Eliminate inefficiencies and errors in the entire reagent and model system selection process that cause costly experimental failure
Accelerate projects by selecting reagents and model systems in 30 seconds vs. 12 weeks
Reduce hard cost of consumables and save millions per year
Empower organizational purpose by restoring research time to scientists
See real business impact from AI with a proven, turnkey application
Backed by Google’s AI fund, Gradient Ventures, ASCEND is the industry standard for antibody selection.
Over 50,000 scientists in 16 of the top 20 pharmaceutical companies and more than 4,500 academic institutions use ASCEND to navigate antibody data to plan more successful experiments, with proven savings of millions per year in hard costs alone. But antibodies constitute just 40-50% of reagent failures.
What began with antibody selection now encompasses additional reagents as well as model systems, including:
Representing about 40-50% of reagent waste, with millions of products and hundreds of vendors
Often the second highest source of waste after antibodies, with a similar number of products and vendors
Challenging to search for, with many not cited by SKU, and a need to find through other means such as sequence
Critical tools whose common misidentification and misuse can have a devastating impact on research projects
One of the most important new gene-editing technologies, with the need to select vectors, nuclease, guide RNA, screening libraries, and trans-activating RNA
Time-consuming to find—scientists often consult many papers to identify a model that has been successfully utilized in experiments similar to their own
A crucial advancement in molecular biology that enables scientists to better understand a specific gene function
Our data sources include:
Real-world experiment data from 16 million scientific publications, including closed-access papers
Partnerships with leading scientific publishers including Springer Nature and Wiley
Independent validations from organizations including The Human Protein Atlas, EuroMAbNet, and Encode
Vendor catalog data for more than 75 million products from 400 vendors
With ASCEND users can:
Easily search using key experiment criteria, such as protein target, reagent or model system type, or species.
Filter by experiment method, disease, and other criteria dependent on the reagent or model system type
Select experiment-specific reagents and model systems in minutes
Using our proprietary machine learning technology, users can:
Unlock the value of figures with image recognition technology that can interpret their text and meaning
Decode reagent and model system specifications and success from published experiments by reading them like a Ph.D. biologist—not by just looking for vendor names, product names, or SKUs
Connect reagents and model systems to use cases and biomedically relevant concepts with advanced bioinformatics and ontologies that overcome challenges with entity homonymy and synonymy