PEGS Boston Summit 참가자에 의한 추천사

“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.”
해당 컨퍼런스는 종료되었습니다.
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Engineering
공학 스트림
  • Display of Biologics
    바이오로직스의 디스플레이
  • Engineering Antibodies
    항체 공학
  • Machine Learning for Protein Engineering
    단백질 엔지니어링용 기계학습
Oncology
종양 스트림
  • Antibodies for Cancer Therapy
    암치료용 항체
  • Emerging Targets for Oncology & Beyond
    종양 이외 신규 타깃
  • Driving Clinical Success in Antibody-Drug Conjugates
    항체약물접합체(ADC)의 임상적 성공의 추진
Bispecific Antibodies
다중특이성 스트림
Immunotherpary
면역치료 스트림
  • Advances in Immunotherapy
    면역치료의 진보
  • Engineering Cell Therapies
    세포치료 공학
  • Next-Generation Immunotherapies
    차세대 면역치료
Expression
발현 스트림
  • Difficult-to-Express Proteins
    발현이 어려운 단백질
  • Optimizing Protein Expression
    단백질 발현의 최적화
  • Maximizing Protein Production Workflows
    단백질 생산 워크플로우의 최대화
Analytical
분석법 스트림
  • ML and Digital Integration in Biotherapeutic Analytics
    바이오의약품 분석에서 ML과 디지털 통합
  • Biophysical Methods
    생물물리학적 방법
  • Characterization for Novel Biotherapeutics
    신규 바이오의약품의 특성 평가
Immunogenicity
면역원성 스트림
Emerging Modalities
신규 치료 스트림
  • Biologics for Immunology Indications
    적응 면역용 바이오로직스
  • Radiopharmaceutical Therapies
    방사성 의약품 치료
  • Next-Generation Immunotherapies
    차세대 면역치료
Machine Learning Stream
기계학습 스트림
  • ML and Digital Integration in Biotherapeutic Analytics
    바이오의약품 분석에서 ML과 디지털 통합
  • Predicting Immunogenicity with AI/ML Tools
    AI/ML 툴에 의한 면역원성 예측
  • Machine Learning for Protein Engineering
    단백질 엔지니어링용 기계학습

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