BenchSci’s
Evolution
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We taught a computer to read and understand experiments like a biologist
Our founders Tom Leung, David Q. Chen, Elvis Wianda, and Liran Belenzon met at the U of T Creative Destruction Lab in 2015. Together they built, tested, and validated an AI solution to the antibody reproducibility crisis. This captured the attention of Google’s AI fund Gradient Ventures, who led BenchSci’s A-round and raised $10M to develop the technology and the business.
Then built an enterprise AI solution to antibody selection
In 2017, BenchSci launched its first application, AI-Assisted Antibody Selection, to help scientists reduce experimental failure. The solution used both experiment-specific text ML and proprietary vision ML models that could understand the type of experiment being conducted. Next, our team then built relationships between those data points with proprietary bioinformatics ontologies. Best of all, we made these results searchable in an intuitive user interface.
Now we power research at the world’s biggest institutions
Within three years, more than 4,300 leading academic research institutions and 16 of the top 20 pharma companies were using our AI solutions. More than 50,000 scientists began relying on BenchSci to support their experiment decisions.
Project Butterfly: The world’s first evidence-backed map of disease biology
What scientists discovered was that BenchSci and our visual ML had built the first objective map of the underlying biology of disease. So, working in secret with top partners and global pharma R&D leaders, we launched Project Butterfly—an attempt to use this transformative technology to solve the biggest challenges in pre-clinical research. iNovia and TCV supported this initiative with a $50M Series C round.
Our next step: ASCEND
The portfolio success platform developed during Project Butterfly was given the name ASCEND, as it is designed to take the discovery and development of medicine to new heights. It augments scientists by giving them an unmatched understanding of disease biology in a platform that democratizes that understanding in all therapeutic areas. With ASCEND, our aim is to increase the speed and yield of the R&D portfolio by multiples, not percentages.
