Health is our most precious asset, and life scientists help protect it. Thanks to public health and pharmaceutical breakthroughs, life expectancy has doubled since 1900. Today, we take treatments like antibiotics, vaccines, insulin, and chemotherapy for granted. But they all took effort by thousands of scientists over decades to research and develop.
At BenchSci, this problem wasn't theoretical. Our founder, Tom Leung, experienced it first-hand in 2015 while researching cancer. An inappropriate antibody caused him to lose rare patient samples. Could machine learning prevent this in future? And help him choose the appropriate antibodies? He contacted machine learning and data experts at the University of Toronto to find out. These experts, David Q. Chen and Elvis Wianda, joined him to solve the problem. Our fourth cofounder, Liran Belenzon, met the team at the Creative Destruction Lab. Together, they built, tested, and validated a solution to preclinical research waste. The solution used advances in deep learning to teach a computer how to read and think like a Ph.D. biologist.
BenchSci launched in July 2017 with our first application, AI-Assisted Antibody Selection. It helped scientists select the appropriate antibodies faster, reducing experimental failure. How? First, we collect relevant open- and closed-access scientific papers and product catalogs. Second, we extract relevant data points from them with proprietary machine learning models. Third, we build relationships between the data points with proprietary bioinformatics ontologies. Finally, we make the results searchable in an intuitive interface.
The response to AI-Assisted Antibody Selection exceeded our loftiest ambitions. Within two years, more than 3,600 research institutions and 15 of the top 20 pharma companies used it. More than 31,000 scientists began relying on BenchSci for their experiments. Based on this response, and the impact of our work, we earned the trust of investors. This includes Gradient Ventures, Google's AI fund.
Today, we're building on our success with antibodies to address other critical reagents—including recombinant proteins, siRNAs, and CRISPR tools—as well as model systems such as animal models and cell lines. We're also developing other machine learning-powered applications to reduce preclinical research waste. In everything we do, we're guided by our mission and hope for the future. By helping scientists be more successful, we can improve R&D productivity. And that means continued good health for all of us.