Incorporating Graph Neural Network with Diffusion-Based Generative Models for Antigen-Specific Antibody Design

• Item type: National Convention(1)
• 公開日: 2024-03-01
• タイトル: Incorporating Graph Neural Network with Diffusion-Based Generative Models for Antigen-Specific Antibody Design
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: コンピュータと人間社会
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: 東洋大
• 著者所属: 東洋大
• 著者所属: 東洋大
• 著者所属: 東洋大
• 著者名: Tuan, Nguyen Manh Duc; 本多, 泰理; 佐野, 崇; 中村, 周吾
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Antibodies are immune system proteins that protect the host by selectively binding to specific antigens, including viruses and bacteria. The binding between antibodies and antigens is predominantly hinges on the complementarity-determining regions (CDR) of the antibodies. While previous research has employed deep generative model that jointly models sequences and structures of CDRs based on diffusion probabilistic models and equivariant neural networks, these efforts have not explicitly incorporated protein structure. This study introduces a novel approach, leveraging the graph structure of proteins and employing an equivariant Graph Neural Network for Antibody design inside the diffusion probabilistic models. We conduct a series of extensive experiments to assess the efficacy of our approach, evaluating both the sequences and structures of designed antibodies. Our evaluation criteria include binding affinity measured by biophysical energy functions, alongside other pertinent protein design metrics.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00349328
• 書誌情報: 第86回全国大会講演論文集; 巻 2024, 号 1, p. 771-772, 発行日 2024-03-01
• 出版者 / 言語: ja
• 出版者: 情報処理学会