BenchSci, a pioneer in artificial intelligence (AI) solutions for preclinical life sciences research and development (R&D), announced a $95 million (US $70 million) Series D funding round, which was co-led by Generation Investment Management and included previous backers iNovia Capital, TCV, Golden Ventures, and F-Prime Capital. BenchSci has already raised a total of $218 million (or $170 million US) in investment.
The money will be used to enhance the business’ ground-breaking artificial intelligence (AI) drug discovery platform, ASCEND™ by BenchSci, which enables researchers to find biological linkages, significantly cut down on trial-and-error experimentation, and identify dangers early. BenchSci asserts that its software allows scientists to quicken the pace and improve research success during the pre-clinical drug-development process using proprietary text and image-based machine learning machine (ML) models.
ASCEND is an end-to-end SaaS (software as a service) platform that helps researchers find biological linkages, cut down on pointless trials, and identify dangers early on. ASCEND extracts experimental data from safe internal and public external sources using BenchSci’s machine-learning technology. The platform compares experimental results using curated ontology datasets. As a result, the software can produce a “map” of the biological processes underpinning various diseases based on evidence.
In order to support IND (investigational new drug) submissions for clinical translations, ASCEND can direct preclinical research by improving target selection, carrying out due diligence, generating hypotheses, developing the best investigative approaches, designing experiments, and identifying safety and efficacy risks.
In order to support IND (investigational new drug) submissions for clinical translations, ASCEND can direct preclinical research by improving target selection, carrying out due diligence, generating hypotheses, developing the best investigative approaches, designing experiments, and identifying safety and efficacy risks.
Early ASCEND users have noted gains in discovering new indications or targets (40%), as well as risks to safety or efficacy that increase R&D output (33%). If scientists hadn’t overlooked crucial information, needless experiments during preclinical programs might have been cut by at least 40%, according to retrospective evaluations of workflows at pharmaceutical corporations.
The pharmaceutical business has historically been inefficient at finding and developing new treatments, wasting a significant amount of time, resources, knowledge, and money. Furthermore, recent findings of biological complexity present a challenge for future research. There are few tools now available to assist scientists in navigating the vast amount of scientific data and evidence.
By creating an AI platform that streamlines the extraction and connection of insights from biological evidence to increase research efficiency, BenchSci hopes to close this gap at the preclinical stage. “At BenchSci, we share the goals of our partners to help patients find hope more quickly. Liran Belenzon, CEO and co-founder of BenchSci, stated that our part in overcoming this massive issue is to create and teach technology that can transform the world through the eyes and minds of scientists. “The groundbreaking aspect of it goes beyond the use of proprietary AI. The combination of cutting-edge technology, a wealth of knowledge in disease biology, and our collaboration with top pharmaceutical firms, which has the potential to accelerate the success of improved medicine for patients, is what makes ASCEND special.
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