Author(s): Nic Fleming
How artificial intelligence is changing drug discovery
An enormous figure looms over scientists searching for new drugs: the estimated US$2.6-billion price tag of developing a treatment. A lot of that effectively goes down the drain, because it includes money spent on the nine out of ten candidate therapies that fail somewhere between phase I trials and regulatory approval. Few people in the field doubt the need to do things differently.
Leading biopharmaceutical companies believe a solution is at hand. Pfizer is using IBM Watson, a system that uses machine learning, to power its search for immuno-oncology drugs. Sanofi has signed a deal to use UK start-up Exscientia's artificial-intelligence (AI) platform to hunt for metabolic-disease therapies, and Roche subsidiary Genentech is using an AI system from GNS Healthcare in Cambridge, Massachusetts, to help drive the multinational company's search for cancer treatments. Most sizeable biopharma players have similar collaborations or internal programmes.
If the proponents of these techniques are right, AI and machine learning will usher in an era of quicker, cheaper and more-effective drug discovery. Some are sceptical, but most experts do expect these tools to become increasingly important. This shift presents both challenges and opportunities for scientists, especially when the techniques are combined with automation (see 'Here come the robots'). Early-career researchers, in particular, need to get to grips with what AI can do and how best to acquire the skills they need to be employable in the job market of tomorrow.
Nic Fleming is a freelance science writer based in Bristol, UK.
Here come the robots
When the time comes for the history of artificial intelligence (AI) to be written, the algorithm that gets the job is likely to flag 12 June 2007 as worthy of note. That was the day that a robot called Adam ended humanity's monopoly on the discovery of scientific knowledge -- by identifying the function of a yeast gene.
By searching public databases, Adam generated hypotheses about which genes code for key enzymes that catalyse reactions in the yeast Saccharomyces cerevisiae , and used robotics to physically test its predictions in a lab. Researchers at the UK universities of Aberystwyth and Cambridge then independently tested Adam's hypotheses about the functions of 19 genes; 9 were new and accurate, and only 1 was wrong.
"Robot scientists using AI can test more compounds, and do so with improved accuracy and reproducibility, and exhaustive, searchable record-keeping," says systems biologist Steve Oliver of the University of Cambridge, a member of the group that developed Adam.
In January, the same team announced that Adam's more advanced robot colleague, Eve, had discovered that triclosan, a common ingredient in toothpaste, could potentially treat drug-resistant malaria parasites. The researchers developed strains of yeast in which genes essential for growth had been replaced with their equivalents either from malaria parasites or from humans. Eve then screened thousands of compounds to find those that halted or severely slowed the growth of the strains dependent on the malaria genes but...
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