When discussing Artificial Intelligence (hereafter AI), many envisage computer systems that are able to take on similar cognitive functions to the human brain and worry that there will no longer be a place for many skilled workers in key industries. The image of mass unemployment due to robot Armageddon is a common one portrayed in the media. While, some of the potential benefits of the rise of intelligent computer systems, for example in the pharmaceutical industry, are often ignored.
In essence, AI is intelligence exhibited by machines. The machine takes in the information, just like the human brain, interprets the complex data and is able to predict an outcome. Some of the activities computers with artificial intelligence are designed for include speech recognition, learning, planning and problem solving (Techopedia, 2017). In the pharmaceutical industry, AI can be used to tremendously enhance the rate of the drug discovery process which, as Professor Jackie Hunter points out, “needs to shift dramatically in order to meet the needs both of society and patients in the 21st Century” (Drug Target Review, 2016). It is estimated that it costs £1.12 billion, and up to 12 years, to develop a single drug (Association of the British Pharmaceutical Industry). This is because research and development, one of the most essential areas in drug discovery, involves undertaking hundreds, often thousands, of tests to get a solid idea of how molecules behave and how likely those molecules are to make a useful drug. Yet, large pharmaceutical companies are beginning to recognise this incredibly time-consuming and costly effort. For example, this year GlaxoSmithKline unveiled a new $43 million AI drug development deal with Exscientia. This is not surprising given that that ‘intelligent’ algorithms can process far more data in a short space of time than the human brain.
The drug discovery process involves many stages. During initial stages, researchers have pointed out that many drugs will identify as effective. For instance, a drug may show to effectively bind to a bacterium in such a way that it can’t produce a protein it needs to survive. Yet, as Hill (2017) explains, despite early promise, many drugs will fail at later stages of the process. This makes the process inefficient and fairly cost-ineffective given that pharmaceutical companies spend a large amount of money on early steps while many targets fail at later stages. What AI can do to aid this process is provide a way for the industry to be more effective and hence cut down its losses. It does so by introducing algorithms which are trained using academic literature and existing studies. As Hill points out, “These algorithms discover patterns in chemical structures and can be used to produce drugs that are specific for the target in question” (Hill, 2017).
It is therefore not surprising to see that pharmaceutical giants like GSK are investing in AI. Indeed, others such as Merck, J&J and Sanofi are also looking into how they can leverage AI to cut losses in their discovery processes. As more pharmaceutical companies come round to the idea of investing in AI, perhaps we will, in time, see a rise in new drugs on our shelves. We might also see the introduction of new companies similar to Benevolent AI, an “artificial intelligence company… with a focus in health and drug development.”
“Artificial Intelligence (AI)” Techopedia
Jackie Hunter (5 December 2016) “How artificial intelligence is the future of pharma” Drug Target Review
(3 July 2017) “RPT-Big pharma turns to AI to speed drug discovery, GSK signs deal” Reuters
Rebecca Hill (3 July 2017) “Megacorp GSK inks AI drug development deal with Brit firm” The Register
Shona Ghosh (24 April 2017) “A British tech unicorn is trying to cure Alzheimer’s and ALS with artificial intelligence” Business Insider
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