FierceBiotechIT | By Ryan McBride
For years, drug researchers have tapped computers to take serendipity out of the discovery equation, and a group from the University of Texas at Austin has advanced computational drug discovery further with updated image-reconstruction and modeling techniques, according to the university.
Led by computer science professor Chandrajit Bajaj, university researchers employed algorithms and supercomputers from the Texas Advanced Computing Center to create 3D models of disease targets, exposing binding sites on viruses for drug molecules. And the high-performance computing has enabled the team to cut down the time required to discover drugs with the qualities needed to bind to the targets from months to days, Bajaj said in a statement.
Both startups and Big Pharma have relied on computer-based methods to speed up drug discovery. Last year, for instance, Merck ($MRK) formed a partnership with Vancouver-based Zymeworks, which uses proprietary software to develop bi-specific antibodies. And GlaxoSmithKline's ($GSK) venture group SR One and Eli Lilly's ($LLY) Lilly Ventures were backers in Cambridge, MA-based Nimbus's $24 million Series A round last year, with some of the money expected to be invested in the company's software-supported discovery of drugs against difficult targets to combat cancer and inflammation.
"More and more, [drug companies] are moving into the computational drug screening arena, and more and more it's teams of people working together," Bajaj stated. "The biophysicist, the biochemist and the synthetic chemist are sitting together with the computational expert, and they say it's giving them clues as to what they should be doing next."