检索结果-南通市图书馆

AB-Gen:Antibody Library Design with Generative Pre-trained Transformer and Deep Reinforcement Learning

Genomics, Proteomics & Bioinformatics 2023年第5期21卷 1043-1053页

作者： Xiaopeng Xu Tiantian Xu Juexiao Zhou Xingyu Liao Ruochi Zhang Yu Wang Lu Zhang Xin Gao Computational Bioscience Research Center(CBRC) King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia State Key Laboratory of Structural Chemistry Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou 350002China University of Chinese Academy of Sciences Beijing 100049China syneron technology Guangzhou 510000China Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry Fuzhou 361005China

Antibody leads must fulfill multiple desirable properties to be clinical *** due to the low throughput in the experimental procedure,the need for such multiproperty optimization causes the bottleneck in preclinical antibody discovery and development,because addressing one issue usually causes *** developed a reinforcement learning(RL)method,named AB-Gen,for antibody library design using a generative pre-trained transformer(GPT)as the policy network of the RL *** showed that this model can learn the antibody space of heavy chain complementarity determining region 3(CDRH3)and generate sequences with similar property ***,when using human epidermal growth factor receptor-2(HER2)as the target,the agent model of AB-Gen was able to generate novel CDRH3 sequences that fulfill multi-property ***,509 generated sequences were able to pass all property filters,and three highly conserved residues were *** importance of these residues was further demonstrated by molecular dynamics simulations,consolidating that the agent model was capable of grasping important information in this complex optimization ***,the ABGen method is able to design novel antibody sequences with an improved success rate than the traditional propose-then-filter *** has the potential to be used in practical antibody design,thus empowering the antibody discovery and development *** source code of AB-Gen is freely available at Zenodo(https://***/10.5281/zenodo.7657016)and BioCode(https://***/biocode/tools/BT007341).

关键词： Protein design Transformer Reinforcement learning Generative modeling Multi-objective optimization

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：