AI-Powered Drug Development Against Coronavirus Outbreak

In recent years, beyond the hype of artificial intelligence, the technology has gained criticism more than appreciation. Many experts fear its misuse and less collaborative behavior along with humans. However, as the coronavirus pandemic is spreading, AI is proving its worth above all. So far the technology has served more good than bad amid the outbreak. AI has proven the world that it can serve mankind with utmost sincerity. From assisting doctors to supplying emergency materials in the hospitals, AI has marked its value. Besides, technology is playing a great role in drug discovery and vaccine development against the COVID-19 virus.

In January, Google DeepMind introduced AlphaFold, a cutting-edge system that predicts the 3D structure of a protein-based on its genetic sequence. In early March, the system was put to the test on Covid-19. DeepMind released protein structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes Covid-19, to help the research community better understand the virus.

AI can spot patterns in data and make predictions, and the hope is these tools could identify drug prospects to test on humans within months. As coronaviruses such as COVID-19 mutate, a drug candidate will have to be effective against a broad spectrum of possible forms. Work is now underway worldwide, from Hong Kong to Israel and the US, to use AI in pursuit of a vaccine. Insilico Medicine Inc., Iktos, Vir Biotechnology Inc., Moderna Therapeutics and Atomwise are among the companies employing AI to discover potential coronavirus medicines. Estimates vary, with the consensus saying it will take at least a year, possibly longer, to develop an effective vaccine and bring it to market.

Iktos, a company specialized in artificial intelligence (AI) for novel drug design and SRI International (SRI), a research center headquartered in Menlo Park, California, has announced that the companies have entered into a collaboration agreement designed to accelerate discovery and development of novel antiviral therapies. Under the collaboration, Iktos' generative modeling technology will be combined with SRI's SynFini™, a fully automated end-to-end synthetic chemistry system, to design novel, optimized compounds and accelerate the identification of drug candidates to treat multiple viruses, including influenza and the Wuhan coronavirus (COVID-19).

Iktos' AI technology, based on deep generative models, aims to help bring speed and efficiency to the drug discovery process by automatically designing virtual novel molecules that have all of the desirable characteristics of a novel drug candidate. This could tackle one of the key challenges in drug design: rapid and iterative identification of molecules that simultaneously validate multiple bioactive attributes and drug-like criteria for clinical testing.

Alex Zhavoronkov, the co-founder and CEO of Insilico Medicine, a Hong Kong-based biotech company, has repurposed its platforms for the fight against COVID-19. It aims to use "a huge Lego system" of machine learning techniques for drug discovery — and, in this case, Insilico's algorithms have dreamed up tens of thousands of unique molecules that could potentially handicap the protein responsible for the virus' spread.

The company's approach isn't without risk. In order to increase the potential drug's selectivity and decrease side effects, Insilico is banking on non-covalent-binding molecules. Unlike other treatments, these will only bind to what they're designed for one type of COVID-19 protein. That means there's less of a chance of these molecules being successful — but if they do meet their goal, it'll ultimately be more accurate than another approach.

The company has claimed that it can dramatically accelerate the drug development process using machine learning — and that it hopes to begin testing on humans within a couple of months.

Moreover, researchers from the University of Texas at Austin and the National Institutes of Health used a popular biology technique to create the first 3D atomic-scale map of the part of the virus that attaches to and infects human cells—the spike protein. The team responsible for this critical breakthrough had spent years working on other coronaviruses, including SARS-CoV and MERS-CoV. One of the predictions released by AlphaFold provided an accurate prediction for this spike structure.

Another effort at the University of Washington's Institute for Protein Design also used computer models to develop 3D atomic-scale models of the SARS-CoV-2 spike protein that closely match those discovered in the UT Austin lab. They are now building on this work by creating new proteins to neutralize coronavirus. In theory, these proteins would stick to the spike protein preventing viral particles from infecting healthy cells.