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.

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[LiquidCoooled]

Boobies, extra large please.

Shape versus negative space

[goombah99]

It's pretty easy to geometrically hash or construct reduced feature vectors for matching. People (like me) have been doing this for years. It's much harder to know if a molecule will fit into a crevice or negative space. THe latter is probably more important to drug design. the reduced feature vectors let you know quickly if two molecules are simmmilar in shape. Which is the title given to the article. But then this is discussed in the context of drug targets. A harder problem. What maybe new or clever here is that they found a very useful set of feature vectors.

Re:Shape versus negative space

[Anonymous Coward]

It's pretty easy to geometrically hash or construct reduced feature vectors for matching. People (like me) have been doing this for years

I bet you have to beat the chicks away with a stick.

Re:WOO HOO!

[Kenja]

Hot molecule on molecule action! See uncensored carbon bonding!

Impact on Pharma (esp. patents)

[Mateo_LeFou]

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?

Good, but just one tiny bit of the problem

[filthWisard]

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.

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Speed versus Thoroughness

[wsherman]

NewScientistTech has a story about a 3D molecular search engine that is over 1,500 times faster than anything previously developed.

The implication both from the summary and from the article itself is that this new search is just as thorough as other search methods but much faster. To prove thoroughness they would have had to show that anything found by other search methods will also be found by their new, much faster, search method. I doubt very much that they were able to do prove this rigorously.

That's not to say that the problem of matching 3D molecular shapes is not important or that their research is not valuable. I would say, though, that it is misleading to claim that they have solved the 3D search problem with a much faster algorithm. There are many different measure of 3D similarity and, for many measures of similarity, the only way to guarantee an optimum match is by exhaustive search.

Note that, in general, every search will be exhaustive in the sense that the query must be compared to every entry in the database. The problem is that many measures of similarity have additional parameters that must be optimized by exhaustive enumeration for each comparison. The classic example is a measure of 3D similarity that pairs each atom in the query with an atom from the structure in the database. In the general case, all possible pairings must be tried through an exhaustive enumeration.

they got it backwards

[oohshiny]

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.

Not enought structures?

[ajax142]

The author lists an apparent problem of this 3D search as a lack of molecular structures and calls for a "jump start" in the supply of 3D data, I call BS on this claim. A quick look at the Cambridge Structural Database shows 400,977 strucutures of 363,931 different molecules. There are another 89,064 structures of inorganic molecules in the Inorganic Crystal Structure Database. On the biological side there are 3,425 structures of Nucleic Acids in the NDB as well as 42,082 structures of proteins and polypeptides in the PDB. If that still isn't enough for the authors, fire up any number of ab initio quantum chemistry programs and in a short time you can create a library of good guesses for the structure of small molecules.

I tend to think the authors of the article are refering to the problems of a "useable form" for the structures and easy access of many of these databases. The first problem is mearly a problem of converting between the various structural file formats out there, something a good programmer (or grad student) can solve is a few weeks or less. The second is a bureaucrat issue and not a scientific one.

Quite interesting

[excelsior_gr]

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.