Are Wet Labs Drying Up?

I speak to many suppliers in the life sciences about business strategy and some just to check-in and see how things are going. Overall, what I hear is that revenue is down by a substantial amount. Alarmingly, I was told that a leading reagent supplier is experiencing a 20% decrease in revenue this year. Mind you, these are companies that sell reagents to researchers in lab coats who work in industry and academia pursuing discovery and basic research. The reason, I was told, is AI.

Here is the Explanation

In recent years, the landscape of biomedical research has undergone a significant transformation. Traditional “wet labs,” where experiments are conducted using physical reagents and biological samples, are increasingly complemented—or even supplanted—by “in silico” methods. These computational techniques, often powered by artificial intelligence (AI), enable researchers to simulate experiments and analyze biological data digitally. This shift is driven by various factors, including the escalating costs of wet lab experiments and the rapid advancements in AI technologies.

The Rise of In Silico Experimentation

In silico experimentation refers to the use of computer simulations and models to conduct experiments virtually. This approach has become particularly prominent in drug discovery and development. AI algorithms can process vast datasets to predict how potential drugs will interact with biological targets, assess their efficacy, and anticipate possible side effects. For instance, companies like Insitro leverage machine learning to analyze extensive datasets of chemical and biological markers, aiming to expedite drug discovery and reduce costs(1).

The integration of AI into biomedical research offers several advantages:

? Speed: AI can rapidly analyze complex datasets, significantly reducing the time required for research and development.

? Cost-Effectiveness: By predicting outcomes and identifying promising candidates early, AI minimizes the need for expensive and time-consuming wet lab experiments.

? Precision: AI models can identify specific patient populations that may benefit from targeted therapies, enhancing the precision of treatments.

Economic Considerations Driving the Shift

The transition towards in silico methods is influenced by the substantial costs associated with traditional wet lab experiments. Conducting physical experiments requires significant investments in laboratory infrastructure, reagents, and personnel. Moreover, the drug development process is notoriously expensive and time-consuming, often taking over a decade and billions of dollars to bring a new drug to market.

AI-driven in silico approaches offer a cost-effective alternative. By utilizing computational models to simulate experiments, researchers can identify potential failures early in the development process, thereby conserving resources. This efficiency is particularly beneficial in the pharmaceutical industry, where reducing the time and cost of drug development is a critical objective (2).

Quality and Reliability of AI-Generated Data

While AI has revolutionized biomedical research, it is essential to recognize that the reliance on AI-generated data is not solely due to its quality. Traditional wet lab experiments provide empirical data that are crucial for validating hypotheses and understanding complex biological systems. In contrast, AI models depend on existing datasets and may not always capture the full complexity of biological interactions.

Therefore, the increasing dependence on AI-generated data is largely driven by economic factors rather than the superiority of these methods over traditional experiments. The high costs and logistical challenges associated with wet lab research make AI an attractive alternative, even though it may not fully replicate the insights gained from physical experiments.

The Ideal Scenario: A Synergistic Approach

A balanced integration of in silico and wet lab methods would harness the strengths of both approaches, leading to more robust and comprehensive scientific discoveries. In silico models can generate hypotheses and guide experimental design, while wet lab experiments can validate and refine computational predictions. This synergy would enhance the reliability and applicability of research findings.

Conclusion

The shift from wet lab to in silico experimentation in biomedical research is primarily driven by the high costs and logistical challenges of traditional experiments. While AI offers valuable tools for accelerating research and reducing expenses, it cannot entirely replace the empirical insights gained from wet lab work. A world that provides ample resources for researchers to utilize both AI-driven in silico methods and traditional wet lab experiments would foster a more comprehensive understanding of biological systems. Such an environment would ultimately lead to more effective treatments and a healthier future for humanity.

Let me be completely honest! I used chatGPT to write this article. I then asked it to rewrite the article in the style of Hunter S. Thompson, one of my favorite authors. I must admit that the results gave me chills.

Below is the HST version. Let me know which one you like better.

Are Wet Labs Drying Up?

