About
Building the future of drug discovery with AI, human genetics, biobanks, and RWE.
I am Shicheng Guo, Ph.D., a statistical and computational geneticist and Senior Director of Translational Genetics & Data Science at Arrowhead Pharmaceuticals, and my vision is end-to-end, evidence-driven drug R&D — the next generation of drug discovery built as a closed loop in which population-scale human biobanks and human genetics nominate and validate targets grounded in causal biology, real-world evidence (RWE) and longitudinal electronic health records define the right disease, the right patients, and unmet need, AI and foundation models learn across genomic, multi-omic, clinical, and molecular data to design and prioritize candidates, and biomarkers link mechanism to measurable patient benefit so programs are de-risked early and matched to the patients most likely to respond — turning precision medicine from aspiration into a repeatable engineering discipline.
Focus Areas
AI and Data Science
Foundation models and ML over multi-omic and clinical data to prioritize targets and de-risk programs in the AI age of pharma.
Real-World Evidence (RWE)
Electronic health records and longitudinal real-world data linked to genetics for target validation and patient stratification.
Biobank-Scale Genetics
Human-genetics-driven target discovery and validation across UK Biobank, eMERGE, PMRP, and other population-scale cohorts.
Biomarker and Precision Medicine
DNA methylation, methylation haplotype blocks, and cell-free DNA for non-invasive detection and biomarkers.
Highlights
- Introduced a novel gene-based recessive diplotype approach that discovered FGF6 as a new iron-metabolism gene — published in Blood (2019).
- Contributed to tissue-of-origin mapping from cell-free DNA methylation — Nature Genetics (2017).
- Co-authored foundational methylome studies in Nature Biotechnology and PLoS Biology.