Yet it won’t happen overnight. In 20 years perhaps, the pharmaceutical industry may be able to develop a drug that relies heavily on genomics, high-throughput screening and computer-aided drug design, but the integration of informatics will require a complete change in the culture of the pharmaceutical industry. "It’s going to take a lot of experimental work to convince the researchers to look at, say, a gene expression study and trust what they’re seeing," explains Blevins, who hopes to eventually use gene expression studies to screen Merck’s entire compound collection.

Leaders in the emerging field stress they will never be able to fully develop a drug using only computers, as some analysts have suggested. "That’s nonsense," Fasman of AstraZeneca says. "The reality is that scientific experimentation started out in vivo, in real animals and plants. The big advance was when we moved to test tubes. But in vitro didn’t replace in vivo; it added on to it. It’s the same thing with in silico drug development. It simply gives us another way to approach the problem."

Eventually, however, there should be a symbiotic relationship between science and technology. Pfizer, which has released eight successful drugs in the past five years, hopes to double the output of its drug discovery efforts as a result of bioinformatics integration. But thus far company officials say there have been no measurable returns.

The significant time and costs savings may not come until these companies have an end-to-end, IT-driven research and development solution. Analysts and executives agree that it will be another three or four years before this technology starts to have an effect on drugs already in the clinics and 10 to 12 years before there’s a real effect on the entire drug discovery process. "So far, informatics spending has been piecemeal, in pockets where the company does understand the impact of the technology," says Vikas Taneja, a project leader at Boston Consulting Group.

But they are optimistic about its potential. "We’re already looking to a new future where there is constant give-and-take between what is learned at the bench and what we do at the computer to allow us to conduct more effective experiments faster," Fasman says. "There’s quite a bit of hope."