Zhiwei Nie
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  • Projects
    • E2VD
    • PepGenWOA
    • S3C
    • S3AI
    • SARS-CoV-2_mutation_simulation
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    • E2VD: a unified evolution-driven framework for virus variation drivers prediction
    • Towards anchoring evolutionary fitness for protein stability with virtual chemical environment recovery
    • Interpretable antibody-antigen interaction prediction by introducing route and priors guidance
    • Hunting for peptide binders of specific targets with data-centric generative language models
    • ProtFAD: Introducing function-aware domains as implicit modality towards protein function perception
    • Semantic knowledge graph as a companion for catalyst recommendation
    • ACM Gordon Bell Special Prize Finalist - Running ahead of evolution—AI-based simulation for predicting future high-risk SARS-CoV-2 variants
    • Automating materials exploration with a semantic knowledge graph for Li-ion battery cathodes
    • Encoding the atomic structure for machine learning in materials science
    • Construction and application of materials knowledge graph based on author disambiguation: revisiting the evolution of LiFePO4
    • Generative models for inverse design of inorganic solid materials
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E2VD

Jun 1, 2024 · 1 min read
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A unified evolution-driven framework for virus variation drivers prediction.

Last updated on Jun 1, 2024
聂志伟
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聂志伟
PhD Student
PepGenWOA Jun 1, 2024 →

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