Closing the data loop in AI-driven drug discovery

Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage.

Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law .

Today, bringing a new drug to market takes an average of 10-15 years and costs anywhere from $1 billion to $2.5 billion , with failure rates upward of 90%. AI has become the pharmaceutical industry’s biggest bet on bringing success rates up and timelines down.

The faster drug companies can identify, test, and optimize new chemical compounds, the lower the risk of costly failures later in development. “The main cost in drug discovery is still the clinical phase, so trying to reduce risk and increase your success rates there is obviously hugely beneficial,” says Paul Belcher, director of protein research strategy at global life sciences company Cytiva. “AI is one approach that drug companies hope will not only save time and compress timelines, but enable better quality candidates to reach the clinic.” Early use of AI in drug discovery shows potential, but also highlights the need for robust and authentic data, as

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  Closing the data loop in AI-driven drug discovery