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论文地址：Multi-Scale Representation Learning for Protein Fitness Prediction 论文实现：https://github.com/DeepGraphLearning/S3F S3F：序列+结构+点云突变预测 Abstract 该论文研究了
论文地址：Pre-training Antibody Language Models for Antigen-Specific Computational Antibody Design 论文实现：https://github.com/KyGao/ABGNN ABGNN：预训练AbBERT+图网络R
论文地址：IgGM：A Generative Model for Functional Antibody and Nanobody Design 论文实现：https://github.com/TencentAI4S/IgGM IgGM：两阶段PLM+Diffusion生成抗体抗原结构 Abstra
论文地址：FlowDesign：Improved Design of Antibody CDRs Through Flow Matching and Better Prior Distributions 论文实现：https://github.com/nohandsomewujun/FlowDesi
论文地址：Flow Matching for Generative Modeling FlowMatching：固定条件概率路径的向量场训练CNF Abstract Flow Matching 是一种新的训练CNF的方法，通过回归固定条件概率路径的向量场来进行训练。这种方法可以使用广义的高斯概率路径
Yang Tan Phone: +86 15223128257 | Email: mashiroaugust@gmail.com Homepage: tyang816.github.io | Reseach: AI4Science | Github: tyang816 Education Shang
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