He mapped how proteins fold under pressure. Now he maps where biotech bets belong. The bench prepared him for the board room - he just never left the science behind.
Most people who spend four years studying G protein-coupled receptors at UCSF go on to write more papers about G protein-coupled receptors. Bryan Faust did not. He wrote the papers, got published in Nature, Science, and Cell - the academic trifecta that defines a career for most researchers - and then walked straight into Andreessen Horowitz to start writing checks instead.
That pivot is the whole story. Faust spent his PhD doing two things simultaneously: running biophysics experiments on how antibodies reshape protein conformation, and sitting in on deals at 5AM Ventures as an investment fellow. By the time he defended his thesis in 2022, he had one foot so far into venture that the question wasn't whether to join a16z - it was when.
At a16z's Bio + Health team, Faust operates at a level of technical depth that most investors cannot fake. When a founder walks in with a cryo-EM dataset or an ADC toxicity problem, Faust isn't nodding along waiting for the bottom-line slide. He's reading the data. His research background in antibody biophysics at Genentech and AbbVie Stemcentrx - before he even started his PhD - gave him a practitioner's intuition for what is real at the bench versus what looks good in a deck.
The portfolio reflects the thesis. Stipple Bio, whose $100M Series A Faust co-led in April 2026, is building a high-throughput "epitomics" platform to find tumor-specific epitopes invisible to current biologic therapies. Gate Bioscience attacks harmful extracellular proteins at their secretion source. Formation Bio sits at the pharma-technology intersection. These are not trend-chasing bets - they're bets rooted in a specific reading of where biology and computation are about to collide.
In January 2024, Faust co-authored a widely circulated a16z framework, "AI Jobs to Be Done in Life Sciences," mapping five categories where AI would meaningfully compress drug development timelines - from human pathway biology to biomanufacturing. The piece wasn't a hype piece. It read like something written by someone who has actually sat with the messy data and knows which problems are genuinely hard.
Faust hunts at the seed and Series A stage across three core categories: Consumer Health, BioTech, and Health IT. His sweet spot is the company that sits at an uncomfortable intersection - where the biology is genuinely hard and the software is what makes it tractable.
The trajectory wasn't accidental. Each stop added a layer - stem cell biology, protein chemistry, antibody biophysics, cryo-EM, GPCR pharmacology, and then venture - before Faust landed at the intersection of all of them.
In January 2024, Faust and his a16z colleagues Vijay Pande, Becky Pferdehirt, and Zak Doric published a framework laying out where AI will matter most in drug discovery and beyond. It wasn't a prediction - it was a map.
Before Faust could credibly question a founder's mechanism-of-action, he had to build one himself. His UCSF research spanned two distinct but connected questions: how antibodies manipulate protein conformation, and how G protein-coupled receptors select between signaling pathways. Both are problems at the heart of modern drug discovery.