By Andy Extance / Source: ScientifcAmerican


While drug companies struggle to develop medicines for rich countries and typically overlook diseases elsewhere, a robot scientist named Eve has found compounds that could fight drug-resistant malaria. Eve’s developers believe their artificial intelligence (AI) technology could speed up drug discovery, as critics call for a “match” with a live chemist.

The AI-endowed robot is designed to add a new, advanced ability to learn on top of the computational smarts that the pharmaceutical industry already uses. In research published February 4 in Journal of the Royal Society Interface computer scientist Ross King from the University of Manchester in England and his team say Eve found a chemical called TNP-470 effectively targets an enzyme that is key to the growth of Plasmodium vivax, one of the parasites that causes malaria. “I didn’t expect to actually find any useful compounds, I thought we’d just demonstrate the AI,” King says.

Eve not only has brains—it also has drug discovery brawn. Its computer server controls two robot arms that dance amidst equipment for dispensing liquids into plastic plates containing large numbers of wells. The plates are used in screening tests for potentially useful drug compounds. Drug molecules, in essence, act like tiny keys that slot into protein or enzyme locks. In the plates each well holds a biochemical system containing a lock, and when a key fits into it the system triggers a detectable signal such as fluorescence. Initial signals—or hits—are usually found on one instrument before further tests are done elsewhere to check if the key really does fit the lock. Eve integrates these usually separate capabilities, accelerating the research process.

Pharmaceutical companies often have to screen hundreds of thousands of compounds to find hits that tell them about the nature of the lock. These hits are never the exact, perfect key needed to treat a disease. So, after having slogged through the lengthy screening process chemists and biologists spend considerable effort using the data to work out what compound to make and test next. To do so, they create a “quantitative structure–activity relationship” (QSAR) from the screening results. This is a mathematical function that relates a molecule’s composition, shape and properties such as electrical charge to how well it fits the lock. Even with computer-assisted QSAR, however, it still takes a slow trial and error process to hammer out the exact key that could become a drug.

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