Any research scientist who works with antibodies has experienced the pain that we at BenchSci are trying to eliminate - failed experiments!

In our previous lives as bench scientists, poor antibodies caused us much distress: loss of samples, and wasted time and money. We still remember the days when we bought antibodies that would just not work. We would then kick them underneath the fridge to hide them to pretend they never existed. It is really a love-hate relationship; as much as we all love to use them in our experiments, their innate unpredictability caused many of our experiments to fail.

As an attempt to mitigate the risk of using a bad antibody, we always turned to peer reviewed papers. We spent hours and sometimes even days reviewing papers and figures just to find that one antibody that has been proven to work by other scientists. One day our PI asked us to review every single paper available before buying a specific antibody for our experiment. That was the last straw! How is it possible that in 2014 (when BenchSci was conceptualized) we could not find antibody validation information within seconds? Throughout the next two years, a team of bench scientists, computational biologists, machine learning experts, bioinformaticists and experienced entrepreneurs all came together to solve this problem.

BenchSci was born.

So what is BenchSci?
BenchSci is a free platform that allows scientists to find figures containing antibodies usage information within seconds. We allow researchers to filter millions of figures by proteins, techniques, tissues, cell lines, and more. We understand just how disappointing it is to find things like “this antibody is a generous gift from this lab” or “please refer to a previous publication for antibody information.”. Therefore, we only include papers that cite the specific antibody that was used. Our platform covers over 2 million unique antibodies from over 150 vendors and provides figures that contain over 2 million data points on antibody usage. Say goodbye to the days of spending hours researching which antibodies to buy!

How did we do it?
Well, it wasn't simple. It took us almost 2 years! We built a machine learning software that reads papers like a scientist. Our software extracts figures to understand the context around them (protein, technique, tissue, etc). We created a massive database and assembled it into an awesome platform with an amazing user interface.

Why are we doing this?
Because we care about scientific discoveries. But to be honest, we also care about our own suffering. We have experienced this problem first hand. After wasting too much time and money on failed experiments, we just wanted to put an end to it and make sure that the scientific community can, from this moment onward, find the antibody they want for experiments with ease.

So what is next?
We will be officially launching BenchSci this November. So far over 500 labs from Stanford University, University of Toronto, McGill University, Mount Sinai Hospital, and Sick Kids Hospital have signed up to our official launch. If you would like us to notify you about our launch, please sign up here.

We are very excited for what's to come and hope you love our platform!

-BenchSci Team

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Written By:
Tom Leung, Ph.D. (he/him)