Artificial intelligence has been a big topic in the world of health and medical research, and specifically in the area of drug discovery. Another contender in this space is causally A London startup that has created an AI platform to help researchers accelerate drug development and testing, has raised $60 million, a Series B that will go toward R&D and continuing to build its team.
ICONIQ Growth, the growth phase fund affiliated with the ICONIQ Capital, leads the round, with participation from previous backers Index Ventures, Marathon Ventures, EBRD, Pentech Ventures and Visionaries Club.
Causaly is just over six years old, and Yiannis Kiachopoulos, the CEO who co-founded the company with CTO Artur Saudabayev, said it already works with 12 of the world's largest pharmaceutical companies and some of the biggest names in medical research, including Gilead, Novo Nordisk, Regeneron, the Food and Drug Administration, and the National Institute of Environmental Health Sciences.
These organizations use its platform based in the cloud to work on the different stages involved in drug development: identifying interesting targets for research and development; determine biomarkers that are specific to those targets; and assist in pathophysiology to better understand a disease to determine what could be addressed with appropriate pharmaceuticals and other therapies.
Kiachopoulos said using Causaly's platform can reduce the 10 to 15 years it typically takes to take an idea from target to end of testing to about six years, a significant reduction in the budget that must be dedicated to this. process.
Just as important, its platform, which enables faster modeling 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 fail,” Kiachopoulos said, estimating a 90% failure rate. The development of each of these drugs usually costs between 1.000 and 2.000 million dollars, according to the investigation from the US National Institutes of Health. “This gives us a real opportunity to accelerate and deliver benefits to patients and society.”
The immense inefficiency in the biomedical research system is the classic type of big data problem that is suited to AI, which can not only process large, multifaceted calculations in real time, but can also be applied to read images for better understanding. the results in cells among other aspects, and that is one of the reasons why it has been a popular field not only among AI startups, but also among investors. For example, recursion — an AI-based drug discovery startup that had already raised hundreds of millions of dollars — in a latest investment ad, another $50 injection from Nvidia that came with a major strategic partnership: recursion would use Nvidia's cloud platform to train its models on giant data sets.
That deal underscores the immense amount of money being pumped into the drug discovery space using AI; Overall, billions have been invested in startups in this field.
Asking Kiachopoulos if computing power was also an issue for his startup, given that this is one of the big topics among AI startups right now, biomedical or otherwise, and his answer was a surprising "no." .
“Only a very small fraction will go to computing resources,” he said. This was partly due to how Causaly was built and partly due to his role in the ecosystem. “Six years ago, when we started the company, there were no big language models, so what we've built doesn't have a lot of computing power. We were building natural language queries before Chat GPT, so now we didn't need big language models.”
It said it is working to incorporate this technology into future products, but that this would not have a noticeable impact on its computing needs.
“With LLM it can be easier to consult AIs. It's true and we are working on it. But you don't need to train an LLM from scratch so we can take and fine-tune what's there, and fine-tuning doesn't involve a waste of computing resources.”
The other relevant detail is that Causaly itself is not in the drug discovery business: it is providing tools to others who are. This is also something that sets Causaly apart from other startups in the field.
“Our solution helps biomedical teams, but we are not developing our own therapies,” he said. “We are a SaaS-based platform, empowering our scientists to make the most of our AI. We have very solid alliances and we do not compete, nor do we have plans to do so.”
With this round, Caroline Xie, general partner at ICONIQ Growth, joins the startup's management team.
“Science is at an inflection point driven by the adoption of AI, and we believe Causaly is a great leader in delivering this power to scientists in a highly reliable and verifiable way,” he said in a statement. “Causaly stands out to us as an exceptionally powerful, user-oriented platform that applies AI to drive significant productivity and business impact benefits for many of today's leading pharmaceutical companies. “We are delighted 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 gives scientists the power to solve the world's biggest challenges like never before. It is one of the clearest real-life applications of AI today,” added Carlos González-Cadenas, partner at Index Ventures. “Causaly, which has already been implemented by some of the world's largest pharmaceutical companies, is now actively accelerating biomedical research. We're really impressed with the level of adoption by leading research organizations, who continue to rapidly expand spending on Causaly, underscoring the impact the technology is already having in R&D”.
