PEGS Boston Summit 与会者的评价回响

“I enjoyed my time at PEGS! It was a superb meeting and well organized. I enjoyed most the variety of the program and easy navigation using the app. I think the presence of so many companies and their active engagement in the program make this conference different compared to other conferences.”

“For me, the PEGS conferences are an important and continuous source to develop new ideas for research and product development. Every visit is a deep dive into a world full of science, insights, and ideas I can discuss with so many scientists establishing new collaborations and networks. Based on these interactions and ideas, PEGS has been the beginning for a significant number of new R&D projects in my career.”


 “I never thought I'd enjoy networking, but PEGS is full of welcoming, innovative, accomplished people. It's exciting to learn from them and share ideas.”

“This is the summit of biologics where you learn from their early discovery to clinical advancement, and future directions.”

“It was great to see people coming back together, and I was able to make many new contacts. Cannot wait to see where they go, and I will definitely be back next year.”

“PEGSCELLENT!!!”

“It was a great experience to be back at in person conference at PEGS Boston 2022, the presentations were excellent, presenting a lot of novel research and highlighting the fantastic progress being made in biologics/cellular therapies. Highly recommend PEGS for future attendance.”

“PEGS was back in form this year with the in person event organized very well with all safety precautions. It was great to see many new and old colleagues and connecting with them. Short courses were a bonus!”

“#PEGS22 is THE opportunity to learn more about innovative approaches in the field of protein engineering.”

“I had a brilliant time attending #PEGS22. The conference gave me a unique opportunity to network with pharma companies from across the globe as well as hosting a wide range of speakers who are experts in their fields. I found it particularly interesting to see how the renaissance of machine learning in biology is being used to solve many problems including predicting affinity and immunogenicity of antibodies as well as protein structures using RoseTTAFold and Alphafold2.”

“A shout out to #PEGS22 , it was again an exceptional summit - so many new things learned, people met, conversations had - thank you, #pegsboston for hosting us.”

“PEGS offers a great opportunity to meet in person, something we very much missed over the last 2 years of the pandemic.”

“Particularly exciting for me was this year’s new conference stream on “Machine Learning Approaches for Protein Engineering”. Spearheaded by last year’s emergence of AlphaFold2, the field has seen tremendous progress in methods for structure prediction, antibody design, binder generation etc.”
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Engineering
工程组
  • Display of Biologics
    生物制剂展示
  • Engineering Antibodies
    抗体工程
  • Machine Learning for Protein Engineering
    用于蛋白质工程的机器学习
Oncology
肿瘤组
  • Antibodies for Cancer Therapy
    用于癌症治疗的抗体
  • Emerging Targets for Oncology & Beyond
    肿瘤学以外的新兴目标
  • Driving Clinical Success in Antibody-Drug Conjugates
    推动抗体药物偶联物 (ADC) 在临床上的成功
Bispecific Antibodies
多特异性组
Immunotherpary
免疫疗法组
  • Advances in Immunotherapy
    免疫疗法的进步
  • Engineering Cell Therapies
    细胞治疗工程
  • Next-Generation Immunotherapies
    下一代免疫疗法
Expression
表达组
  • Difficult-to-Express Proteins
    难以表达的蛋白质
  • Optimizing Protein Expression
    优化蛋白质表达
  • Maximizing Protein Production Workflows
    最大化蛋白质生产工作流程
Analytical
分析法组
  • ML and Digital Integration in Biotherapeutic Analytics
    生物制药分析中的机器学习和数位整合
  • Biophysical Methods
    生物物理性手法
  • Characterization for Novel Biotherapeutics
    新型生物治疗药物的表征
Immunogenicity
免疫原性组
Emerging Modalities
新兴治疗学组
  • Biologics for Immunology Indications
    新兴治疗学组
  • Radiopharmaceutical Therapies
    放射性药物治疗
  • Next-Generation Immunotherapies
    下一代免疫疗法
Machine Learning Stream
机器学习组
  • ML and Digital Integration in Biotherapeutic Analytics
    生物制药分析中的机器学习和数位整合
  • Predicting Immunogenicity with AI/ML Tools
    使用 AI/ML 工具预测免疫原性
  • Machine Learning for Protein Engineering
    用于蛋白质工程的机器学习

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