BenchSci’s ASCEND platform supports researchers to discover biological connections, lowering the trial-and-error experimentation and identifying the associated risks early
Drug discovery solutions provider BenchSci has secured a C$95m ($70m) Series D funding round to expand its artificial intelligence (AI)-based drug discovery platform, ASCEND.
The funding round was led by Generation Investment Management. It also saw participation from existing investors TCV, iNovia Capital, Golden Ventures and F-Prime Capital.
With the recent fundraising, Canada-based BenchSci has now raised a total of C$218m ($170m).
BenchSci’s ASCEND platform supports researchers to discover biological connections, lowering the trial-and-error experimentation and identifying the associated risks early.
The Canadian firm said that its customers were able to find new targets and disease indications in 22% of key projects using this AI-driven drug discovery technique. The ASCEND platform also minimised its users’ unnecessary experimentation by 40%, thus proving its efficiency.
BenchSci CEO and co-founder Liran Belenzon said: “Thousands of scientists use our platform every day to move their most promising drug discovery projects forward faster. This investment is the 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’s 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.”
The ASCEND platform helps scientists by converting inaccessible, unstructured, and unstandardised experimental evidence from external and internal data sources into useful insights irrespective of the therapeutic field.
It links and decodes the complete history of available biomedical research, customer experimentation and procurement data.
BenchSci’s ASCEND then produces an unbiased and objective picture of the underlying biology of disease using a proprietary text- and image-based machine learning approach.
The firm said that this method helps to understand the feasibility of new or ongoing endeavours. It also provides insights to test them effectively and enhance translation to clinical trials.
This expedites the scientific conclusions that create more promising results by sparing scientists months and weeks of study and failed experiment time, BenchSci added.