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* Drug Discovery

Predicting New Medicines

Rensselaer researchers in computer science, chemistry, and mathematics have collaborated to create a software program capable of quickly identifying molecules that show promise for future medicines. The software program, now being licensed to companies, enables drug makers to comb through enormous databases of molecules and identify the ones that have sound medicinal properties.

The software is part of the Drug Discovery and Semi-Supervised Learning Project (DDASSL, pronounced “dazzle”), supported by a $1.2 million grant from the National Science Foundation.


The “Dazzle” project makes use of shortcuts that enable computers to search vast amounts of molecular data to quickly identify molecules that have sound medicinal properties. Photo by Curt Breneman.

The safety and effectiveness of medicines depend on the shape and chemistry of the molecules. To find the most likely molecules, the new software makes use of two shortcuts that enable the computer to search a vast molecular database rapidly.

The first shortcut describes the molecule — its shape and chemistry — in terms of numbers a computer can crunch rapidly. Chemistry professor Curt Breneman has a technique to quickly calculate electronic properties on the surface of a molecule. The technique produces a description — basically a set of numbers — that the computer can use easily.

The second shortcut identifies which molecules have the right chemistry for a specific therapy. Using advanced pattern-recognition techniques known as “kernel methods,” the software analyzes a small sample database to identify molecules with the right chemical features. Once the key features are identified, the software can quickly screen large databases, accurately predicting the molecules that show potential.

“The trick with drug discovery is to have the drug molecule fit like a key in a lock because shape affects its performance,” says Mark Embrechts, associate professor of decision sciences and engineering systems.

The researchers say that predictive modeling is one of a new breed of drug discovery methods marking a shift in industry practice — a shift away from cell-based assays performed in the lab toward math-based models calculated on the computer.

“Our program allows researchers to ‘crash test’ lots of molecules quickly and inexpensively,” says Breneman.

As drug makers increasingly target complex, chronic illness, drug development becomes far more costly and time consuming. Meanwhile, in the search for new drugs, 99.9 percent of compounds tested fail. Accordingly, drug makers want to be able to predict more accurately which compounds will produce the next blockbuster drug.

Kristin Bennett, associate professor of mathematics, also collaborated on the project.
Rensselaer Magazine: Summer 2004
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