Artificial intelligence (AI) technologies have become dramatically more sophisticated in the 70 years since scientist Arthur Samuel developed the first program that could learn on its own — a checkers-playing game that competed against humans.
Not coincidentally, the utilization of AI has also become increasingly more creative and diversified in manners that are much more likely to assist humans, as opposed to attempting to outsmart us.
Whether one is more likely to engage in AI as a consumer, a business professional, or for the management of their healthcare, these tools have become engrained into all aspects of our daily lives.
In the near future, the anticipated continued advancement of AI will bring with it a wealth of innovation that today might still seem imaginary, but could also raise the potential for more significant cybersecurity concerns and related risks.
AI: The Great Disruptor & Collaborator
At Insilico Medicine, an end-to-end AI-driven pharma-technology company based in New York, NY, and Pak Shek Kok, Hong Kong, where physicians are creating an entirely new AI-driven drug discovery pipeline, Alex Zhavoronkov, PhD, is anticipating, among other innovations, that the industry will see the first AI-discovered and/or AI-designed drugs in late-stage trials entering the phase of efficacy validation in 2023. “There are a number of companies at the forefront of that race to the clinic,” said Zhavoronkov, founder and chief executive officer at Insilico. “I also think we will see the use of robotics paired with AI drug discovery and development to further accelerate the process and contribute to validating experiments. Insilico Medicine is playing a leading role on this front. We will be opening our fully autonomous AI-run robotics lab in Suzhou, China, in January.”
When compared to other industries, Zhavoronkov believes the outlook for the medical technology space in regard to AI will be a bit more dramatic moving forward.
“AI is disrupting many industries, but few have the potential to radically improve human life like pharmaceuticals and medical technology,” he said. “This is an industry that has been ripe for disruption – the timeline for drug discovery and approval is long, the costs are massive, and there’s a high failure rate. It is an industry that has trillions of data points from diverse datasets that can be best analyzed and processed with machine learning tools. We’ve already seen many pharmaceutical companies partnering with AI drug discovery biotechs like ours, and I expect this trend to continue. More and more, I believe AI will fundamentally shift the time and cost to deliver new life-saving drugs to patients.”
A More Equitable Future?
While Zhavoronkov says he believes that AI is already contributing greatly to the democratization of technology, he’s encouraged by advancements that lie ahead for his company and the industry at large.
“I have seen the explosive growth of new regional centers of technology excellence in Abu Dhabi, for instance, where they have been able to quickly grow an industry with a combination of government investment, outside investment, and public-private partnerships,” he said. “It has also led to opportunities to develop new treatments where there is a high global need but low investment opportunity that has been traditionally overlooked and underfunded. A good example is the need for non-hormonal contraceptive options. More than 200 million women and girls in low- and middle-income countries who want to avoid pregnancy aren’t using a modern method of contraception, and the Gates Foundation is providing funding to companies, including Insilico Medicine, who can use AI drug discovery to find new options.”
Trust In More Security As AI Advances
With the great innovation that AI continues to generate, the “side effect” will continue to be a level of security that remains just as innovative, or more so. Zhavoronkov said his expectations are that international laws and regulations aimed at data privacy and protection and compliance will become more stringent in the coming years. He also offers some of the strategies that are practiced at his company.
“At Insilico, we use only publicly available data and employ privacy by design and by default,” he said. “We facilitate security of our systems by thorough security analysis on each phase of development. All Insilico data hubs are contained in Amazon Web Services or Microsoft Azure cloud.”
Additionally, Zhavoronkov said there are several checks and balances in place at Insilico to ensure continuous data integrity, protection, and privacy.
Zhavoronkov said that these types of careful considerations are also essential to building and maintaining public trust as AI becomes more invasive for the greater good.
“AI drug discovery is truly a global, collaborative effort, and AI should be thought of as a bridge connecting scientists and innovators across the world who are united by the same shared goal – to improve and extend human life,” he said. “The more we bring together expertise from pharma, government, academia, and industry, the better equipped we will be to navigate any challenges that arise. I think it is important to provide a steady stream of information that helps people to understand the process and the science to reassure the public about AI’s role in developing and delivering new drugs. We must emphasize that safety testing drugs is a rigorous process. Whether or not drugs were designed by AI or produced by an AI-run robotics lab, they must still pass multiple stages of trials, from animals to humans, before being approved. The difference is that these drugs can be developed faster, for less cost, and with a higher likelihood of success.”
Next Step In AI Drug Discovery: Robots
Insilico Medicine is launching its AI-run robotics lab that is expected to be fully operational in 2023. It's being described as a real-world interconnected expansion of the company’s end-to-end AI-driven drug discovery platform and will be run remotely by an AI system with autonomous guided vehicles conducting experiments in place of human scientists.
These robots will reportedly conduct cell culture, high throughput screening, next-generation sequencing, cell imaging, and genomics analysis and prediction. As the robots generate data, that data will feed directly into Insilico's PandaOmics platform, improving the system's target hypotheses and ability to validate those hypotheses. More information on the lab can be found here.