PEGS Boston Summit 與會者的評價迴響

“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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顯示:

Engineering
工程組
  • Display of Biologics
    生物製劑展示
  • Engineering Antibodies
    抗體工程
  • Machine Learning for Protein Engineering
    用於蛋白質工程的機器學習
Oncology
腫瘤組
  • Antibodies for Cancer Therapy
    用於癌症治療的抗體
  • Emerging Targets for Oncology & Beyond
    腫瘤學以外的新興目標
  • Driving Clinical Success in Antibody-Drug Conjugates
    推動抗體藥物偶聯物 (ADC) 在臨床上的成功
Bispecific Antibodies
多特異性組
Immunotherpary
免疫療法組
  • Advances in Immunotherapy
    免疫療法的進步
  • Engineering Cell Therapies
    細胞治療工程
  • Next-Generation Immunotherapies
    下一代免疫療法
Expression
表達組
  • Difficult-to-Express Proteins
    難以表達的蛋白質
  • Optimizing Protein Expression
    優化蛋白質表達
  • Maximizing Protein Production Workflows
    最大化蛋白質生產工作流程
Analytical
分析法組
  • ML and Digital Integration in Biotherapeutic Analytics
    生物製藥分析中的機器學習和數位整合
  • Biophysical Methods
    生物物理性手法
  • Characterization for Novel Biotherapeutics
    新型生物治療藥物的表徵
Immunogenicity
免疫原性組
Emerging Modalities
新興治療學組
  • Biologics for Immunology Indications
    新興治療學組
  • Radiopharmaceutical Therapies
    放射性藥物治療
  • Next-Generation Immunotherapies
    下一代免疫療法
Machine Learning Stream
機器學習組
  • ML and Digital Integration in Biotherapeutic Analytics
    生物製藥分析中的機器學習和數位整合
  • Predicting Immunogenicity with AI/ML Tools
    使用 AI/ML 工具預測免疫原性
  • Machine Learning for Protein Engineering
    用於蛋白質工程的機器學習

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