What Decades in Pharma Taught Me About De-risking Drug Discovery

Drug discovery has always been a paradox: despite decades of progress, it remains slow, costly, and fraught with risk. Even after years of investment, promising therapies for Alzheimer’s, Parkinson’s, cancer, and other devastating diseases often falter late in development—when the stakes and costs are highest.

Few people understand this better than Eric Parker, PhD. Over a 30-year career at Merck, Schering-Plough, and Bristol Myers Squibb, he led teams that advanced therapies for neurodegeneration, oncology, and psychiatric disease. His work has underscored a central truth: success depends not only on bold ideas, but on rigorous strategies to reduce risk early and often.

Now, as Senior Biology Advisor at FAR Biotech, Eric brings that experience to the next generation of drug discovery. In this conversation, he reflects on the lessons he’s learned in big pharma, the trends reshaping neuroscience and oncology, and why he believes FAR’s Quantum Mechanics-powered approach is opening doors to targets once thought undruggable.

What have your experiences in pharma taught you about what it really takes to bring a new therapy from concept to clinic?

EP: That’s a big question—we could spend the whole interview on it. But let me highlight a few lessons that really stand out and that are especially relevant to what we’re doing at FAR Biotech.

First, the quality of the chemical matter you start with is critical. In small-molecule drug discovery, you want a lead compound that not only looks promising structurally but also avoids what we call “structural alerts”—chemical features known to cause toxicity. You also want a molecule that has clear paths for optimization, whether that’s making it more potent, improving its pharmacokinetics, or addressing solubility. If you start with poor chemical matter, you’ll spend years and millions of dollars going nowhere.

Second, target selection is everything. Ideally, you want human genetic validation—that is, evidence from population data that a mutation either causes or protects against disease. That’s the strongest signal we have that a target really matters for disease biology. Beyond that, strong biological and pharmacological validation helps to ensure the target is central to the disease pathway.

Third, structural information is increasingly important. Having a three-dimensional model of the target and knowing how molecules bind to it lets chemists make far more intelligent decisions about how to improve binding and efficacy. It gives you a roadmap for drug optimization.

What trends in neuroscience and oncology drug discovery have you seen come and go—and which ones will define the next decade?

EP: In neuroscience, one trend that is thankfully fading is over-reliance on animal models that don’t translate well to human conditions. Psychiatric diseases like depression, anxiety, or schizophrenia are almost impossible to model accurately in animals. Even with neurodegenerative diseases like Alzheimer’s or Parkinson’s, our models are imperfect. Some are useful, but they rarely capture the full human condition.

The field is shifting toward human-relevant models. Induced pluripotent stem cells (iPSCs) are one example—you can reprogram a patient’s skin cells into neurons and study the biology in a much more relevant way. Organoids are another. Essentially, you’re creating a mini-organ in a dish. These systems aren’t perfect yet, but they’re powerful tools that will shape the next decade of neuroscience drug discovery.

In oncology, the major shift has been realizing how central the immune system is to both cancer progression and treatment. Tumors evade the immune system in remarkable ways. But if you can flip that equation—stimulate the immune system to recognize and attack tumors—you unlock a powerful therapeutic approach. That’s the foundation of immuno-oncology, and it’s transforming the field. The modalities are diverse—small molecules, antibodies, cell therapies—but the principle is the same: leverage the immune system against cancer.

You’ve emphasized the importance of translational science—PK/PD, biomarkers, modeling and simulation. Why are these so often overlooked in early-stage biotech, and what’s the cost when they are?

EP: Drug development is expensive. Depending on the estimate, it costs $1–2 billion to bring a drug to market, and most of that expense comes in Phase 2 and Phase 3 trials. If a drug is going to fail, you want it to fail early—before those expensive stages.

That’s where translational tools come in. Biomarkers, PK/PD modeling, and early simulations give you ways to test whether your drug is hitting the target, whether it’s doing so at the right dose, and whether it’s causing the biological effect you expect. These are things you can often assess in Phase 1 trials, sometimes even in healthy volunteers. Without those tools, you risk moving a compound forward blindly—and spending huge amounts of money on a drug that was doomed from the start.

How is FAR Biotech thinking differently about de-risking early drug discovery?

EP: The biggest advantage of FAR Biotech’s approach is that it produces chemical matter other techniques can’t. That’s especially important for so-called “undruggable” targets—proteins that are notoriously hard to bind. Martin’s Quantum Mechanics-powered methods generate options where traditional approaches often come up empty. That gives chemists more and better starting points, which opens the door to tackling targets that have eluded drug developers for years.

You work closely with early-stage companies. What do you think investors often misunderstand about what’s truly ready to scale in biotech?

EP: I think sometimes investors chase the shiny object. A flashy paper comes out in Nature or Science, and suddenly everyone wants in. But not all that glitters is gold. Sometimes the data just isn’t strong enough, or the target hasn’t been validated well enough, to justify scaling. I’ve seen plenty of cases where I’ve thought, “I wouldn’t put my money there,” but the herd mentality takes over.

The best investors are the ones who take the time to understand the biology and the strength of the evidence. That’s what separates good bets from costly mistakes.

What advice would you give to family offices or VCs trying to assess the quality and depth of a drug discovery platform like FAR Biotech’s?

EP: Make sure you have people on your team who truly understand the science. There’s so much hype around AI in drug discovery right now. Everyone feels pressure to invest in “AI for biotech,” but not all platforms are doing something meaningful.

The right questions to ask are: What exactly is this platform doing? How is it different from others? What evidence do you have that it works? What kinds of targets can it address that others can’t? In FAR Biotech’s case, the application of rigorous Quantum Mechanics sets it apart, but investors need to understand why that matters.

What advances do you think we’ll see at the intersection of AI, Quantum Mechanics, and drug discovery in the next 5–10 years?

EP: I think we’ll see big advances even in the next year or two. One is protein structure prediction. Instead of spending months crystallizing proteins or running cryo-EM experiments, AI can now predict structures directly from amino acid sequences. That’s a huge leap. And the tools are improving quickly.

Combine that with Quantum Mechanical modeling—what FAR is doing—and you get a powerful engine for discovering novel molecules. Many groups use docking techniques, which are fine but limited. FAR’s methods go much deeper by solving the actual equations of Quantum Mechanics to predict interactions. That’s a fundamentally different level of rigor.

What excites you most about the work you’re doing right now with FAR Biotech?

EP: For me, it’s the chance to identify lead structures for targets that have never been successfully drugged. I’ve always gravitated toward the frontier—being the first to crack a target, not the fifth. FAR Biotech’s technology makes that possible. Out of the roughly 20,000 proteins in the human proteome, only a small fraction have drugs against them today. There’s a vast landscape still to be explored, and FAR gives us a way in.

Anything else you’d like to add?

EP: It’s just been a lot of fun working with this team—Martin, Ravi, and the others. I’ve worked with Ravi before at Merck, and he’s an exceptional chemist. Martin brings the theoretical and computational perspective, and Ravi and I bring the drug discovery experience. It’s a great mix of expertise, and that makes the work both productive and enjoyable.

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We’ll be sharing more conversations like this with the people guiding FAR Biotech’s science and strategy. If you’d like to follow along as we explore how Quantum Mechanics and human experience come together in drug discovery, connect with us on LinkedIn or visit farbiotech.com.

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