Bioscience Method 2024, Vol.15, No.1, 28-36 http://bioscipublisher.com/index.php/bm 28 Reviews and Progress Open Access New Methods for Predicting Drug Molecule Activity Using Deep Learning Zhang Jie Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, China Corresponding email: jessi.j.zhang@foxmail.com Bioscience Method, 2024, Vol.15, No.1 doi: 10.5376/bm.2024.15.0004 Received: 12 Jan., 2024 Accepted: 13 Feb., 2024 Published: 25 Feb., 2024 Copyright © 2024 Zhang, 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. Preferred citation for this article: 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) 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. Keywords Deep learning; Drug molecule activity; Drug discovery; Graph neural networks; Interdisciplinary collaboration Over the past few decades, drug discovery has been a central focus of medical research, not only because it provides new therapies to improve patient quality of life, but also because it plays a critical role in public health and global health. However, despite the increasing importance of drug discovery, the traditional drug discovery process faces numerous challenges, including high R&D costs, lengthy development cycles, and low success rates (Noe and Peakman, 2017). Every new drug from conceptualization to market must undergo a lengthy R&D journey, covering stages from early basic research and molecular screening to clinical trials (Schneider, 2017). Additionally, traditional methods depend on limited chemical and biological knowledge, making it particularly difficult to predict the biological activity and safety of molecules, leading to many potential drug candidates failing in clinical trial stages. With the rapid development of computing technology, deep learning, as an advanced form of artificial intelligence, has shown great potential in learning and simulating human cognitive processes (Walters and Barzilay, 2020). Deep learning technologies, particularly Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), have made revolutionary advances in fields such as image and speech recognition, natural language processing, and gaming strategies (Jiménez-Luna, 2020). These technologies can process and analyze large amounts of unstructured data, uncover deep patterns in complex data, thereby providing profound insights into human intelligence. Therefore, deep learning has not only changed the landscape of data science but also provided new perspectives and methods for medical research, especially drug discovery (Tran et al., 2023). Given the tremendous success of deep learning in other fields, this study aims to explore how deep learning technologies can be applied to predict drug molecule activity, and how these new methods can help overcome the challenges of the traditional drug discovery process. This research will thoroughly review the latest advances and applications of deep learning technologies in drug molecule design, activity prediction, toxicity assessment, and drug-target interaction prediction. By deeply analyzing how deep learning can improve the efficiency and accuracy of drug discovery, this study will demonstrate the potential of these technologies in accelerating new drug development, reducing R&D costs, and enhancing drug safety.
RkJQdWJsaXNoZXIy MjQ4ODYzMg==