Thermo Fisher Scientific has signed an agreement with reagent intelligence platform BenchSci to use its trademarked machine learning to mine antibody data published in peer-reviewed scientific journals and display it alongside associated application information on product-specific webpages.

Thermo Fisher aims for this collaboration to help scientists make informed decisions about the optimal antibodies to use in experiments.

An image gallery on the company’s antibody product pages will incorporate data generated by BenchSci’s platform so website visitors can view internal product development data and peer-reviewed journals’ figures, all in one location. Additional published figures will be added over time.

Critical research delays can be caused by poor antibody specificity or application performance as these issues significantly hinder the ability to obtain good results. Choosing the wrong antibody or one that underperforms can result in a lack of reproducibility, wasted time and wasted resources, Thermo Fisher said in a news release. Therefore, researchers need antibodies that bind to the right target and work in their applications every time.