A point-in-time engine that values a pharmaceutical company one drug at a time — each marketed drug as a lifecycle DCF, each clinical asset as a risk-adjusted NPV, plus an R&D renewal engine for the drugs not yet born — then bridges to a per-share intrinsic value.
One company, one as-of date, eleven stages. Data is acquired point-in-time, a three-agent LLM trio extracts the drug portfolio under deterministic gates, the deterministic engine values three legs, and a QC layer quarantines anything it cannot defend. Hover-free, scroll down for the detail behind any stage.
Enterprise value = Σ marketed-drug DCFs + Σ pipeline rNPVs + an R&D renewal-engine platform leg, minus corporate overhead. No portfolio-level revenue assumption is ever needed — value is built bottom-up, drug by drug.
The production engine is deliberately conservative: it runs on well-established literature parameters. A parallel set of feature-tuned ML models is fitted and benchmarked, but each is gated OFF until it clears documented promotion criteria.
Strict point-in-time data discipline, analyst consensus used only as a reference (never an input), and a quality-control layer that flags anything it cannot defend rather than quietly shipping it.
Every input is anchored to the valuation date — the model only ever sees what was knowable then. A historical valuation reads from a frozen snapshot of that date's data, never from today's prices or news, so a backtest can never accidentally "see the future" and a live run can never quietly drift.
Compustat fundamentals, CRSP prices/returns, IBES point-in-time consensus. The reproducible backbone, with as-of dates that avoid look-ahead.
Current market data and analyst consensus for the live valuation. Consensus is used only as a reference point to compare against — never as an input the model fits to.
10-K / 10-Q filings, the drug-level Product Sales table (the source of truth we reconcile drug revenue against), and patent-expiry & risk disclosures. Machine-readable filing data fills any gaps in WRDS.
ClinicalTrials.gov confirms which pipeline programs existed and their phase; market feeds supply price, shares and beta. Companies are matched to filings even after a ticker change or acquisition via a hand-verified identity map.
After Visible Alpha's drug-level feed was retired (credentials lapsed mid-2025), the assembled revenue evidence is built from WRDS + Refinitiv segments and is explicitly tagged is_drug_level = false. Drug-level revenue comes from filings and the agents, reconciled against SEC Product Sales — not from a vendor drug feed.
1 · Textbook cost of capital
cost of equity = risk-free + β · equity-risk-premium
WACC = equity-weight·(cost of equity) + debt-weight·(after-tax cost of debt)
Standard CAPM, with the company's betaHow much the stock moves with the market. It's shrunk toward the market average to damp noisy single-stock estimates. stabilized and the cost of debt inferred from credit quality when the reported figure looks unreliable.
2 · Pharma risk overlay
A bounded add-on for company-specific fragility — small size / thin liquidity, high volatility, short cash runway, and a stretched balance sheet. It can only move the rate within tight limits, so no single factor dominates.
This overlay-adjusted rate is what discounts every leg of the valuation.
Each approved drug is its own discounted cash flow across a four-phase lifecycle. The explicit horizon is adaptive — (LOE − start) + 12 years, capped at 40 — and a continuation tail is added only where it is economically justified.
A single 70% after-tax margin on branded drug revenue (roughly 20% cost of goods + 10% selling & admin), applied uniformly.
A margin table specific to each therapeutic-area × drug-type combination (~30 in all) — e.g. an oncology biologic carries different economics than a cardiovascular pill.
The Inflation Reduction Act lets Medicare negotiate prices, but only Medicare-covered sales are affected. The model applies the cut to just the ~36% of revenue that is Medicare-exposed — not the whole drug — for the years a drug is eligible.
If no erosion profile is supplied, one is chosen from the drug type: pills erode fast, biologics follow a slower biosimilar curve, complex cell/gene therapies slower still, and generics fastest of all.
Each drug is projected explicitly until ~12 years past patent expiry (capped at 40) — long enough to capture durable franchises without assuming a dying pill lasts forever.
