BenchSci Raises $95 Million Series D Funding to Enable Drug Discovery Innovation at Scale With its Groundbreaking AI Platform ASCEND
ASCEND by BenchSci harnesses proprietary text and image-based machine learning models, empowering scientists to dramatically improve the speed and success of research at any stage of pre-clinical drug development
BenchSci, a world leader in AI solutions for preclinical life sciences research and development (R&D), announced a $95 million (US $70 million) Series D funding round led by Generation Investment Management, with participation from existing investors iNovia Capital, TCV, Golden Ventures and F-Prime Capital. The most recent funding brings BenchSci’s total funds raised to $218 million (US $170 million).
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“Previously, a scientist typically had to spend hundreds of hours to understand existing experiments and conclusions. With BenchSci’s models, scientists can find the same answer in minutes, accelerating drug discovery and reducing the time and cost to develop potentially life-saving drugs.”
The funds will be used to expand the company’s revolutionary AI drug discovery platform, ASCEND™ by BenchSci, which enables scientists to discover biological connections, dramatically reduce trial-and-error experimentation and uncover risks early. ASCEND users from BenchSci’s top 10 customers were able to uncover novel targets and disease indications in 22% of key projects and reduced unnecessary experimentation by 40%, contributing to a new era of more efficient, AI-driven drug discovery.
“Our focus will always be on what will drive the greatest impact for our customers,” said Liran Belenzon, CEO and Co-Founder of the Canadian-based BenchSci. “Thousands of scientists use our platform every day to move their most promising drug discovery projects forward faster. This investment is validation of the incredible work of our team and the traction we have in the market with the largest pharmaceutical and biotech companies in the world. With the industry 98% failure rate advancing medicines to patients in need, we’re grateful to partner with Generation who shares our thesis that purpose built, innovative AI must be deployed at scale within pharmaceutical R&D to solve the productivity crisis and bring novel medicines to patients years faster.”
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BenchSci’s ASCEND platform empowers scientists at every stage of preclinical research, regardless of therapeutic area by turning inaccessible, unstructured, and unstandardized experimental evidence from external and internal data sources into actionable insights. Using a proprietary text and image-based machine learning approach, ASCEND decodes and connects the entire available history of biomedical research, combined with customer experimentation and procurement data, to create an unbiased and objective map of the underlying biology of disease. This approach not only helps scientists understand the feasibility of new or existing pursuits but also provides insights on how they can effectively test them and improve translation to clinical trials. This ultimately saves scientists weeks and months of investigation and failed experiment time, expediting the critical scientific conclusions that produce more promising pursuits from the onset. Today, BenchSci’s software is used by 16 of the top 20 pharmaceutical companies and by over 50,000 scientists working at more than 4,500 leading research centers worldwide.
“We believe more effective preclinical research is critical to a sustainable healthcare system – drug development is expensive and this cost is only increasing. BenchSci is an example of how AI can support existing drug development by helping scientists focus their research on the most promising areas of science,” said Anthony Woolf, Growth Equity Partner at Generation Investment Management. “Previously, a scientist typically had to spend hundreds of hours to understand existing experiments and conclusions. With BenchSci’s models, scientists can find the same answer in minutes, accelerating drug discovery and reducing the time and cost to develop potentially life-saving drugs.”
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