In this episode of The Chain, host Nimish Gera, Ph.D., vice president of biologics at Mythic Therapeutics, speaks with Peyton Greenside, CSO and co-founder of BigHat Biosciences, about the role of AI and machine learning in antibody engineering and targeting complex modalities, including bispecifics and ADCs. Greenside also discusses the work her organization does, what BigHat is doing to ensure the quality of data to train their AI models with, the methods of approaching harder targets such as GPCRs, and certain properties that AI can optimize better.

GUEST BIO

Peyton Greenside, CSO & Co-Founder, BigHat Biosciences 
A pioneer of deep learning applied to life science problems, Peyton has developed computational, statistical, and AI/ML techniques to model, understand, and optimize biological sequences in academia and industry. Peyton was an inaugural Schmidt Science Fellow, a computational biologist at the Broad Institute, a scientific founder of Valis, and holds a PhD from Stanford University (Accel Innovation Scholar), an MPhil in Computational Biology from Cambridge University (Herchel Smith Scholar), and a BA in Applied Math from Harvard.

MODERATOR BIO

Nimish Gera, Ph.D., Vice President of Biologics, Mythic Therapeutics 
Nimish Gera is the Vice President of Biologics at Mythic Therapeutics leading multiple projects to engineer and develop novel antibody and antibody-based drugs in oncology and immuno-oncology. Prior to Mythic, Nimish has over fifteen years of experience in antibody and protein engineering with five years leading bispecific antibody programs in several disease areas such as rare diseases, oncology, and immunology at Alexion Pharmaceuticals and Oncobiologics. Nimish received his PhD degree in Chemical and Biomolecular Engineering from North Carolina State University and a B.Tech degree in Chemical Engineering from Indian Institute of Technology, Guwahati.