative diseases. Leveraging the power of deep learning, the RNN model is trained on diverse chemical datasets to learn the underlying patterns and relationships within molecular structures. The generated molecules are then evaluated for their drug-likeness and potential efficacy in targeting key pathways implicated in neurodegeneration. The results demonstrate the capability of RNNs to autonomously generate structurally diverse and biologically relevant compounds, providing a valuable resource for the development of novel therapeutics. This approach not only accelerates the drug discovery process but also offers a tailored and data-driven strategy for addressing the complexities of neurodegenerative diseases.