In the bizarre jungle of modern science, where chaos reigns supreme and truth often comes served on a Petri dish with a side of budget cuts, a curious shift is underway. The wet lab—the hallowed temple of pipettes, centrifuges, and overpriced reagents—is being supplanted. The new gods are digital, conjured from the smoky depths of artificial intelligence. They promise efficiency, speed, and a whiff of sorcery. But don’t light your candles to these silicon saints just yet—this is less a revolution and more a retreat. A retreat from the unforgiving economics of biomedical research, where dreams go to die under the weight of the almighty dollar.

The Rise of In Silico Snake Oil

“Let’s ditch the wet labs,” say the prophets of the machine age, “and march boldly into the pixelated frontier.” What they mean is: let’s feed heaps of data into algorithms and let computers tell us what might work, without ever touching a test tube. AI has barged into the biomedical saloon, slinging promises of faster, cheaper, and smarter science.

Take drug discovery, for instance. Once the playground of chemists hunched over bubbling flasks, it’s now a battleground for AI-powered simulations. Companies like Insitro crank out drug candidates the way Vegas spits out slot losers: quick and dirty, with a few big winners. Algorithms chug through oceans of molecular data, pointing out potential hits before anyone’s donned a lab coat. Efficient? Sure. Reliable? Well, that depends on how much snake oil you like in your science (apnews.com).

The Real Catalyst: The Almighty Dollar

Don’t be fooled by techno-optimism. This isn’t a story about AI’s brilliance—it’s about money. The wet lab is an expensive beast, demanding endless sacrifices: millions in reagents, state-of-the-art facilities, armies of postdocs surviving on ramen and despair. Each experiment is a financial gut-punch.

In silico experimentation, on the other hand, is seductive. It whispers sweet nothings to cash-strapped institutions: “Why spend billions on lab gear and staff when you can just pay for some sweet GPU time?” Even Big Pharma, usually as risk-averse as a cat in a dog park, is getting hooked on these AI simulations. A cheap bet compared to the financial black hole of wet lab R&D (ft.com).

The Devil is in the Data

But here’s the rub: AI doesn’t invent truth; it manipulates the data it’s given. Garbage in, garbage out. AI models depend on existing datasets, which are often incomplete, biased, or downright misleading. Sure, they’re great for identifying patterns, but biological systems are messy, chaotic things. They don’t give a damn about algorithms.

And yet, the financial squeeze keeps pushing researchers into AI’s embrace. Not because it’s better, but because it’s cheaper. A sad state of affairs, where the promise of “cutting-edge science” often translates to “we couldn’t afford the real thing.”

A Call for Balance (and More Damn Funding)

The ideal scenario? A world where wet labs and in silico methods coexist, each compensating for the other’s weaknesses. Let AI crunch the numbers, generate hypotheses, and guide experiments. But let wet labs do what they do best: reveal the messy, beautiful, unpredictable reality of biology.

Science isn’t a zero-sum game. It’s a symphony—or a circus, depending on the day. AI and wet labs should work in concert, not competition. But that dream costs money, and in this age of austerity, funding flows like molasses in January.

Final Notes from the Edge

So here we are, watching wet labs dry up—not because AI is inherently better, but because bean counters prefer algorithms to autoclaves. This is a cautionary tale wrapped in a technological fairytale. AI isn’t the villain, but it’s also not the hero. The real problem is a world where science is starved for resources, forced to choose between the empirical rigor of the lab and the seductive shortcuts of the machine.

Imagine a world where researchers have what they need: state-of-the-art wet labs, robust AI systems, and the freedom to chase wild ideas. That’s the world humanity deserves. Until then, the drying of wet labs isn’t progress—it’s a symptom of our collective failure to prioritize science over short-term savings. And that, dear reader, is the real tragedy.

Original Article Hashtags:

#BiomedicalResearch #AIinScience #InSilico #WetLabs #DrugDiscovery #ArtificialIntelligence #ScienceFunding #ScientificInnovation #ResearchEconomics #FutureOfScience

Hunter S. Thompson-Style Article Hashtags:

#WetLabs #AIRevolution #BiomedicalResearch #ScienceEconomics #DataVsReality #InSilicoMadness #ArtificialIntelligence #DrugDiscovery #ResearchFunding #ScienceAndHumanity