ScienceDaily (Feb. 3, 2009) — A robot scientist that can make informed guesses about how effective different chemical compounds will be at fighting different diseases could revolutionise the pharmaceutical industry by developing more effective treatments more cheaply and quickly than current methods.
The robot,known as Eve, uses advanced artificial intelligence combined with innovative data mining and knowledge discovery techniques to analyse the results of pharmacological experiments it conducts itself.
By relating the chemical structure of different compounds to their pharmacological activity, Eve is able to learn which chemical compounds should be tested next, bringing a degree of predictability todrug screening procedures that, until now, have tended to be a bit hit and miss.
“Over time, Eve will learn to pick out the chemical compounds that are likely to be most effective against a certain target by analysing data from past experiments and comparing chemical structures to their pharmacological properties,” explains Saso Dzeroski, a researcher at the Jozef Stefan Institute in Ljubljana whohelped develop Eve’s data mining capabilities.
“That should help scientists and pharmaceutical companies identify more effective compounds to treat different diseases, allowing them to find drug leads in a fraction of the time and at a fraction of the cost of current methods.”
Eve could minimise the need for random testing of chemical compounds, Dzeroski says, noting that the robot scientist isthe first computer system capable of originating its own experiments, physically performing them, interpreting the results and then repeating the cycle.
Currently, when a new drug is sought pharmacological researchers conduct a blind study of tens or hundreds of thousands of chemical compounds, applying them to an assay for a disease. The results of those tests determine the so-calledQuantitative Structure-Activity Relationships (QSARs) that relate the structure of a chemical compound to its pharmacological activity.
Exhaustive testing like this is time-consuming, costly and generally has to be repeated each time a new drug is sought.
More “intelligent” approach to drug discovery
Eve offers a more “intelligent” approach, says Ross King, a computer science researcher at the Universityof Wales, Aberystwyth where Eve is to be installed.
The robot conducts the QSAR testing in assays itself, analyses the results and stores the data for future use. Over the course of numerous experiments, Eve learns which chemical structures are likely to be effective in specific assays. So, instead of choosing compounds to test at random, it can pick ones that are more likely to be effective.“We have carried out some preliminary trials and the compounds picked by Eve show more promise than those selected randomly,” Dzeroski says.
New data mining techniques developed by a team of researchers led by Dzeroski lie at the heart of Eve’s groundbreaking drug discovery capabilities. Working in the EU-funded IQ project, the team developed new methods to analyse complex data, including chemicalstructures, from databases such as that in which Eve stores the results of its experiments.
Unlike most data mining approaches, in which an individual analysis is carried out on a single dataset, such as a spreadsheet, the techniques developed in the IQ project allow knowledge discovery processes, consisting of several analysis steps, to be carried out across multiple sets of complex data.
Thetechniques rely on the use of so-called inductive databases that contain not only raw data but also information about patterns and models valid in the data. In the case of drug discovery, the structures of the chemical compounds tested and their effectiveness would be the raw data, while molecular structures that appear commonly in effective compounds would be patterns, and the equations that...