Robot Screening System Streamlines Discovery of New Drug Compounds

British drug developers have automated the drug discovery process by using a "robot scientist" to screen thousands of potential candidate compounds.

Investigators at the University of Cambridge (United Kingdom) and the University of Manchester (United Kingdom) described the development of the robot scientist "Eve" in the February 4, 2015, online edition of the journal Interface. Eve is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. The system integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing.

In practice, Eve systematically tested up to 10,000 compounds per day in the standard brute-force way of conventional mass screening. In addition, Eve selected at random a subset of the library to identify compounds that passed the first screening analysis. Any "hits" were re-tested multiple times to reduce the probability of false positives. Taking this set of confirmed hits, Eve used statistics and machine learning to predict new structures that might score better against the assays. In this fashion, Eve repositioned several drugs against specific targets in parasites that cause tropical diseases. One important validated discovery was that the anti-cancer compound TNP-470 was a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax.

“Eve exploits its artificial intelligence to learn from early successes in her screens and select compounds that have a high probability of being active against the chosen drug target. A smart screening system, based on genetically engineered yeast, is used. This allows Eve to exclude compounds that are toxic to cells and select those that block the action of the parasite protein while leaving any equivalent human protein unscathed. This reduces the costs, uncertainty, and time involved in drug screening, and has the potential to improve the lives of millions of people worldwide,” said contributing author Dr. Stephen G. Oliver, professor of biochemistry at the University of Cambridge.

Senior author Dr. Ross King, professor of biotechnology at the University of Manchester, said, “Every industry now benefits from automation and science is no exception. Bringing in machine learning to make this process intelligent—rather than just a "brute force" approach—could greatly speed up scientific progress and potentially reap huge rewards.”

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University of Cambridge
University of Manchester