Imagine you wanted to design a drug for a new disease, 'Disease X', about which little is known. Imagine then that you have a machine that could use all the available data in the world about Disease X to identify a potential mechanism of disease and use this to predict which molecules within this mechanism could make suitable targets for drugs against the disease. Then, a machine would virtually design a drug targeting these optimal molecules, building it bit by bit and continuously checking with the target's structure to ensure activity at the desired binding site. Once the drug was "built", it could then be synthesised and, following various rounds of in vitro, in vivo, and clinical testing to validate its efficacy, the drug could be used in clinical practice. Although a machine like this does not yet exist, advocates of artificial intelligence (AI) propose that AI has the potential to revolutionise drug design, turning this imaginary scenario -- at least in part -- into a reality.

Artificial intelligence (AI) is a broad and evolving scientific field, and the value it can deliver at various stages of the drug discovery process is now widely accepted in the pharmaceutical industry. This blog seeks to demystify the application of AI in drug discovery, focusing on its key challenges, opportunities and successes. Over one million scientific articles are published every year in the biomedical domain alone, and every new year brings new methods for data collection and more detailed data modalities. While scientists have access to an exponentially increasing amount of knowledge and data, biological data is messy and incomplete; it may contain conflicting or contradicting evidence, suppositions, biases, uncertainty, gaps in knowledge or misclassifications. This prevents us from understanding the full biology landscape and complicates decision making.

Late one January afternoon, British pharmacologist Peter Richardson ran out of his home office and told his wife, "Got it!" She asked what he was talking about and offered a cup of tea. Richardson explained that he had identified a drug that might help people infected with a new virus spreading in China. Richardson's dash was prompted by a finding from artificial intelligence software developed by his employer, BenevolentAI, a London startup where he is vice president of pharmacology. The company has created a kind of search engine on steroids that combines drug industry data with nuggets gleaned from scientific research papers.

Medical researchers are employing AI to search through databases of known drugs to see if any can be associated with a treatment for the new COVID-19 coronavirus. An early success story comes from BenevolentAI of London, which using tools developed to search through medical literature, identified rheumatoid arthritis drug baricitinib as a possible treatment for COVID-19. In a pilot study at the end of March, 12 adults with moderate COVID-19 admitted to the hospital in either Alessandria or Prato, Italy, received a daily dose of baricitinib, along with an anti-HIV drug combination of lopinavir and ritonavir, for two weeks. Another study group of 12 received just lopinavir and ritonavir. After their two-week treatment, the patients who received baricitinib had mostly recovered, according to a recent account in The Scientist.

The drug, baricitinib, is currently marketed by Eli Lilly to treat rheumatoid arthritis. Now, thanks to AI, it is being tested against COVID-19 in a major randomised-controlled trial in collaboration with the U.S. National Institute for Allergies and Infectious Diseases (NIAID) in combination with remdesivir, an antiviral drug from Gilead Sciences that recently won emergency-use approval for COVID-19. Eli Lilly has now commenced its own independent trial of baricitinib as a therapy for COVID-19 in South America, Europe and Asia.

Machine learning can also help accelerate the discovery of drugs to help treat COVID-19. BenevolentAI, a UK AI company and AWS customer, turned its platform toward understanding the body's response to the coronavirus. They launched an investigation using their AI drug discovery platform to identify approved drugs which could potentially inhibit the progression of the novel coronavirus. They used machine learning to help derive contextual relationships between genes, diseases and drugs, leading to the proposal of a small number of drug compounds. In just days, BenevolentAI found that Baricitinib (a drug currently approved for rheumatoid arthritis, owned by Eli Lilly) proved the strongest candidate.

In a rush to find solutions for the Covid-19 pandemic, researchers are deploying machine learning algorithms to trawl through data that might give us more clues about the virus. Some claim to have identified potential treatments based on the data, while others are using it to screen patients or identify those at highest risk. But, like their vaccine and drug counterparts, many of these algorithms are still unproven. With hundreds of research articles describing the use of artificial intelligence or machine learning -- many of them preprints -- it can be difficult to sort out which ones are most effective. "I've heard a lot of hype about machine learning being applied to battling Covid-19, but I haven't seen very many concrete examples where you could imagine in the short- or medium-term something that is going to have a substantial effect," said John Quackenbush, chair of the Department of Biostatistics at the Harvard T.H. Chan School of Public Health, in a phone interview.

As the COVID-19 pandemic continues to infect people across the world, a technological application already familiar to many in the biotech field is lending a key supporting role in the fight to treat and stop it: artificial intelligence (AI). AI is currently being used by many companies to identify and screen existing drugs that could be repurposed to treat COVID-19, aid clinical trials, sift through trial data, and scour through patient electronic medical records (EMRs). The power of AI in COVID-19 is that it is being used to generate actionable information--some of which would be impossible without AI--much more quickly than before. A simple definition of AI is the ability of a computer to rapidly think and learn. AI utilizes machine learning to analyze large amounts of data.

To battle the novel coronavirus that's been linked to Wuhan, China, researchers are using artificial intelligence to discover potential treatments, including already-approved drugs and completely new compounds. At the same time, the pneumonia-like illness has only gotten worse. As of Friday morning, the 2019-nCoV coronavirus had taken the lives of more than 600 people and infected more than 30,000, with cases documented in at least 25 countries. Researchers at the British artificial intelligence startup Benevolent AI say they used the tech to search for existing approved drugs that might be helpful in limiting the virus's infection. Another set of scientists affiliated with Deargen, a drug discovery company based in South Korea, say that they used deep learning to find various available antiviral drugs that could be investigated as a potential treatment (that research has not yet been peer-reviewed). Meanwhile, a Maryland-based biotech company, Insilico, said it used AI to come up with new molecules that could serve as potential medications, and it will now synthesize and test 100 of the compounds, according to Fortune.