Drugs discovered in test tubes are failing patients.
The holy grail is to use data from in vivo experiments to discover drugs - but they can’t be generated at scale.
We have made in vivo experiments scalable. We are building a new way of doing discovery, starting from and guided by in vivo data.
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In vivo data + AI = Better targets and drugs that can treat more patients

Building the world's largest in vivo, single-cell atlas of how chemistry perturbs biology

cell
Millions
of Cells
cell
1000s
of Compounds
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100s
of Patients

Mosaic: The first platform to generate high-resolution in vivo data at scale

Single-cell resolution

Measuring both phenotypic and transcriptomic changes in cell states, we can precisely capture general rules of drug efficacy as well as drug-induced changes in gene expression.

One experiment, many patients

Leveraging our proprietary methods for pooling cells from 10s to 100s of diverse patients in one tumor and single-cell RNA profiling, we can determine drug action across all of those patients in one experiment. By using a mosaic of patient cells, we can better represent patient diversity in drug response earlier in the discovery process.

In vivo

In vitro models overlook the fact that disease occurs in living organisms. Our platform is the first to study drug-cell interactions in vivo – allowing us to uncover previously undetectable mechanisms of action and resistance.

AI models trained on our in vivo atlas that capture the diversity of patients

To uncover novel targets and drug candidates, undetectable by today’s models.

Founders

Nima Alidoust

CEO & Cofounder

Princeton Ph.D.,

Ex-Rigetti, McKinsey, 1QBit

Nima Alidoust
Johnny Yu

CSO & Cofounder

UCSF Ph.D.,

Ex-Biogen, Broad, Millenium

Johnny Yu
Hani Goodarzi

Cofounder

Princeton Ph.D.,

Associate Professor at UCSF

Hani Goodarzi
Kevan Shokat

Cofounder

Professor at UCSF,

UC Berkeley, HHMI

Kevan Shokat

Team

Daniele Merico
Daniele Merico

CompBio - Bioinformatics

UNIMIB Ph.D.,

Ex-Deep Genomics, Hospital for Sick Children, University of Toronto

Tal Ashuach
Tal Ashuach

CompBio - Bioinformatics & ML

UC Berkeley Ph.D.,

Ex-Patch Biosciences

Diego Borges
Diego Borges

CompBio - Software

MIT M.S.

Ex-Deepbiome, Goldfinch Bio

Shreshth Gandhi
Shreshth Gandhi

CompBio - Machine Learning

UofT M.Sc,

Ex- Deep Genomics

Casey Hutchison
Casey Hutchison

Operations

UofLouisville B.S.,

Ex-Federation Bio

Matt Jones
Matt Jones

CompBio - Bioinformatics

UCSF Ph.D., Stanford Postdoc

Ex-Google

Umair Khan
Umair Khan

CompBio - Machine Learning

UCSF Ph.D. Candidate

Graduate Student Researcher - Keiser Lab + Sirota Lab

Ian Lai
Ian Lai

Cancer Biology - Research

U of Iowa Ph.D.,

Ex- D2G Oncology

Jocelyne Lopez
Jocelyne Lopez

Biology - Research

UCSF Ph.D.

Tyler Miyasaki
Tyler Miyasaki

Biology - Research

UC Davis M.S.,

Ex-UCSF

Yasmin Sameni
Yasmin Sameni

Operations

SFU B.A.,

Ex-1QBit, Good Chemistry Company

Colin Tang
Colin Tang

Biology - Research

Weill Cornell Ph.D.,

Ex-Arsenal Bio

Neha Thakar
Neha Thakar

Biology - Research

Johns Hopkins University, B.S.

Airol Ubas
Airol Ubas

Biology - Research

Yale B.S.,

Ex-Genentech, Sutro Biopharma Intern

Nicole Thomas
Nicole Thomas

CompBio - Cancer Biology

SFU Ph.D.

Morin Lab at SFU

Aidan Winters
Aidan Winters

CompBio - Bioinformatics

UCSF Ph.D. Candidate, Goodarzi lab at UCSF, Luke Gilbert's lab at Arc Institute

Investors

News

Vevo Therapeutics Launches with Oversubscribed $12M Seed Financing to Discover Better Drugs Using Higher Resolution In Vivo Data Generated at Scale