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Imagine growing your personal book collection. Adding more books affords you greater access to knowledge. But even the most dedicated bookworm can only read so much in one lifetime.

This is the plight of big data in pharma. The volume of human molecular data is growing exponentially faster than scientists can extract insight. Potentially significant findings remain buried and unused—like books collecting dust on an overstuffed shelf.

Unused data not only hampers innovation—it also hurts business. Today, developing drugs takes over 10 years and costs $2.6B on average.

Most concerning is the impact on patients. Higher development costs lead to increased retail prices, limiting affordability and access.

To escape this damaging trend, pharma must find a way to consolidate and distill the ever-growing body of scientific knowledge—a way to make big data useful. Luckily, one solution is already here: computational disease models.

At its most basic, a disease model is a platform that unifies and organizes all accessible data about a given disease, from its overall mechanism to the specific genes involved. Scientists use disease models to assist with decision-making in both preclinical and clinical development, such as determining target priorities or evaluating how a particular medication might affect different patient populations. Computational disease models take the guesswork out of drug development, shifting a notoriously costly and inefficient process to one that is faster, cheaper, and more accurate than ever before.

Today, the pharmaceutical industry has yet to harness all the information in the global library of scientific data—so much is still lost or untapped. But with the help of disease models, we’ll soon be able to extract useful knowledge from every book, every biological study, no matter how big or fast our library grows. Efficient drug development increases medicine affordability, freeing up budgets for neglected disease research and time for further scientific discovery. The next chapter in pharma R&D is here. And computational disease models are turning the page.