Most clinical drugs never reach market, so a pipeline asset is valued by its risk-adjusted NPVRisk-adjusted net present value: the drug's future cash flows weighted by the chance it actually gets approved, minus the cost still needed to develop it. — future revenue weighted by the chance of approval, minus the development cost still to come. The chance of approval is where the live path and the calibrated model deliberately differ.
Two design choices keep it honest. Abandonment floor: a program is never carried at negative value — if the economics turn negative it is simply dropped, exactly as a developer would. Staged cost: the current phase costs 100¢ on the dollar, but later phases are weighted by the chance of ever reaching them — so you only "spend" Phase III money if you get to Phase III.
A curated lookup of phase-transition probabilities across 17 therapeutic areas (Wong 2019 + BIO 2022 + Nature 2025), combined with eight additive modifiers applied at the relevant gate.
Rather than read a published average, this estimates each gate (Phase 1→2, 2→3, 3→Approval) from the drug's own features — learned from 50,749 historical trial transitions (FDA + ClinicalTrials.gov, 2000–2025).
The literature tables give a robust, well-cited average for a drug's category; the feature-tuned model gives a drug-specific estimate from its trial design and sponsor history. Today the production engine values pipelines on the published rates and runs the calibrated model alongside for comparison — a deliberately conservative default that upgrades to the learned model when it has earned it.
Across most areas the Phase II→III transition has the lowest pass rate (≈30–50%) — small-trial efficacy signals that fail to replicate. The model makes Phase II assets worth dramatically less than Phase III.
Biomarker-selected programs approve ~3× as often (25.9% vs 8.4%, BIO 2022). The live path encodes this as a +15pp modifier; the calibrated model learns it directly from the trial-design feature.
Development cost varies by area (CNS ~$750M, oncology ~$650M, rare disease ~$300M from Phase II) and is probability-weighted across remaining phases — not charged up front.
A pharma company is a renewal engine: it keeps spending R&D to launch new drugs that replace the ones going off patent. This leg captures the value of those not-yet-existing drugs — and, crucially, awards nothing to a company whose research has historically destroyed value.
Each year of R&D is credited only with the economic profit it generates — the amount by which a dollar of research historically returns more than a dollar. That stream is capitalized into a perpetuity, but only after the named pipeline has matured, so it never double-counts drugs already valued one-by-one. The R&D figure is also taken net of what's already committed to the named pipeline.
If a company's R&D has historically returned less than it cost, this leg is floored to zero — a value-destroying research engine earns no credit for "future pipeline," no matter how busy it looks.
Only genuinely new, unborn franchises are valued here. The R&D already spent on named pipeline drugs is excluded, because those drugs are valued individually in the pipeline leg.
Capped as a share of the drugs we can already see: large-cap 12%, mid-cap 25%, biotech 70% — halved when the pipeline dominates. Pre-revenue biotechs get none (all their value is in the pipeline leg).
The R&D-return multiple is read from a historical calibration covering 28,762 companies, resolved by how each firm's story actually ended (approval, acquisition, or failure). A present-day valuation uses the most recent year available.
Instead of a company-type average, this predicts a specific firm's next five years of approvals and revenue renewal from its own fundamentals — R&D intensity, margins, balance sheet, acquisition spend, and approval track record — learned from 19,919 company-years.
The hard part of any "future pipeline" estimate is not double-counting drugs you've already valued and not rewarding R&D that doesn't pay off. Crediting only economic profit (returns above cost), starting only after today's pipeline matures, and zeroing out value-destroying research engines handles both — so the platform leg adds genuine going-concern value without inflating the total.
The three legs sum to enterprise value, less a corporate-overhead drag, then standard balance-sheet items bridge to equity and a per-share value. Distressed companies get special handling, and anything the engine cannot stand behind is flagged rather than silently shipped.
The engine checks its own work at every stage. When a result rests on inputs it can't stand behind, it marks the value as not publishable and attaches the caveats — so a weak number is clearly labelled rather than quietly passed off as solid.
Live valuations, historical replays, and re-runs all use one identical, version-locked set of parameters — so a result can't drift just because it was produced on a different day or path.
Every company carries a transparency score for how much of its revenue is pinned to specific, identified drugs versus thin or inferred data — computed only from what's observable, never from the stock price.