Assessing the health of pharmaceutical R&D by unearthing hidden patterns in procurement data is a task made simpler by AI. At least, that’s the pitch given by David Qixiang Chen, Elvis Wianda, Liran Belenzon, and Tom Leung, who cofounded BenchSci in 2015. The Toronto, Canada-based biotech company taps AI to run experiments that accelerate drug discovery with the aim of increasing the speed and quality of medical research. This week, in a sign of confidence from new and existing backers, BenchSci raised $22 million in series B funding, bringing its total raised to $45 million.
Coinciding with the infusion of capital, BenchSci announced the launch of its new AI-assisted reagent selection product and expanded a contract with Novartis in a market it estimates is worth more than $10.2 billion per year. CEO Belenzon says the funds will be used to further develop BenchSci’s product suite and expedite drug testing.
“The pharmaceutical industry is facing a productivity crisis. R&D costs per drug keep rising while revenue is stagnant. Without major change, this crisis will affect everyone. Low or negative returns will reduce investment in new drugs,” said Belenzon. “Artificial intelligence promises to reverse the trend. But most AI in drug discovery is unproven. BenchSci’s products, on the other hand, have immediate, quantifiable impact.”
BenchSci’s marquee offering — an antibody selection service — employs machine learning to select antibodies in as little as 30 seconds (versus the 12 weeks antibody selection typically takes). The company claims it reduces consumable costs by up to $3 million per year by cutting down on inappropriate antibodies, with features that support search by protein targets and filtering by technique and 16 other experimental variables (including organism, tissue, cell type, and disease).
Above: BenchSci search
Belenzon says it’s often difficult for vendors to predict how an antibody will behave in experiments; up to 50% of selected antibodies don’t work, and data on antibody use is buried in biomedical papers, vendor catalogs, and independent validation databases. According to a study published in Nature, researchers can often spend $50,000 on unnecessary antibodies that take up to three to six months to develop, and it then takes days to select the antibodies and weeks to test and validate them.
BenchSci’s image recognition technology extracts antibody specifications from published experiments using AI — not just vendor names, product names, or SKUs. The system applies bioinformatics and ontologies to link antibodies to use cases while providing access to catalog data of more than 7.7 million products from 231 vendors, as well as literature-wide trends on antibody usage across technique, species, and more.
BenchSci draws on real-world experiment data from 10 million scientific publications, including closed-access papers. The results, the company says, are independently validated by organizations like the Human Protein Atlas, Encode, and EuroMAbNet, as well as scientific publishers such as Springer Nature and Wiley.
According to Belenzon, BenchSci now powers reagent selection for more than 31,000 researchers at over 3,600 academic institutions and 15 of the top 20 pharmaceutical companies. He says Novartis will be among the first customers to deploy the aforementioned AI-assisted reagent selection tool, which covers antibodies and recombinant proteins.
F-Prime Capital led this latest funding round, with participation from Northleaf Capital Partners and existing investors, including Gradient Ventures, Inovia Capital, Golden Ventures, and Real Ventures. F-Prime senior vice president Shervin Ghaemmaghami will join BenchSci’s board of directors.
Author: Kyle Wiggers.
Source: Venturebeat
