Cambridge University. UK: Eve,
an artificially-intelligent ‘robot scientist’ could make drug discovery
faster and much cheaper, say researchers writing in the Royal Society
journal Interface. The team has demonstrated the success of the approach
as Eve discovered that a compound shown to have anti-cancer properties
might also be used in the fight against malaria.
Robot
scientists are a natural extension of the trend of increased
involvement of automation in science. They can automatically develop and
test hypotheses to explain observations, run experiments using
laboratory robotics, interpret the results to amend their hypotheses,
and then repeat the cycle, automating high-throughput hypothesis-led
research. Robot scientists are also well suited to recording scientific
knowledge: as the experiments are conceived and executed automatically
by computer, it is possible to completely capture and digitally curate
all aspects of the scientific process.
In 2009, Adam, a robot scientist developed by researchers at the Universities of Aberystwyth and Cambridge, became the first machine to independently discover new scientific knowledge.
The same team has now developed Eve, based at the University of
Manchester, whose purpose is to speed up the drug discovery process and
make it more economical. In the study published today, they describe 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.
Professor Ross King, from the Manchester Institute of Biotechnology at
the University of Manchester, says: “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.”
To test the viability of the approach, the researchers developed assays
targeting key molecules from parasites responsible for diseases such as
malaria, Chagas’ disease and schistosomiasis and tested against these a
library of approximately 1,500 clinically approved compounds. Through
this, Eve showed that a compound that has previously been investigated
as an anti-cancer drug inhibits a key molecule known as DHFR in the
malaria parasite. Drugs that inhibit this molecule are currently
routinely used to protect against malaria, and are given to over a
million children; however, the emergence of strains of parasites
resistant to existing drugs means that the search for new drugs is
becoming increasingly more urgent.
“Despite extensive efforts, no one has been able to find a new
antimalarial that targets DHFR and is able to pass clinical trials,”
adds Professor King. “Eve’s discovery could be even more significant
than just demonstrating a new approach to drug discovery.”
The research was supported by the Biotechnology & Biological Sciences Research Council and the European Commission.
Reference
Williams, K. and Bilsland, E. et al. Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases. Interface; 4 Feb 2015.
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