基于深度学习的药物-靶点结合亲和力预测的最新进展
Recent Advances in Deep Learning-Based Drug-Target Binding Affinity Prediction
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- Prairie View A&M University（普雷里维尤农工大学）
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中文总结 AI 辅助
本文综述近期基于深度学习的药物-靶点结合亲和力预测方法，分析其优势、局限与研究缺口，指出当前方法存在数据集偏差等问题，探讨未来研究方向。
中文摘要 AI 辅助
药物发现的计算方法涉及多个子问题，其中药物-靶点结合亲和力预测发挥着重要作用。尽管已有最新进展，但准确预测结合亲和力仍是一个开放的研究领域。本文的主要目标是对近期用于药物-靶点结合亲和力预测的机器学习方法进行全面综述和比较分析，重点在于确定这些方法的优势、局限性及研究缺口。我们综述了具有代表性的近期深度学习方法，这些方法使用常见的基准数据集和评估指标，涵盖了多种神经网络架构和表示策略。此外，我们分析了七种广泛使用的药物-靶点结合亲和力预测基准数据集及常用评估指标。我们的分析表明，尽管许多方法在标准基准上报告了较强的性能，但其有效性常受数据集偏差和有限评估设置的影响。此外，大多数方法在冷启动场景中性能会下降，凸显了泛化方面的挑战。我们确定了当前方法的若干局限性，包括数据集不平衡、缺乏标准化评估、实际应用适用性有限以及冷启动场景的处理挑战。我们还讨论了未来的研究方向，包括更好的数据集设计、更稳健的评估方法、改进的冷启动问题处理以及多模态表示的整合。
英文摘要
Computational approaches to drug discovery involve multiple sub-problems, and among them, drug-target binding affinity prediction plays an important role. Despite recent advances, accurately predicting binding affinity remains an open research area. The major objective of our paper is to perform a comprehensive review and comparative analysis of recent machine learning methods for drug-target binding affinity prediction, with a focus on identifying strengths, limitations, and research gaps. We review representative recent deep learning approaches that use common benchmark datasets and evaluation metrics, covering a range of neural network architectures and representation strategies. In addition, we analyze seven widely used benchmark datasets and commonly adopted evaluation metrics for drug-target binding affinity prediction. Our analysis indicates that although many methods report strong performance on standard benchmarks, their effectiveness is often influenced by dataset bias and limited evaluation settings. Furthermore, most methods exhibit reduced performance in cold-start scenarios, highlighting challenges in generalization. We identify several limitations of current approaches, including dataset imbalance, the lack of standardized evaluation, limited real-world applicability, and challenges in cold-start scenarios. We also discuss future research directions, including better dataset design, more robust evaluation methods, improved handling of cold-start problems, and the integration of multimodal representations.
