Date: February 3, 2018 @ 1:00 pm – @ 3:30 pm
Location: Genentech Building 31, 310 DNA Way, South San Francisco CA, 94080

Big data for drug target discovery

Date: Feb 3, 2018, Saturday @ 1:00 pm – @ 3:30 pm
Genentech Building 31, 310 DNA Way, South San Francisco CA, 94080

If any company is interested in sponsoring, please contact 

When register, please enter your affiliation and name on your photo ID which is required at onsite check-in.

Please register online. Online registration is free. Onsite registration is $10.

1:00 - 1:30PM check-in, onsite registration and networking

1:30PM - 3:30PM


Michael J Keiser PhD, Assistant Professor at UCSF

The Keiser lab combines machine learning and chemical biology methods to investigate how small molecules perturb protein networks to achieve their therapeutic effects. Michael Keiser joined the UCSF faculty in the Dept. of Pharmaceutical Chemistry and the Institute for Neurodegenerative Diseases as an Assistant Professor in 2014, with joint appointments in the Dept. of Bioengineering & Therapeutic Sciences and the Institute for Computational Health Sciences. Before this, he co-founded a startup bringing systems pharmacology methods to pharma and the US FDA. During his bioinformatics Ph.D. at UCSF as a NSF Fellow, Michael developed techniques to relate drugs and proteins from the statistical similarity of their ligands, such as the Similarity Ensemble Approach (SEA). He also holds B.Sc., B.A., and M.A. degrees from Stanford University.

Talk title: Integrating uncertainty into drug-target deep learning

Bin Chen, PhD, Assistant Professor at UCSF

Dr. Bin Chen is an assistant professor in the Institute for Computational Health Sciences at University of California, San Francisco. Dr. Chen is also the founding member of DahShu, a non-profit organization to promote research and education in data sciences. Dr. Chen trained as a chemist in college, worked as a software engineer before graduate school, trained as a chem/bioinformatician in graduate school, worked as a computational scientist at Novartis, Pfizer and Merck. He received his PhD in informatics at Indiana University, Bloomington and pursed the postdoctoral training in Dr. Atul Butte’s lab at Stanford University. His lab is supported by the BD2K K01, one R21, one P01, one U24, and one grant from L’Oreal. His work is recently featured in UCSF News, STAT, GEN, GenomeWeb and KCBS. The liver cancer drug discovery work is featured in the UCSF Magazine Winter 2018 and the UCSF Cancer Year in Review: 2017. More info is available on his lab website (

Talk title: Big-Data Analysis Points Toward A New Cancer Drug Discovery Method

Online Free Registration – $0 (USD)