Scientists create chemical 'brain': Giant network links all known compounds and reactions

ScienceDaily (Aug. 22, 2012) — Northwestern University scientists have connected 250 years of organic chemical knowledge into one giant computer network -- a chemical Google on steroids. This "immortal chemist" will never retire and take away its knowledge but instead will continue to learn, grow and share.

A decade in the making, the software optimizes syntheses of drug molecules and other important compounds, combines long (and expensive) syntheses of compounds into shorter and more economical routes and identifies suspicious chemical recipes that could lead to chemical weapons.

"I realized that if we could link all the known chemical compounds and reactions between them into one giant network, we could create not only a new repository of chemical methods but an entirely new knowledge platform where each chemical reaction ever performed and each compound ever made would give rise to a collective 'chemical brain,'" said Bartosz A. Grzybowski, who led the work. "The brain then could be searched and analyzed with algorithms akin to those used in Google or telecom networks."

Called Chematica, the network comprises some seven million chemicals connected by a similar number of reactions. A family of algorithms that searches and analyzes the network allows the chemist at his or her computer to easily tap into this vast compendium of chemical knowledge. And the system learns from experience, as more data and algorithms are added to its knowledge base.

Details and demonstrations of the system are published in three back-to-back papers in the Aug. 6 issue of the journal Angewandte Chemie.

Grzybowski is the senior author of all three papers. He is the Kenneth Burgess Professor of Physical Chemistry and Chemical Systems Engineering in the Weinberg College of Arts and Sciences and the McCormick School of Engineering and Applied Science.

In the Angewandte paper titled "Parallel Optimization of Synthetic Pathways Within the Network of Organic Chemistry," the researchers have demonstrated algorithms that find optimal syntheses leading to drug molecules and other industrially important chemicals.

"The way we coded our algorithms allows us to search within a fraction of a second billions of chemical syntheses leading to a desired molecule," Grzybowski said. "This is very important since within even a few synthetic steps from a desired target the number of possible syntheses is astronomical and clearly beyond the search capabilities of any human chemist."

Chematica can test and evaluate every possible synthesis that exists, not only the few a particular chemist might have an interest in. In this way, the algorithms find truly optimal ways of making desired chemicals.

The software already has been used in industrial settings, Grzybowski said, to design more economical syntheses of companies' products. Synthesis can be optimized with various constraints, such as avoiding reactions involving environmentally dangerous compounds. Using the Chematica software, such green chemistry optimizations are just one click away.

Another important area of application is the shortening of synthetic pathways into the so-called "one-pot" reactions. One of the holy grails of organic chemistry has been to design methods in which all the starting materials could be combined at the very beginning and then the process would proceed in one pot -- much like cooking a stew -- all the way to the final product.

The Northwestern researchers detail how this can be done in the Angewandte paper titled "Rewiring Chemistry: Algorithmic Discovery and Experimental Validation of One-Pot Reactions in the Network of Organic Chemistry."

The chemists have taught their network some 86,000 chemical rules that check -- again, in a fraction of a second -- whether a sequence of individual reactions can be combined into a one-pot procedure. Thirty predictions of one-pot syntheses were tested and fully validated. Each synthesis proceeded as predicted and had excellent yields.

In one striking example, Grzybowski and his team synthesized an anti-asthma drug using the one-pot method. The drug typically would take four consecutive synthesis and purification steps.

"Our algorithms told us this sequence could be combined into just one step, and we were naturally curious to check it out in a flask," Grzybowski said. "We performed the one-pot reaction and obtained the drug in excellent yield and at a fraction of the cost the individual steps otherwise would have accrued."

The third area of application is the use of the Chematica network approach for predicting and monitoring syntheses leading to chemical weapons. This is reported in the Angewandte paper titled "Chemical Network Algorithms for the Risk Assessment and Management of Chemical Threats."

"Since we now have this unique ability to scrutinize all possible synthetic strategies, we also can identify the ones that a potential terrorist might use to make a nerve gas, an explosive or another toxic agent," Grzybowski said.

Algorithms known from game theory first are applied to identify the strategies that are hardest to detect by the federal government -- the use of substances, for example, such as kitchen salt, clarifiers, grain alcohol and a fertilizer, all freely available from a local convenience store. Characteristic combinations of seemingly innocuous chemicals, such as this example, are red flags.

This strategy is very different from the government's current approach of monitoring and regulating individual substances, Grzybowski said. Chematica can be used to monitor patterns of chemicals that together become suspicious, instead of monitoring individual compounds. Grzybowski is working with the federal government to implement the software.

Chematica now is being commercialized. "We chose this name," Grzybowski said, "because networks will do to chemistry what Mathematica did to scientific computing. Our approach will accelerate synthetic design and discovery and will optimize synthetic practice at large."

