We’re living in a golden age of drug discovery and development.

Treatments that seemed like science fiction 20 years ago are healing people today.

Not that long ago, the process of drug discovery was like throwing darts.

Scientists discovered them in the lab through trial and error.

For example, when scientists at the Boots Company were searching for a pain and inflammation treatment without the side effects of aspirin or steroids, they tested hundreds of unrelated chemicals in guinea pigs.

They eventually stumbled upon ibuprofen (otherwise known as Advil) in December 1961.

This isn’t a great way to do things.

Despite consuming massive amounts of time and other resources—and depending on luck for success—trial and error dominated drug discovery until fairly recently.

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Now, scientists can design drugs based on an understanding of disease at the molecular level rather than luck. And they’ve developed new technologies like CRISPR gene editing, CAR-T cell therapy, and RNA-based drugs to target many diseases that used to be a death sentence.

Meanwhile, artificial intelligence (AI) has added a new layer to the biotech story that makes it even more exciting.

Everything about traditional drug discovery and development is slow, inefficient, and expensive.

First, you must identify a drug target involved in the disease you want to treat. That could take years of basic research to understand the disease on a molecular level.

Then you need to run countless tests in the lab to confirm that the target plays an important role in the disease, and that targeting it is safe and effective.

Next, you actually have to make a drug to go after that target.

For small molecule pharmaceuticals like the cholesterol drug Crestor, that involves screening millions of molecules, then synthesizing and further testing potentially thousands of the promising molecules to find the best balance of properties like potency, toxicity, and bioavailability.

This can take years and cost millions of dollars. And even after all this effort, about two-thirds of drug programs fail to yield a molecule that’s good enough to advance to clinical trials.

For large molecule biologics like the breast cancer drug Herceptin, the process is even more costly and complex.

If you’re lucky enough to make it this far, then you have the massive clinical trial hurdles. That’s when you test the drug in humans to make sure it’s safe and effective.

Clinical trials come in three main phases. They take years and cost hundreds of millions to billions of dollars.

At the end of the day, the bottom line is that:

• It takes anywhere from 10 to 15 years to move a new drug candidate from the lab to pharmacy shelves.
• It costs anywhere from a few hundred million dollars, up to several billion dollars, depending on the kind of drug and what it’s intended to treat.
• And only about 1 in 10 drug candidates that enter Phase 1 trials receive FDA approval.

So again, we’re talking slow, inefficient, and expensive.

AI is ideally suited to change the game here.

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Thanks to its ability to analyze massive amounts of data and make predictions based on that data, AI has the potential to automate and streamline drug discovery and development to help companies develop drugs faster, cheaper, and with less risk.

Exscientia plc’s (EXAI) AI platform, for example, predicts how small molecules will interact with biological targets to accelerate the identification of promising drug candidates.

The AI allowed the company to discover a new drug for obsessive-compulsive disorder and get it into clinical trials 5X faster than the traditional timeline.

Insilico Medicine used its AI platform, Pharma.AI, to achieve similar speed gains for its discovery of a new lung fibrosis drug. And then there’s Lantern Pharma (LTRN), which is disrupting the cancer space with its AI platform called RADR. It’s allowed the company to compress the timeline of early-stage drug development by 70% so far, while also achieving an 80% reduction in cost.

Advancements like these are transforming healthcare and providing us with unparalleled investment opportunities.

Avoid biotech ETFs.

Biotech is the ultimate stock picker’s market. And which stocks you own really matters.

Pull up a list of the best-performing stocks in a given year. Biotech stocks dominate, just like they are this year.

Yet, the largest biotech ETF—the iShares Biotechnology ETF (IBB)—has been a dud over the past decade. It’s flat while the S&P 500 has more than doubled.

What’s the deal? Biotech companies spend a ton of money on research and development. The average biotech company loses money and never makes a breakthrough. When you buy a biotech ETF, you’re buying a big blob of average.

I suspect that the use of AI in biotech will light a fire under the whole sector, making this a great investment opportunity. But remember, most companies will fail. Be selective when investing in biotech stocks.

I write about AI, biotech, and other disruptive megatrends in my free investing letter The Jolt. Go here if you’d like to join.

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Stephen McBride is Chief Analyst, RiskHedge. To get more ideas like this sent straight to your inbox every Monday, Wednesday and Friday, make sure to sign up for The Jolt, a free investment letter focused on profiting from disruption.

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