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Toward a 3D Search Engine
Plasma Droid writes "NewScientistTech has a story about a 3D molecular search engine that is over 1,500 times faster than anything previously developed. The researchers, from Oxford University, developed a lightning-fast way to quickly match 3D shapes mathematically. This could not only speed up searches for new drugs, but lead to 3D search engines, for finding objects uploaded to platforms such as Google Earth, they say." The problem will be in jump-starting the supply of 3D data about molecules and everything else.
I've always been of two minds about whether the drug industry was a good example of patents being cost-effective, because I suspect that very good technology will soon emerge that makes pharma R&D less expensive, by making it primarily a data-processing (esp. simulation) issue. Seems like this tech might be the first piece of that puzzle?
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This is a really cool advance when working with molecules you already know the shape of, but it still doesn't get around the problem of what shape a molecule is in the first place. A protein molecule will naturally collapse into the shape with the lowest energy. If there are 100 atoms in the main chain, that's 99 different angles that it could have, that's 99 degrees of freedom. I hear that genetic algorithms are pretty good at finding the most lightly shape though, so this may not be as big a problem as it used to be.
Currently, the most common way to find the 3D shape of a particular molecule within a database is to superimpose a candidate over the query molecule and see how much of it overlaps. But this is time consuming, partly because it requires both molecules to be precisely aligned.
Yes, that's currently "the most common way" because at least you can tell what you're getting: when you get a match, you can actually say how close the different shapes are to one another.
The new technique uses a different approach. It analyses the position of the different atoms within a molecule to understand its shape. These relative positions can be mapped and stored a molecular database.
That's actually not a "new technique", it's an old technique. It's what people used to do before they tried to overlay 3D shapes accurately. They used to do that because computers used to be too slow to do the accurate comparison.
As the article points out, there is only limited 3D shape information available at all. Few people need to do 3D queries right now, and there is little data to do them on, so optimizing speed is the wrong thing to do; we need to optimize accuracy and scientific relevance.
This is quite an interesting achievement. The tools that I am familiar with can only search for 2D structures like functional groups (alcohol groups, aromatic rings, etc). At their best, they might give the ability to search for R- and S- stereoisomers, but that is it. This is pretty enough for tasks like solvent design that are quite frequent in the chemical process industry, but in the pharmaceutical R&D they need more powerful tools.
I will give a simple example of an enzyme: These nice molecules catalyze reactions of vital importance in the modern pharmaceutical industry by providing a chemical "lock" where the "keys" (i.e. the reacting molecules) will dock on. This enables them to react and form a new molecule that will then undock from the enzume leaving the "lock" free for the next pair.
These "locks" are actually 3D structures of appropriately aligned molecules. This is where this search ability comes in: The chemist suspects how the appropriate lock would look like for catalyzing his reaction (3D alignment of functional groups), much like someone suspects what the right keywords for a Google search are. Then he feeds the data to the machine and gets the molecules that are likely to be of assistance in his work. After that, he can make experiments testing these enzymes to see if they actually work.
This should speed things up very much in biochemical research. It means less literature research and less failed experiments.