Drugs enter our bodies as small molecules and bind to the surface of target proteins, inhibiting their function or reproduction. For a compound to tame a headache or reduce a swollen knee, it needs to be effective at small doses, and selective enough to limit side effects.
With so many medicines to choose from on the shelves of your local pharmacy, it would seem that creating a new drug is a simple, straightforward process. In reality, discovering a new drug can be a Herculean effort.
According to a prominent paper by Joseph DiMasi of Tufts University, it requires 15 years and more than $800 million in research and development for a drug to come to market. This drives up the price of blockbuster drugs, while limiting research into less profitable medications.
Due to the time and costs involved, computing is crucial to drug discovery efforts. By creating virtual models of proteins and ligands, and automating aspects of the interaction process, chemists have been able to screen the pool of possible compounds and discover drugs more efficiently and at a lower cost. Early examples of such discoveries include important HIV protease inhibitors.
However, computational drug discovery has fallen short of its promise, in part because of the inconsistency of conventional docking algorithms, which are used to narrow potential compounds from millions to hundreds, at which point they can be studied in the lab.
This virtual “enrichment” offered by traditional docking is only helpful if the most effective molecules end up in the top 10 percent of the prediction. Unfortunately, more often than not, they don’t. In fact, studies have shown that docking methods systematically miss some of the best compounds, while promoting duds, leading to frustration and skepticism in the field.
Pengyu Ren, assistant professor of biomedical engineering at The University of Texas at Austin, is trying to solve this problem by taking advantage of sophisticated physical models and supercomputing power to create a more robust way of searching for new drugs. Read more.
