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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.

Source: www.sciencedaily.com

By Elizabeth Kiefer

Brittany Wenger is one seriously smart cookie. In 2012, the then-17-year-old submitted her "artificial brain" technology -- which assesses tissue samples for breast cancer -- to the Google Science Fair and walked away with the grand prize. It was no wonder: Her invention, which uses a type of computer program called neural networks, can identify complex data patterns and make breast cancer detection calls with 99 percent accuracy. But she's not stopping there: Brittany hopes to help wipe out cancer completely.

Since she took home the gold two years ago, she's been named one of Time's 30 Under 30, given a truly inspiring TED Talk, and launched her app, Cloud4Cancer, which allows doctors to enter their own data and fuel continued cancer research. And did we mention she's also holding down a full course load at Duke University? Um, yeah. 

We recently chatted with Brittany about how she got started, her challenges along the way, and how she balances being a college student with breaking the barriers of cancer diagnostics.

How did you get into computer programming?

When I was in 7th grade I took an elective class on futuristic thinking. When we were assigned our final paper, I decided to write mine on technology of the future. The moment I started researching artificial intelligence and its transcendence into human knowledge, I was inspired. I went out and bought a coding textbook, and taught myself how to code. I remember one of the first projects that I ever worked on was an artificial neural network that taught people how to play soccer.

You're a self-taught coder who went on to create a potentially game-changing cancer detection tool. How did that happen?

Well, it definitely didn't happen overnight. I spent over five years working with neural networks, starting with an entire year of research to try and recognize patterns and connect breast cancer to artificial intelligence. I faced a lot of roadblocks along the way, as this was a very complicated program with no predefined solution. I went through thousands of pages of coding and data that was available through public domains, and performed over 7.6 million test trials. I two failed projects before finally succeeding on my third attempt, taking what didn't work the first few times to optimize the code that helped build the Cloud4Cancer app.

Why did you decide on developing breast cancer detection technology?

When I was 15, my cousin was diagnosed with breast cancer. I have a very close-knit family, so seeing the impact that the disease can have on a woman and her family, firsthand, was so real to me. When I learned that one in eight women will be diagnosed with breast cancer in their lifetime, I knew that I wanted to get involved in making the process better for patients. Now, the coding that I first used to help detect breast cancer has been extended into diagnosing other types of cancers, including blood-based diseases like leukemia.

What's been the most rewarding part of the process?

The people. I've already had the opportunity to work with real patients and breast cancer survivors, as well as talk with kids who are interested in doing research or coding in the future. Knowing that my cloud application has the potential to save lives and expedite the process of discovery is so rewarding. I still get chills thinking about how, a couple of years down the line, my research can actually contribute to finding the cure for cancer.

You've got a lot on your plate these days, between Cloud4Cancer and school. How do you balance everything?

The great thing about where I am with school right now is that my schedule is entirely what I make it. I can attend classes during the week and then travel over some weekends. School is not something that I will ever bend on, as I'm actually going for my MD, PhD in pediatric oncology. At the same time, my initiative is so important to me, I don't want either one to ever outweigh the other. Luckily, I think they complement each other well and what I'm learning in my classes helps me improve Cloud4Cancer.

What's one thing you want other young women to know if they're thinking about going the tech route?

If you're interested, go for it! There have never been so many available resources or opportunities -- for women, and for society as a whole -- to pursue a career in the field. I love how technology allows you to make new things by putting together the little pieces and working towards something bigger that can really benefit the world. There's no greater feeling than solving a problem and seeing your code come to life.

Source: www.huffingtonpost.com