About half a million scientists spend over $3 billion a year buying research antibodies for experiments. They use these essential reagents to detect and quantify proteins.
Selecting antibodies slows the velocity of research and drug development. There are over 6,000,000 commercial antibodies, and vendors can't predict how each one will work in specific experiments. Data on antibody use is buried in biomedical papers, vendor catalogs, and independent validation databases. In part because this data is hard to find, up to 50% of selected antibodies don't work in experiments.
This wastes resources and delays research projects. Our work with pharmaceutical companies suggests each spends up to $1-3 million a year on commercial antibodies that do not work. Researchers also purchase unnecessary custom antibodies at $50,000 and 3-6 months to develop, spend days to select and weeks (sometimes months) to test and validate antibodies, and redundantly and unknowingly often validate the same antibodies as colleagues within the same organization.
Data to solve this problem is buried in scientific publications. Technological advances now allow it to be decoded. This includes advances in machine learning to better interpret text and images, increases in processing power to perform this analysis at scale, and improvements in graph databases to map and extract insights from results.
This has enabled huge benefits from AI-assisted antibody selection. These include reducing the hard cost of consumables up to $3 million per year, accelerating projects by selecting antibodies in 30 seconds versus 12 weeks, empowering organizational purpose by alleviating manual publication searches and restoring research time to scientists, and providing an immediate turnkey application of AI to increase organizational efficiency.
BenchSci is the leader in AI-assisted antibody selection. We collate the world’s largest collection of antibody-specific data; identify biological entities in their text and images with proprietary, antibody-specific machine learning models; map their relationships in a knowledge graph that incorporates bioinformatics databases and ontologies; and provide an intuitive interface to select antibodies by protein target, technique, and other experimental variables.
As the emerging industry standard, BenchSci now powers AI-assisted antibody selection for more than 26,000 researchers at more than 2,000 academic institutions and 15 of the top 20 pharmaceutical companies.