What if we could generate novel molecules to target any disease, overnight, ready for clinical trials? Imagine leveraging machine learning to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000. It’s a multibillion-dollar opportunity that can help billions.
The worldwide pharmaceutical market, one of the slowest monolithic industries to adapt, surpassed $1.1 trillion in 2016. In 2018, the top 10 pharmaceutical companies alone are projected to generate over $355 billion in revenue. At the same time, it currently costs more than $2.5 billion (sometimes up to $12 billion) and takes over 10 years to bring a new drug to market. Nine out of 10 drugs entering Phase I clinical trials will never reach patients. As the population ages, we don’t have time to rely on this slow, costly production rate. Some 12 percent of the world population will be 65 or older by 2030, and “diseases of aging” like Alzheimer’s will pose increasingly greater challenges to society. But a world of pharmaceutical abundance is already emerging. As artificial intelligence converges with massive datasets in everything from gene expression to blood tests, novel drug discovery is about to get more than 100 times cheaper, faster, and more intelligently targeted.
One of the hottest startups I know in this area is Insilico Medicine. Leveraging AI in its end-to-end drug pipeline, Insilico Medicine is extending healthy longevity through drug discovery and aging research. Their comprehensive drug discovery engine uses millions of samples and multiple data types to discover signatures of disease and identify the most promising targets for billions of molecules. These molecules either already exist or can be generated de novo with the desired set of parameters.
Insilico’s CEO Dr. Alex Zhavoronkov recently joined me on an Abundance Digital webinar to discuss the future of longevity research. Insilico announced the completion of a strategic round of funding led by WuXi AppTec’s Corporate Venture Fund, with participation from Pavilion Capital, Juvenescence, and my venture fund BOLD Capital Partners. What they’re doing is extraordinary — and it’s an excellent lens through which to view converging exponential technologies.
Case Study: Leveraging AI for Drug Discovery
You’ve likely heard of deep neural nets — multilayered networks of artificial neurons, able to ‘learn’ from massive amounts of data and essentially program themselves. Build upon deep neural nets, and you get generative adversarial networks (GANs), the revolutionary technology that underpins Insilico’s drug discovery pipeline.
What are GANs? By pitting two deep neural nets against each other (“adversarial”), GANs enable the imagination and creation of entirely new things (“generative”). Developed by Google Brain in 2014, GANs have been used to output almost photographically accurate pictures from textual descriptions.