Virtual Docking Library Launched

Researchers have launched an ultra-large virtual docking library expected to grow to more than 1 billion molecules by next year. It will expand by 1000-fold the number of such "make-on-demand" compounds readily available to scientists for chemical biology and drug discovery, according to a paper published in Nature today.

"To improve medications for mental illnesses, we need to screen huge numbers of potentially therapeutic molecules," explained Joshua A. Gordon, M.D., Ph.D., director of NIH's National Institute of Mental Health (NIMH), which co-funded the research. "Unbiased computational modeling allows us to do this in a computer, vastly expediting the process of discovering new treatments. It enables researchers to virtually "see" a molecule docking with its receptor protein—like a ship in its harbor berth or a key in its lock—and predict its pharmacological properties, based on how the molecular structures are predicted to interact. Only those relatively few candidate molecules that best match the target profile on the computer need to be physically made and tested in a wet lab."

Over the past few years, Bryan Roth, M.D., Ph.D., of the University of North Carolina Chapel Hill, Brian Shoichet, Ph.D., of the University of California San Francisco, and colleagues have employed their virtual structure-based docking approach to uncover molecular secrets of an antipsychotic drug and LSD docked in their respective target receptors—and to create a designer painkiller that selectively targets brain analgesic circuitry without morphine's side effects.

A staggering number of potential drug-like molecules are known to exist. Yet, hundreds-of-millions to billions of diverse molecules have remained inaccessible due to limitations of existing methods used to compile molecular libraries, say the researchers. For example, their virtual structure-based docking technique, while promising, risks finding many false positives or "decoys"—flaws in the model allow for molecules that appear plausible but turn out to be biologically inactive.

To overcome this challenge, the researchers focused on molecules that result from 130 well-characterized chemical reactions using 70,000 different chemical building blocks. Computer simulations with these molecules showed that as the size of a library grew, the ratio of "true actives" to decoys increased—just as a study's statistical power increases with a larger sample.

Image: A selection of molecules discovered using the mega docking library.