Mou Minjie, Zhang Zhichao, Pan Ziqi, Zhu Feng
College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Research (Wash D C). 2025 Feb 24;8:0615. doi: 10.34133/research.0615. eCollection 2025.
The rapid evolution of deep learning has markedly enhanced protein-biomolecule binding site prediction, offering insights essential for drug discovery, mutation analysis, and molecular biology. Advancements in both sequence-based and structure-based methods demonstrate their distinct strengths and limitations. Sequence-based approaches offer efficiency and adaptability, while structure-based techniques provide spatial precision but require high-quality structural data. Emerging trends in hybrid models that combine multimodal data, such as integrating sequence and structural information, along with innovations in geometric deep learning, present promising directions for improving prediction accuracy. This perspective summarizes challenges such as computational demands and dynamic modeling and proposes strategies for future research. The ultimate goal is the development of computationally efficient and flexible models capable of capturing the complexity of real-world biomolecular interactions, thereby broadening the scope and applicability of binding site predictions across a wide range of biomedical contexts.
深度学习的快速发展显著提升了蛋白质 - 生物分子结合位点预测能力，为药物发现、突变分析和分子生物学提供了至关重要的见解。基于序列和基于结构的方法的进展展示了它们各自的优势和局限性。基于序列的方法具有效率和适应性，而基于结构的技术提供空间精度，但需要高质量的结构数据。结合多模态数据（如整合序列和结构信息）的混合模型的新兴趋势，以及几何深度学习的创新，为提高预测准确性提供了有前景的方向。这一观点总结了计算需求和动态建模等挑战，并提出了未来研究的策略。最终目标是开发出计算高效且灵活的模型，能够捕捉现实世界生物分子相互作用的复杂性，从而拓宽结合位点预测在广泛生物医学背景下的范围和适用性。
