Track 2 - January 22 11.00 A.M.-11.30 A.M.
Epitopes are the foundation of efficacy in therapeutic antibody discovery. Antibody discovery is severely limited in the targets and epitopes that can be accessed with traditional practices. Advances in molecular simulation and machine learning can over-come these limitations and enable discovery of on-epitope antibodies for traditionally challenging targets.
Session Chair Profile
Ph.D., Co-Founder & V.P. Technology, RubrYc Therapeutics
Biography
Dr. Greving has a passion and an awarded record for innovation in computation and laboratory integration. At RubrYc, he leads development of a unique machine learning backed epitope-selective antibody discovery platform for challenging targets. Dr. Greving founded his first company 20 years ago that developed software to deliver legacy mainframe data and algorithms to modern systems. Dr. Greving received a Ph.D. in Biochemistry in 2010. His thesis research helped establish the “Immunosignature” technology. Dr. Greving completed his metabolomics post-doctoral research at Scripps Research. Subsequently, Dr. Greving founded Nextval that developed a high-throughput mass spectrometry screening technology. These developments resulted in two prestigious awards: Society for Lab Automation and Screening (SLAS) 2012 “Innovation of the Year”, and R&D Magazine’s 2013 “Oscar of Innovation”. After Nextval, Dr. Greving joined HealthTell to commercialize the Immunosignature diagnostic. He then integrated HealthTell’s library platform with molecular simulation and machine learning to build RubrYc’s discovery platform.
Speaker Profile
Ph.D., Cofounder, CIO, ATUM
Biography
As ATUM’s Co-founder and CIO, Dr. Sridhar Govindarajan leads the company's automation, machine learning and protein engineering efforts. He has more than 25 years of scientific computing experience. Prior to his current venture, Govindarajan led the computational research in optimizing directed evolution technologies at Maxygen, Inc., and was a Systems Architect at EraGen Biosciences. Dr. Govindarajan has an undergraduate degree from the Indian Institute of Technology (IIT, Mumbai) and received his PhD in Computational Chemistry/Biophysics from the University of Michigan's Department of Chemistry.
Speaker Profile
Ph.D., Machine Learning Scientist, RubrYc Therapeutics
Biography
Dr. Taguchi is an interdisciplinary scientist working at the intersection of biochemistry and artificial intelligence (AI). He is an author on more than 20 publications in peer-reviewed journals, an inventor on several machine learning patents, and has received a Ph.D. in Biophysics and Computational Biology for his research on magnetic resonance spectroscopy of protein complexes. Dr. Taguchi was awarded two postdoctoral fellowships, one from the National Institutes of Health to support his research at the Massachusetts Institute of Technology (MIT), and another from the Japan Society for the Promotion of Science for international studies in Japan. At MIT, Dr. Taguchi built machine learning algorithms for automating multi-dimensional signal processing tasks. He won two AI hackathon competitions hosted by MIT, leading him to co-found a startup called MatchLab that uses machine learning to standardize the dermatological image collection process. Dr. Taguchi currently develops AI technologies for antibody discovery at RubrYc Therapeutics.
