Zeng Xin, Li Shu-Juan, Lv Shuang-Qing, Wen Meng-Liang, Li Yi
College of Mathematics and Computer Science, Dali University, Dali, China.
Yunnan Institute of Endemic Diseases Control and Prevention, Dali, China.
Front Pharmacol. 2024 Apr 2;15:1375522. doi: 10.3389/fphar.2024.1375522. eCollection 2024.
Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.
准确计算药物-靶点亲和力（DTA）对于制药行业的各种应用至关重要，包括药物筛选、设计和重新利用。然而，传统的用于计算DTA的机器学习方法往往缺乏准确性，在准确预测DTA方面构成了重大挑战。幸运的是，深度学习已成为计算生物学中一种很有前景的方法，促使开发了各种基于深度学习的DTA预测方法。为了支持研究人员开发新颖且高精度的方法，我们对使用深度学习预测DTA的最新进展进行了全面综述。我们首先对常用的公共数据集进行了统计分析，提供了基本信息并介绍了这些数据集的应用领域。我们进一步探索了药物和靶点的序列与结构的常见表示形式。这些分析为构建基于深度学习的DTA预测方法奠定了基础。接下来，我们着重解释了深度学习模型，如卷积神经网络（CNN）、循环神经网络（RNN）、Transformer和图神经网络（GNN），是如何在特定的DTA预测方法中有效应用的。我们突出了这些模型在DTA预测背景下的独特优势和应用。最后，我们对多种基于深度学习的预测DTA的先进方法进行了性能分析。这一全面综述旨在帮助研究人员了解现有方法的缺点和优点，并进一步开发高精度的DTA预测工具，以促进药物发现的发展。
