Jessi J. White

Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, China
Author    Correspondence author
Bioscience Methods, 2024, Vol. 15, No. 1   doi: 10.5376/bm.2024.15.0004
Received: 12 Jan., 2024    Accepted: 13 Feb., 2024    Published: 25 Feb., 2024

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Zhang J., 2024, New methods for predicting drug molecule activity using deep learning, Bioscience Method, 15(1): 28-36 (doi: 10.5376/bm.2024.15.0004)

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.

Deep learning; Drug molecule activity; Drug discovery; Graph neural networks; Interdisciplinary collaboration