Silicon Valley Sets its Sights on Curing Diseases with AI

 By the CogX R&I team

June 26, 2024

For an industry notorious for high failure rates and skyrocketing costs, could big tech be the long-awaited disrupter? 

The annual J.P. Morgan Healthcare Conference is typically a gathering of pharmaceutical giants and medical professionals. This year, however, amid the usual sea of pharma execs and medical bigwigs, a new face stood out in the crowd – Jensen Huang, the chief executive of Nvidia.

Huang's appearance signalled a significant shift for the tech giant. Nvidia, the chip powerhouse whose GPUs have been fueling the AI revolution in recent years, was now setting its sights on the world of healthcare and biotech.

In his address to the audience of healthcare and biotech experts, Huang delivered a clear message: the future of medicine is heavily reliant on the capabilities of AI and AI-driven medical instruments.

This pivot isn't unique to the GPU giant. Google's DeepMind has made waves with AlphaFold, a groundbreaking tool for predicting protein structures. This has already led to exciting applications like "molecular syringes" and pesticide-resistant crops. Meanwhile Meta, Microsoft, Amazon, and even Salesforce are all racing to develop their own protein design projects.

Traditionally, bringing a new drug to market could take a decade and cost billions. But recent AI advancements promise to slash this timeline and cost dramatically, potentially by half in the preclinical stage alone. And make no mistake, Investors are taking notice.  

A wave of VC funding, exceeding $9 billion, surged towards ML-driven drug discovery startups from 2019 to 2022. While deal activity peaked in 2021, enthusiasm remains high.

The allure is clear. AI can inject creativity into molecule design and streamline workflows, both crucial for drug discovery. By analysing vast datasets, AI can develop better molecules or uncover entirely new ones for specific diseases. This creates a whole new approach rooted in advanced data driven decision-making.

But cutting-edge tech doesn't guarantee faster, cheaper cures. The biopharmaceutical industry is notoriously complex, with regulations and biological systems that can confound even the most sophisticated algorithms. Big Tech's prowess in data analysis might not translate smoothly to the labyrinthine world of drug trials.

This knowledge gap presents both a hurdle and an opportunity. To succeed, Big Tech companies must acknowledge these differences and bridge the gap. If they can leverage their data expertise, they could truly disrupt the industry.

As AI takes hold in drug discovery, ethical considerations and data privacy also start to become paramount. Balancing the need for patient data to train AI models with individual privacy is crucial. For example, some companies are exploring "federated learning" where AI models train on data that never leaves the hospital.

The next few years will be crucial for AI in drug discovery. Even incremental improvements would be a huge benefit for an industry plagued by high failure rates and skyrocketing costs. But if, as some believe, AI unlocks a whole new way to understand biology, it could revolutionise the entire process, making even the most intractable diseases treatable.

This synergy between AI and biotechnology, driven by both tech giants and agile startups, has the potential to usher in a new era of medicine – one where life-saving treatments are developed faster, more efficiently, and with unprecedented precision. And to be clear, these companies wouldn't be investing heavily in this if there wasn't the potential to make a profound impact on the world (and, of course, turn a profit along the way).