Partnerships that Shape the Future: Why Collaboration Accelerates Drug Discovery
Accelerating drug discovery with AI and deep learning.
Breakthroughs in biomedicine happen when expertise and technology come together. Our collaboration with TME Pharma shows how AI-powered analytics can accelerate oncology research and open new paths for discovery.
It’s a vivid example of how combining strengths can push the boundaries of what’s possible in drug development. In fact, the pharmaceutical industry is increasingly embracing such alliances—approximately 9,000 new biopharmaceutical R&D partnerships were formed between 2005 and 2014, more than double the number in the previous decade.
AI, Automation, and the Cost of Complexity
No company transforms an industry alone, and this trend highlights a simple truth: breakthroughs in biomedicine demand collaboration between domain experts, data innovators, and technology builders. That’s why partnerships are central to how we work at aimed analytics.
Our platform was designed to remove one of the biggest barriers in drug discovery: the slow, costly, and often inconsistent process of analyzing complex biomedical data. Drug development remains notoriously time-consuming and expensive—it takes about a decade on average for a new drug to go from candidate to launch, and R&D costs for each successful drug now exceed $2–3 billion on average.
A major contributor to these high costs and long timelines is the sheer complexity of biomedical data analysis. Researchers must sift through massive omics datasets and experimental results, often spending weeks or months to get actionable insights. Moreover, traditional workflows can be error-prone: around 50% of preclinical studies are not reproducible, leading to an estimated $28 billion per year wasted on research that can’t be validated.
By bringing automation and AI-driven workflows into the research pipeline, we enable our partners to generate insights faster, reduce costs, and make their science more reproducible. Leading pharma companies have already seen what’s possible—by streamlining R&D with automation and data analytics, some have cut early development timelines by 40%, moving from drug candidate nomination to first-in-human trials in as little as 12–15 months.
👉 See how our platform accelerates time to trial.
Even greater gains are expected as AI is integrated into more steps of the process. In one example, AI-driven design helped produce a clinical drug candidate in under 12 months, a task that traditionally takes 4–5 years. These kinds of advancements illustrate how an AI-enhanced workflow can dramatically speed up discovery. Equally important, automation eliminates many repetitive manual tasks and reduces human error. For instance, in silico modeling and machine learning can replace certain lab experiments—potentially saving substantial time and resources—and have even been shown to quadruple the speed of some drug discovery steps like lead optimization. The result is a research process that is not only faster and cheaper, but also more reliable and reproducible.
This was the ”why” behind our partnership approach. Next, we explore the ”how”: a behind-the-scenes look at our collaboration with TME Pharma — and how AI-driven analytics are shaping the future of oncology research.