English(EN) DuaDeep-SeqAffinity: Dual-Branch Deep Learning for Tri-Stream Sequence-Based Antibody--Antigen Affinity Prediction

新型深度学习模型可根据序列预测抗体-抗原结合亲和力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

研究人员开发了DuaDeep-SeqAffinity，一个新颖的深度学习框架，旨在直接从氨基酸序列预测抗体-抗原结合亲和力。该方法通过独立处理抗体和抗原序列，绕过了对昂贵且稀缺的3D结构数据的需求。该框架利用具有ESM-2嵌入、Transformer和CNN组件的双分支架构，在AbRank基准测试上取得了强劲的性能，并证明了对关键结合区域的优先关注。 AI

影响该模型为高通量抗体筛选提供了一个可扩展、无结构工具，有望加速药物发现和开发。

排序理由该集群包含一篇详细介绍用于特定科学任务的新深度学习模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Aicha Boutorh, Soumia Bouyahiaoui, Manel Kara Laouar, Sara Belhadj, Nour El Yakine Guendouz, Asma Boutorh · 2026-09-04 04:00

DuaDeep-SeqAffinity: 用于三流序列抗体-抗原亲和力预测的双分支深度学习

arXiv:2512.22007v2 Announce Type: replace Abstract: DuaDeep-SeqAffinity is a sequence-only deep learning framework that predicts antibody--antigen binding affinity directly from primary amino acid sequences, avoiding the cost and scarcity of resolved three-dimensional structures.…

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DuaDeep-SeqAffinity: 用于三流序列抗体-抗原亲和力预测的双分支深度学习

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