Modern biopharma BD: How to find the diamonds in the rough

Some of the most transformative therapies in biopharma started as overlooked bets.

Take nivolumab (Opdivo), the anti–PD-1 antibody quietly co-developed by Ono Pharmaceutical and Medarex. At the time, immune checkpoint inhibitors were still scientific curiosities, unproven, poorly understood, and commercially ambiguous. But when Bristol-Myers Squibb stepped in to in-license global rights in 2011, they weren’t buying a product, they were betting on a paradigm shift. BMS committed to a bold, mechanistically grounded development strategy, running expansive trials across dozens of indications. That “unloved” asset became Opdivo: the first PD-1 inhibitor approved in the U.S., a cornerstone of modern immuno-oncology, and a multi-billion-dollar franchise that helped rewrite the playbook for cancer therapy.

The takeaway from this story? High-impact assets rarely arrive perfectly packaged. The job of a great BD team isn’t just to spot the obvious — it’s to develop the systems, talent, and risk lens to find and shape the promising outliers hiding in plain sight.

Here’s how the best BD teams do exactly that.

Start with strategic clarity, grounded in first principles

Great BD work starts with a clear understanding of the organization’s strategy, capabilities, and risk appetite. Here’s what to consider when looking externally:

• Scientifically: What are we trying to prove? What internal gaps do we need a partner to fill (e.g., models, modalities, biomarkers)?
• Commercially: What milestone unlocks value? An Investigational New Drug (IND) Application, a biomarker-driven Phase 2, or a financing event?
• Operationally: Can we successfully execute this? Does it fit our bandwidth and burn?

Blueprint Medicines is an example of strategy-first BD. Before expanding into REarranged during Transfection (RET) driven cancers, they outlined the mechanistic space their platform could credibly access and prioritized deals that played into that blueprint.

Don’t just pick a partner, pick the right battlefield

Too often, BD starts with a list of hot areas or flashy platforms. But biotech BD isn’t just about matchmaking, it’s about market-making. That could mean starting with biology, not categories. Ask: if we’re targeting Pathway X, what diseases, beyond our usual focus, are implicated? That broader lens reveals overlooked markets and smarter partnerships.

So how can you pick your battlefield more intelligently?

Start with the biology:

If you’re developing a therapy that hits a particular target, pathway, or modality, step back and ask: What is the full landscape of diseases where this biology is implicated? You might be chasing an oncology indication, but your biology may have equal (or even stronger) relevance in rare neurology or immunology. Why limit your impact (or your partnering options) by staying in one lane?

Zoom out to the therapeutic area:

Different indications come with vastly different regulatory paths, commercial models, and competitive dynamics. Oncology moves fast but faces steep development hurdles. Rare disease offers regulatory tailwinds but demands deep patient engagement. Autoimmunity offers broad coverage and clinical overlap. Each indication reshapes commercialization, regulatory strategy, and BD structure.

For example: Vertex Pharmaceuticals, after decades in cystic fibrosis, mapped alternative channel targets for pain and kidney disease, leading to new internal and partnered programs that expanded their platform’s reach.

Yes, Yes-If, No: The BD triage that builds pipeline discipline

The best BD teams triage with discipline by using one simple, powerful mental model: Yes, Yes-If, and No. This framework forces clarity and speed in an industry where opportunity cost is the biggest barrier. Here’s how it works:

Category	Description	Key Question
Yes	Assets that are a clear fit scientifically, strategically, and operationally. These pass filters on biology (e.g., target/pathway alignment), unmet need, development feasibility, and commercial logic. Moving fast on these is an easy choice.	Is this a slam dunk across science, strategy, and execution?
Yes–If	Promising opportunities that need refinement—better data, tighter IP, clearer positioning, or improved deal terms. These aren’t ready yet, but with the right work, they could be.	Does your team know what needs to be true for this to become a “Yes”?
No	May look shiny on paper or come with big logos, but if they don’t align with your biology, focus, or strategic intent—they’re distractions. Cut them loose.	Are you being honest about what fits your mission and what doesn’t?

This framework is about focus. Every “No” protects your time. Every “Yes–If” challenges your team to unlock value. And every “Yes” is a green light to go full throttle.

Mining the “Yes-If”: Where the hidden gems live

A Yes-If asset provides an invitation to dig deeper. Here are a few questions to ask in that deeper dive:

• If you had one more dataset, would this shift the asset from promising to compelling?
• If your internal strategy evolved slightly, would this become foundational?
• If you layered in your platform, team, or capital, could you create value that others can’t?

Yes-Ifs force you to see potential and assess whether you can make it materialize. That may mean creating a development plan with the partner, seeding a small collaboration to derisk the science, or revisiting your own strategy.

Not every Yes-If becomes a Yes. But if you’re never converting any of them, you’re likely playing it too safe. Discard the Nos, move fast on the Yeses, but spend time on the Yes-Ifs. That’s where your unique insights transform into a competitive advantage.

Use AI to track dynamic assets at scale 

As your Yes-If pipeline grows, the challenge shifts from identification to ongoing signal tracking, and this is an area where AI is essential. The asset landscape evolves daily: new preclinical updates, pipeline changes, mechanisms revalidated. Human teams can’t track it all.

In the Yes-If bucket, AI can help you stay on top of the asset landscape by:

• Mapping the target space: Surface assets targeting similar biology, even unpublished or in stealth mode.
• Tracking changes: Monitor trial updates, regulatory shifts, and competitor signals in real time.
• Scoring triage priority: Evaluate assets based on data robustness and fit with your strategy.

A great BD team builds a systematic edge by seeing what others miss. You can make your business development efforts more successful by codifying your framework into a shared, adaptable playbook that makes your Yes/Yes-If/No criteria legible to everyone, from analysts to VPs. Then, use VibeOne for AI-powered targeting, tracking, and triage, so you can act faster, with greater precision, when conviction strikes.

The next Sovaldi is out there. Will you have the systems, and conviction, to spot it first?