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The above story is reprinted from materials provided by Northwestern University. The original article was written by Megan Fellman.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal References:

Mikolaj Kowalik, Chris M. Gothard, Aaron M. Drews, Nosheen A. Gothard, Alex Weckiewicz, Patrick E. Fuller, Bartosz A. Grzybowski, Kyle J. M. Bishop. Parallel Optimization of Synthetic Pathways within the Network of Organic Chemistry. Angewandte Chemie International Edition, 2012; 51 (32): 7928 DOI: 10.1002/anie.201202209Chris M. Gothard, Siowling Soh, Nosheen A. Gothard, Bartlomiej Kowalczyk, Yanhu Wei, Bilge Baytekin, Bartosz A. Grzybowski. Rewiring Chemistry: Algorithmic Discovery and Experimental Validation of One-Pot Reactions in the Network of Organic Chemistry. Angewandte Chemie International Edition, 2012; 51 (32): 7922 DOI: 10.1002/anie.201202155Patrick E. Fuller, Chris M. Gothard, Nosheen A. Gothard, Alex Weckiewicz, Bartosz A. Grzybowski. Chemical Network Algorithms for the Risk Assessment and Management of Chemical Threats. Angewandte Chemie International Edition, 2012; 51 (32): 7933 DOI: 10.1002/anie.201202210

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Sharing data links in networks of cars

ScienceDaily (July 5, 2012) — A new algorithm lets networks of Wi-Fi-connected cars, whose layout is constantly changing, share a few expensive links to the Internet.

Wi-Fi is coming to our cars. Ford Motor Co. has been equipping cars with Wi-Fi transmitters since 2010; according to an Agence France-Presse story last year, the company expects that by 2015, 80 percent of the cars it sells in North America will have Wi-Fi built in. The same article cites a host of other manufacturers worldwide that either offer Wi-Fi in some high-end vehicles or belong to standards organizations that are trying to develop recommendations for automotive Wi-Fi.

Two Wi-Fi-equipped cars sitting at a stoplight could exchange information free of charge, but if they wanted to send that information to the Internet, they'd probably have to use a paid service such as the cell network or a satellite system. At the ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, taking place this month in Portugal, researchers from MIT, Georgetown University and the National University of Singapore (NUS) will present a new algorithm that would allow Wi-Fi-connected cars to share their Internet connections. "In this setting, we're assuming that Wi-Fi is cheap, but 3G is expensive," says Alejandro Cornejo, a graduate student in electrical engineering and computer science at MIT and lead author on the paper.

The general approach behind the algorithm is to aggregate data from hundreds of cars in just a small handful, which then upload it to the Internet. The problem, of course, is that the layout of a network of cars is constantly changing in unpredictable ways. Ideally, the aggregators would be those cars that come into contact with the largest number of other cars, but they can't be identified in advance.

Cornejo, Georgetown's Calvin Newport and NUS's Seth Gilbert -- all three of whom did or are doing their doctoral work in Nancy Lynch's group at MIT's Computer Science and Artificial Intelligence Laboratory -- began by considering the case in which every car in a fleet of cars will reliably come into contact with some fraction -- say, 1/x -- of the rest of the fleet in a fixed period of time. In the researchers' scheme, when two cars draw within range of each other, only one of them conveys data to the other; the selection of transmitter and receiver is random. "We flip a coin for it," Cornejo says.

Over time, however, "we bias the coin toss," Cornejo explains. "Cars that have already aggregated a lot will start 'winning' more and more, and you get this chain reaction. The more people you meet, the more likely it is that people will feed their data to you." The shift in probabilities is calculated relative to 1/x -- the fraction of the fleet that any one car will meet.

The smaller the value of x, the smaller the number of cars required to aggregate the data from the rest of the fleet. But for realistic assumptions about urban traffic patterns, Cornejo says, 1,000 cars could see their data aggregated by only about five.

Realistically, it's not a safe assumption that every car will come in contact with a consistent fraction of the others: A given car might end up collecting some other cars' data and then disappearing into a private garage. But the researchers were able to show that, if the network of cars can be envisioned as a series of dense clusters with only sparse connections between them, the algorithm will still work well.

Weirdly, however, the researchers' mathematical analysis shows that if the network is a series of dense clusters with slightly more connections between them, aggregation is impossible. "There's this paradox of connectivity where if you have these isolated clusters, which are well-connected, then we can guarantee that there will be aggregation in the clusters," Cornejo says. "But if the clusters are well connected, but they're not isolated, then we can show that it's impossible to aggregate. It's not only our algorithm that fails; you can't do it."

"In general, the ability to have cheap computers and cheap sensors means that we can generate a huge amount of data about our environment," says John Heidemann, a research professor at the University of Southern California's Information Sciences Institute. "Unfortunately, what's not cheap is communications."

