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 seque...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.展开更多
基金supported by the National Natural Science Foundation of China(82373790,22220102001,U1909208,and 81872798)Natural Science Foundation of Zhejiang(RG25H300001 and LR21H300001)+7 种基金National Key R&D Program of China(2022YFC3400501)Leading Talents of“Ten Thousand Plan”National High-Level Talents Support Plans of China,The Double Top-Class Universities(181201*194232101)Fundamental Research Funds for Central Universities(2018QNA7023)Key R&D Program of Zhejiang Province(2020C03010)Westlake Laboratory(Westlake Laboratory of Life Science&Biomedicine)Alibaba CloudInformation Technology Center of Zhejiang UniversityAlibaba-Zhejiang University Joint Research Center of Future Digital Healthcare.
文摘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.
