Causaly, an AI platform for drug discovery and biomedical research, raises $60 million

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Artificial intelligence has been a major theme in the world of health and medical research, and particularly in the area of ​​drug discovery. Today, another hopeful in space announces a funding round to expand its contribution to the field. Causaly, a London startup that has built an AI platform to help researchers accelerate drug development and trials, has raised $60 million, a Series B that it will go to R&D and continue to build its team.

ICONIQ Growth, the growth-stage fund affiliated with the iconic investment firm of the same name, is leading the round, with previous backers Index Ventures, Marathon Venture Capital, EBRD, Pentech Ventures and the Visionaries Club also participating. The company has now raised $86 million in total and its valuation has not been disclosed.

Causaly is just over six years old and Yiannis Kiachopoulos, the CEO who co-founded the company with CTO Artur Saudabayev, said he already works with 12 of the world’s biggest pharmaceutical companies and some of the biggest names in the industry. medical research, including Gilead, Novo Nordisk, Regeneron, the Food and Drug Administration, and the National Institute of Environmental Health Sciences.

These organizations use its cloud-based platform to work through the different stages that go into drug development: identifying attractive targets for research and development; determination of specific biomarkers for these targets; and assisting pathophysiology to better understand a disease, in order to determine what could be resolved with the right medications and other therapies.

Kiachopoulos said that using the Causalys platform can cut the 10-15 years it might normally take to get an idea from goal to end of trials, down to about six years, a significant reduction in the budget that needs to be spent at the processes.

Equally important, its platform that allows for faster models and calculations based on different chemical permutations and how they work in different environments aims to reduce the number of false starts and dead ends that characterize the drug discovery process.

For every drug that hits the market, there are 9 that have failed, Kiachopoulos said, with a 90% failure rate. Each of these drugs typically cost between $1 and $2 billion to develop, according to research by the US National Institutes of Health. This gives us a real chance to accelerate and deliver benefits to patients and society.

The immense inefficiency in the biomedical research system is the classic kind of big data problem suitable for AI that can not only process large and multifaceted calculations in real time, but can be applied to read images to better understand the results on cells and more and this is one of the reasons why it has been a popular field not only among AI startups, but also among investors. Just yesterday, Recursion, an AI-powered drug discovery startup that has raised hundreds of millions of dollars in funding, announced its latest investment, a $50 injection from Nvidia that came with a important strategic partnership: Recursion would use Nvidia’s cloud platform to train its models on gigantic data sets.

That deal underscores the immense amount of money being pumped into the AI ​​drug discovery space, overall there have been billions invested in startups in the field, but something else is interesting to note as well.

I asked Kiachopoulos if computing power was also an issue for his startup, as this is indeed one of the big themes among AI startups right now, biomedical or otherwise, and his answer was a surprising no.

Only a very small fraction will go into computing resources, he said. This is partly due to the way Causaly was built and partly due to his role in the ecosystem. Six years ago, when we were starting the company, there weren’t any large language models, so what we’ve built isn’t starved of computing power. We were building natural language queries before Chat GPT, so now we didn’t need large language models.

He said he’s working on incorporating more of this into future products, but that it won’t have a noticeable impact on his computing needs.

With LLM it can be easier to query AIs. This is true and we are working on it. But you don’t need to train an LLM from scratch so we can take and tune what is there, and tuning requires far less computational resources.

The other detail this highlights is that Causaly itself isn’t in the drug discovery business—it provides tools for others who are. This is also something that differentiates Causaly from other startups in the industry.

Our solution helps biomedical teams, but we’re not developing our own therapies, he said. We are a SaaS-based platform, training our scientists to get the most out of our artificial intelligence. wWe have very strong partnerships and we are not competing, nor do we intend to.

With this round Caroline Xie, general partner of ICONIQ Growth, joins the board of startups.

The sciences are at an inflection point driven by the adoption of artificial intelligence, and we believe Causaly is a leader in providing this power to scientists in a highly reliable and verifiable way, he said in a statement. Causaly stands out to us as an extraordinarily powerful and user-focused platform that applies AI to drive significant productivity gains and business impact for many of today’s leading pharmaceutical companies. We are thrilled to support the entire Causaly team in their mission to revolutionize the way scientists find, visualize and collaborate on scientific evidence in pharmaceuticals, life sciences and beyond.

Causaly empowers scientists to solve the world’s greatest challenges like never before. It’s one of the clearest real-world applications of AI today, added Carlos Gonzalez-Cadenas, partner at Index Ventures. Already implemented by some of the largest pharmaceutical companies in the world, Causaly is actively accelerating biomedical research now. We’ve been really impressed with the level of adoption by leading research organizations, who continue to rapidly expand their spend on Causaly, underscoring the impact the technology is already having on R&D.

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