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UID:luma-e9d3041d35@palaner.app
DTSTAMP:20260802T151003Z
DTSTART:20260807T003000Z
DTEND:20260807T020000Z
SUMMARY:Reimagining Proteins with AI: Antibodies and Beyond
LOCATION:MBC BioLabs
DESCRIPTION:Saved in Palaner as: saved\n\nPalaner match: 17% · why: matches
  your usual pattern\n\nAI has changed what’s easy in protein engineering. 
 Structure prediction\, de novo binder and antibody design\, developability
  and immunogenicity triage\, sequence optimization - work that took years 
 now takes months and even days. The harder question is what that actually 
 buys us. A pipeline moves at the speed of its slowest step\, and a step ge
 tting cheaper only shortens the timeline if that step was the one holding 
 things up.\nOur discussion takes that question seriously across the modali
 ties defining the field today - antibodies\, ADCs\, nanobodies - and look 
 into the future of engineered proteins: transcription factors\, enzymes an
 d proteins with previously unseen properties. Walking from sequence to cli
 nic\, we look at each step and ask how AI already deliver on safer bets li
 ke antibodies and where there is an opportunity to shack up the field of p
 rotein design to accelerate research\, answer biological questions and exp
 and treatment options.\nMeet the speakers:Kavita Kulkarni is a Principal P
 M for AI Research at Biohub\, where she works on biological foundation mod
 els. Previously at Google Research\, she was Global Head for Scientific co
 llaborations in Biomedical Intelligence and co-invented the AI Co-Scientis
 t\, in addition to contributing to MedPaLM\, Med-Gemini\, and AMIE. She ho
 lds Stanford graduate fellowships in BioDesign & Innovation and Biomedical
  Informatics\, and her work sits at the intersection of AI\, biology\, and
  clinical impact.\nNikita Savelyev is MSAT scientist with 8 years of exper
 tise in production of biologics\, in particular ADCs. He brings up experti
 se in optimizing ADC production\, in particular - payload and linker chemi
 stry and bioconjugation.\nRIco Meinl is product lead for applied AI team a
 t Retro Biosciences. His team designed new versions of Yamanaka factors to
  convert somatic cells into stem cells more efficiently. He and his team n
 ow integrate AI and robotics to engineer transcription factors with the go
 al to develop therapies that extend human health and lifespan.\nJames Luca
 s is Associate Director of Computational Protein Generation at Generate:Bi
 omedicines\, where he leads de novo protein design strategy and a team dev
 eloping scalable computational approaches for therapeutic discovery. His w
 ork connects generative models with computational evaluation and high-thro
 ughput experimental testing\, enabling rapid design-build-test-learn cycle
 s. Before joining Generate\, James was a computational protein design scie
 ntist at Nautilus Biotechnology. He holds a Ph.D. in Bioengineering from U
 C San Francisco and UC Berkeley. His interests include model-guided protei
 n engineering and translating advances in machine learning into experiment
 ally validated protein therapeutics.
URL:https://luma.com/2qiy3z11
CATEGORIES:palaner,saved
STATUS:TENTATIVE
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