For the past 10 years, Bartosz Grzybowski, a chemist with the Northwestern University, Illinois, has been leading a project to build a network of nearly 250 years of chemical knowledge. Called Chematica, the network links more than seven million substances via reactions that get you from one substance to another. He says this giant network of organic chemistry offers possibilities to create existing drugs more efficiently and cheaply.

Could you explain where the idea came from to build the Chematica?

More than a decade ago, I, like many others, started realising that we are in the midst of a technological revolution, where everything is being networked. Yet, for some reason, chemistry hasn't taken advantage of the potential of networks – it has remained "fragmented" in the singular experiences of individual chemists. This is quite inefficient, not least because when a trained chemist retires, all the knowledge he/she has amassed during their career disappears. On the other hand, if properly programmed, modern computers could be made to "remember" and process the entirety of chemistry; we can create something that I call the "immortal scientist". We have already created a machine that not only contains all the chemical knowledge, but has also learned some 86,000 chemical rules; for comparison, the top-level organic chemists I have spoken to say they know 1,000-1,500 rules.

Networking all this data allows you to find new syntheses?

Yes, because with appropriate network algorithms we can traverse the network of reactions to explore all possible chemical syntheses, not only the few that chemists typically consider. In doing so, our algorithms rank the syntheses not according to some subjective criteria one has learned in his/her chemical education, but according to the objective rules that the machine learned from all the existing chemical knowledge. In these searches, we can achieve search speeds that will never be achievable to human chemists. To give you a flavour, our method can explore billions and billions of possible syntheses in a fraction of a second and can then choose the one that is most economical, is most environment-friendly or involves only popular (ie, easy to purchase) chemicals. It is like a chemical Google on steroids.

Chematica's algorithms are trained to look for synethic shortcuts that combine multiple reactions into one step. What are the advantages of making a drug in a shorter number of steps?

In a multistep synthesis, about 60-80% of the total cost is actually not in the "cooking" itself but in the purification of intermediates after each step. For instance, in a two-step reaction, A to B to C, one has to first make B from A, then purify B, and only then proceed from B to C. Each purification step often requires the use of chromatographic columns, or environmentally dangerous solvents – and here lies the extra cost that we can avoid if the two individual steps are combined into one.

Historically, this type of "shortening" of synthetic routes to one-pot syntheses has been one of the holy grails of organic chemistry. Now, with our network and the chemical rules we taught the computer, we can search and evaluate for one-pot sequences in seconds. This translates into enormous savings for chemical industry. And, let me add, the coolest – and most important – thing is that our computational analyses are fully validated in chemical practice.

Could you give me some examples of such validation?

Of course, we demonstrated that over 30 computer predicted one-pot reactions proceed very cleanly and with excellent yields when done in the lab. Perhaps the most exciting example is the synthesis of an anti-asthma drug that typically takes four individual steps. When we analysed this sequence, the computer told us that all four steps can be combined into one – we made the drug in just one step with double the yield and with no cost of intermediate purification. We have many similar examples, mostly for our pharmaceutical clients who have recently purchased our software.

Could Chematica be used to create new compounds?

While these results are not yet published, we already do have algorithms that allow Chematica to synthesize new compounds. Some of this work goes back to our original papers (2005 and 2006 angewandte chemie) where we used the elements of network science and group theory to teach the computer to recognize structural patterns/motifs that emerge and repeat in the organic compounds as the discipline evolves. Now, with this knowledge, we have implemented algorithms that predict what compounds will (or can) be made. It is like learning from the past and current structure of chemistry to project into its future.

Could a terrorist use Chematica to spot a quicker and cheaper way to make a substance that was dangerous?

Yes. We have been in contact with the Pentagon already. You might well have terrorists trying to cook up a chemical weapon, but currently it is difficult to analyse what is suspicious chemical-buying behaviour because you don't know what new ways there might be of making dangerous things. Or a terrorist may try making, say, a nerve gas using a complex route to confuse the authorities. But, if the government – using Chematica – knows all pathways for making a nerve gas or another chemical weapon, it is more likely to be able to recognise dangerous behaviour.

Sounds like a disruptive technology.

Oh, yeah. Honestly, I don't know why nobody has ever tried it: it's probably because chemists are not trained to think in terms of computer algorithms, network theory, and so on. And also because it has only been very recently that computers became powerful enough to perform searches we need.

There is, naturally, some opposition to these ideas from the very practical chemists who think I want the computer to replace the chemist. Nothing of the sort! I see Chematica and all its algorithms as a tool that will help chemists to optimise their practice. In a few months, Chematica will be made available for smartphones and so everyone who is interested will have the collective chemical knowledge literally in his/her palm. This can but accelerate chemical discovery.