首页|AB-Gen:Antibody Library Design with Generative Pre-trained Transformer and Deep Reinforcement Learning
AB-Gen:Antibody Library Design with Generative Pre-trained Transformer and Deep Reinforcement Learning
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Antibody leads must fulfill multiple desirable properties to be clinical candidates.Pri-marily due to the low throughput in the experimental procedure,the need for such multi-property optimization causes the bottleneck in preclinical antibody discovery and development,because addressing one issue usually causes another.We 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 agent.We showed that this model can learn the antibody space of heavy chain complementarity determining region 3(CDRH3)and generate sequences with similar property distributions.Besides,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 constraints.Totally,509 generated sequences were able to pass all prop-erty filters,and three highly conserved residues were identified.The 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 task.Overall,the AB-Gen method is able to design novel antibody sequences with an improved success rate than the tra-ditional propose-then-filter approach.It has the potential to be used in practical antibody design,thus empowering the antibody discovery and development process.The source code of AB-Gen is freely available at Zenodo(https://doi.org/10.5281/zenodo.7657016)and BioCode(https://ngdc.cncb.ac.cn/biocode/tools/BT007341).
Protein designTransformerReinforcement learningGenerative modelingMulti-objective optimization
Computational Bioscience Research Center(CBRC),King Abdullah University of Science and Technology,Thuwal 23955-6900,Saudi Arabia
State Key Laboratory of Structural Chemistry,Fujian Institute of Research on the Structure of Matter,Chinese Academy of Sciences,Fuzhou 350002,China
University of Chinese Academy of Sciences,Beijing 100049,China
Syneron Technology,Guangzhou 510000,China
Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry,Fuzhou 361005,China
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Office of Research Administration(ORA)King Abdullah University of Science and Technology(KAUST)Saudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaNational Natural Science Foundation of China
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