New Methods for Predicting Drug Molecule Activity Using Deep Learning

Abstract

With the rapid development of deep learning technology, its application in predicting drug molecule activity is becoming increasingly widespread. This study reviews the latest progress and applications of deep learning in the field of drug discovery, especially in predicting drug molecule activity. It focuses on discussing several major deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Graph Neural Networks (GNN), and how they help improve the accuracy and efficiency of drug activity prediction. Additionally, the importance of interdisciplinary collaboration in promoting the application of deep learning in drug discovery is explored, and directions for future research are proposed, including improving model interpretability, optimizing data quality, and expanding the application of deep learning technology. This study aims to provide researchers and drug development experts with a comprehensive and in-depth perspective on the potential and challenges of deep learning in predicting drug molecule activity, while also offering insights and references for research and development in related fields.