If you're not using artificial intelligence (AI) in your investing process, you're missing out on something that could empower your decision-making unlike anything ever before. Biotech and pharma investing is quite complex, and AI is a great way to slash through the complexity to make it more understandable. And you don't need to know anything about programming a computer or similar issues to get started, either.
All you need is a willingness to experiment and a few leads that'll inform you about what's possible. So here are three ideas for how you can use the AI tools of today to be a better biopharma investor.
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1. Generate investing ideas based on your preferences
One of the easiest ways to use AI in your investing process is to use it for generating ideas that are tailored to your preferences.
For example, let's say you want to invest in some kind of pre-revenue gene-editing stock because you think it'll be a huge area moving forward, and you are OK with some uncertainty, but you don't want to take a big risk by investing in a super early-stage biotech that might fail to commercialize a new medicine and wipe out. Simply ask the AI system like OpenAI's ChatGPT or Microsoft's Bing for a few ideas given what you're looking for and why, and you'll have a list of companies that'll probably contain ones like CRISPR Therapeutics (CRSP -1.35%) as it's on the cusp of seeking regulatory approval for a pair of its programs.
The point isn't to then go and immediately invest in whatever options the AI suggests, though. The idea is to use its output as a starting point for your own research process, which you can then also augment with its help. In the case of a recommendation like CRISPR Therapeutics, the next step would be to confirm that the AI's interpretation of your risk profile and the company's focus is accurate, as AIs aren't yet infallible on matters of fact nor on matters of interpretation. Heading over to the biotech's website to look at its pipeline and investor materials is a good place to start.
2. Explain complicated scientific concepts in simple language and learn how they relate to competitive factors
Perhaps the most powerful and most useful way to implement AI in your biopharma investing is to ask it to explain economically relevant scientific topics that you don't understand, like how a company's technology platform or its therapy is supposed to work and why it's better than what came before in terms of its efficacy or cost characteristics. The BioGPT model that's out now is designed especially for this purpose, but many of the others can do it competently too.
In other words, you don't need to squint at AbbVie's explanations of how its biologic therapies for ankylosing spondylitis work and whether that matters in its long-running competitive showdown with the generic biologics for the condition that are made by Merck and others. You can just ask the AI to explain it to you like you're five years old, and in a few short moments, you'll have a basic understanding of what ankylosing spondylitis is, what AbbVie's drug does, and which factors matter to determine the competitive outlook. Then, you can work on clarifying your remaining points of confusion by asking follow-up questions or by conducting further research yourself.
Note that the AI systems of today aren't universal answer machines, even if they might feel like it sometimes. The biggest value-add is helping you to understand the gist of complex situations that are relevant to your investing thesis. The computer might get the details of an analysis wrong, so don't rely on them for the heavy lifting of your research process -- use them for getting your foot in the door with complicated concepts that you'd otherwise be too intimidated to approach. Even the creators of specialized models like BioGPT estimate that it only answers scientific questions accurately in around 81% of cases, which isn't good enough to make definitive investing decisions on, at least not without doing some work to confirm the findings.
3. Compare and contrast programs, markets, and regulatory designations
Another powerful way to use AI with biopharma investing is to understand the intersection of a company's programs with its target markets and the regulatory environment, not to mention the multidimensional impact of more complicated factors like regulatory designations.
Let's say a biotech business you're interested in, got a fast-track designation from the Food and Drug Administration (FDA), and its competitor in Europe got a similar-sounding designation from the European Medicines Agency (EMA). Normally, it'd take a tremendous amount of research to figure out which company would have the best chance of making it to the market with their candidate first, as you'd need to compare when they submitted their clinical data to regulators, the average timelines for approval for their target indications within each regulatory regime, and about two dozen other important factors.
You'll still need to do a lot of that diligence, but at a minimum, with a query to the AI, you'll be able to immediately understand which elements could be problematic, like if regulators in one jurisdiction had recently been more skeptical toward certain types of therapies than regulators in the other jurisdiction. Or, perhaps more importantly, if the two similar-sounding designations actually imply different levels of regulatory commitment to faster development times.
Remember, you don't need to take the AI's word as truth, but if you aren't trying to learn how to get such a system to work for you right now, there's a good chance that you'll be missing out on a lot of value that's currently being given away for free.
