Eve, an artificially intelligent drug-discovery robot, has discovered that a cancer drug might be good for treating malaria, too.
Designed to speed up drug discovery and make it more economical, Eve can automatically develop and test hypotheses to explain observations, run experiments, record and interpret the results to amend their hypotheses, and then repeat the cycle. A new study published in the Royal Society journal Interface explains how the robot can help identify promising new drug candidates for malaria and neglected tropical diseases, such as African sleeping sickness and Chagas’ disease.
“Neglected tropical diseases are a scourge of humanity, infecting hundreds of millions of people, and killing millions of people every year,” says Professor Steve Oliver from the Cambridge Systems Biology Centre and the Department of Biochemistry at the University of Cambridge. “We know what causes these diseases and that we can, in theory, attack the parasites that cause them using small molecule drugs. But the cost and speed of drug discovery and the economic return make them unattractive to the pharmaceutical industry.
“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.”
Eve is designed to automate early-stage drug design. First, she systematically tests each member from a large set of compounds in the standard brute-force way of conventional mass screening. The compounds are screened against assays (tests) designed to be automatically engineered, and can be generated much faster and more cheaply than the bespoke assays that are currently standard. This enables more types of assay to be applied, more efficient use of screening facilities to be made, and thereby increases the probability of a discovery within a given budget.
Eve’s robotic system is capable of screening over 10,000 compounds per day. However, while simple to automate, mass screening is still relatively slow and wasteful of resources as every compound in the library is tested. It is also unintelligent, as it makes no use of what is learnt during screening.
To improve this process, Eve selects at random a subset of the library to find compounds that pass the first assay; any ‘hits’ are re-tested multiple times to reduce the probability of false positives. Taking this set of confirmed hits, Eve uses statistics and machine learning to predict new structures that might score better against the assays. Although she currently does not have the ability to synthesise such compounds, future versions of the robot could potentially incorporate this feature.