Over the past decade, the collection and availability of data have become one of the key trends helping to innovate the life science industry. Every minute, two research papers are published on PubMed, exponentially increasing the amount of data available for access. Throw in AI advancements, and you’d expect high-tech systems to be able to turn that research into drug discovery and development without breaking a sweat.

That hasn’t been the case. In fact, over the last decade, the industry has struggled to discover, handle, and produce new drugs. Clinical trial success rates have plateaued at below 10%, while the cost of bringing a drug to market has risen steeply. Two decades ago the average cost was $800 million. Today, it’s $2.3 billion.

In fairness, Big Pharma is seeking out AI talent, partly because boards are clamoring for more generative AI and data-driven processes, especially at the drug research and discovery stage. Yet, the healthcare industry simply doesn't have the same appeal as pure-play tech or biotech companies.

Unless that changes, the sector needs to use the right software so that less technically savvy workers can access and utilize these ever-expanding datasets. Companies have already stepped in to simplify that process, such as Seqera. Plus, without in-house tech experts, the industry has turned to external partnerships with expensive but capable AI-focused businesses, like BenevolentAI.

There seems to be an opportunity here, to build and scale software that enables existing pharma companies to double, triple, and quadruple their success rate. After assessing dozens of companies building at the frontier of AI and drug discovery, it became clear that companies were taking one of two approaches to do that.

One route is to sell results and discoveries. This approach sees companies leverage an underlying technology to sell “insights”, “simulations”, “validated targets” or “compounds” to pharma companies. This usually leads to significant revenue and royalties, as the stream aligns with Big Pharma’s current preference for outsourcing research to hedge the risk.

However, the challenge here is that the success of the “IP” is not necessarily up to the startup. Sometimes, pharmaceutical companies make decisions based on portfolio prioritization, which can include killing successful programs. On top of that, these bets are best suited to specialized life science, biotech, or healthcare investment funds because of the high level of sector knowledge and expertise involved.

A second approach is to deliver SaaS products for a narrow section of the drug discovery funnel. The challenge here is that companies need to target a very narrow segment of researchers. For example, out of the 2,000 researchers at AstraZeneca, only 200 focus on protein design. The pharma industry also has a hard psychological cap on how much it’s willing to pay on tooling its researchers. So for the startups trying to charge over €1 million a year, which isn’t outlandish given the amount of building needed, the solutions may be a hard sell.

In the current climate, software isn’t keeping up with the potential in research and development. That leaves a prime opportunity for the right solutions to emerge: software that, from the get-go, acts as a unified AI-powered workbench for scientists. The tech could allow them to collaborate and discover insights, as well as let them leverage LLM searching to scan through omics data, external papers, internal data, and AI-powered simulations. Such platforms could help accelerate the impact of AI in understanding and modifying biology — a requirement to improve targets and increase personalization of medicine.