Heidemann says that the real advantage of aggregation is that it enables the removal of redundancies in data collected by different sources, so that transmitting the data requires less bandwidth. Although Heidemann's research focuses on sensor networks, he suspects that networks of vehicles could partake of those advantages as well. "If you were trying to analyze vehicle traffic, there's probably 10,000 cars on the Los Angeles Freeway that know that there's a traffic jam. You don't need every one of them to tell you that," he says.

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The above story is reprinted from materials provided by Massachusetts Institute of Technology. The original article was written by Larry Hardesty.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Sharing data links in networks of cars

ScienceDaily (July 5, 2012) — A new algorithm lets networks of Wi-Fi-connected cars, whose layout is constantly changing, share a few expensive links to the Internet.

Wi-Fi is coming to our cars. Ford Motor Co. has been equipping cars with Wi-Fi transmitters since 2010; according to an Agence France-Presse story last year, the company expects that by 2015, 80 percent of the cars it sells in North America will have Wi-Fi built in. The same article cites a host of other manufacturers worldwide that either offer Wi-Fi in some high-end vehicles or belong to standards organizations that are trying to develop recommendations for automotive Wi-Fi.

Two Wi-Fi-equipped cars sitting at a stoplight could exchange information free of charge, but if they wanted to send that information to the Internet, they'd probably have to use a paid service such as the cell network or a satellite system. At the ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, taking place this month in Portugal, researchers from MIT, Georgetown University and the National University of Singapore (NUS) will present a new algorithm that would allow Wi-Fi-connected cars to share their Internet connections. "In this setting, we're assuming that Wi-Fi is cheap, but 3G is expensive," says Alejandro Cornejo, a graduate student in electrical engineering and computer science at MIT and lead author on the paper.

The general approach behind the algorithm is to aggregate data from hundreds of cars in just a small handful, which then upload it to the Internet. The problem, of course, is that the layout of a network of cars is constantly changing in unpredictable ways. Ideally, the aggregators would be those cars that come into contact with the largest number of other cars, but they can't be identified in advance.

Cornejo, Georgetown's Calvin Newport and NUS's Seth Gilbert -- all three of whom did or are doing their doctoral work in Nancy Lynch's group at MIT's Computer Science and Artificial Intelligence Laboratory -- began by considering the case in which every car in a fleet of cars will reliably come into contact with some fraction -- say, 1/x -- of the rest of the fleet in a fixed period of time. In the researchers' scheme, when two cars draw within range of each other, only one of them conveys data to the other; the selection of transmitter and receiver is random. "We flip a coin for it," Cornejo says.

Over time, however, "we bias the coin toss," Cornejo explains. "Cars that have already aggregated a lot will start 'winning' more and more, and you get this chain reaction. The more people you meet, the more likely it is that people will feed their data to you." The shift in probabilities is calculated relative to 1/x -- the fraction of the fleet that any one car will meet.

The smaller the value of x, the smaller the number of cars required to aggregate the data from the rest of the fleet. But for realistic assumptions about urban traffic patterns, Cornejo says, 1,000 cars could see their data aggregated by only about five.

Realistically, it's not a safe assumption that every car will come in contact with a consistent fraction of the others: A given car might end up collecting some other cars' data and then disappearing into a private garage. But the researchers were able to show that, if the network of cars can be envisioned as a series of dense clusters with only sparse connections between them, the algorithm will still work well.

Weirdly, however, the researchers' mathematical analysis shows that if the network is a series of dense clusters with slightly more connections between them, aggregation is impossible. "There's this paradox of connectivity where if you have these isolated clusters, which are well-connected, then we can guarantee that there will be aggregation in the clusters," Cornejo says. "But if the clusters are well connected, but they're not isolated, then we can show that it's impossible to aggregate. It's not only our algorithm that fails; you can't do it."

"In general, the ability to have cheap computers and cheap sensors means that we can generate a huge amount of data about our environment," says John Heidemann, a research professor at the University of Southern California's Information Sciences Institute. "Unfortunately, what's not cheap is communications."

Heidemann says that the real advantage of aggregation is that it enables the removal of redundancies in data collected by different sources, so that transmitting the data requires less bandwidth. Although Heidemann's research focuses on sensor networks, he suspects that networks of vehicles could partake of those advantages as well. "If you were trying to analyze vehicle traffic, there's probably 10,000 cars on the Los Angeles Freeway that know that there's a traffic jam. You don't need every one of them to tell you that," he says.

Share this story on Facebook, Twitter, and Google:

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Story Source:

The above story is reprinted from materials provided by Massachusetts Institute of Technology. The original article was written by Larry Hardesty.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here