The Stanford Drug Discovery Symposium (SDDS) 2026 brought together ~40 speakers across 12 sessions spanning drug discovery science, Nobel-level basic research, venture capital, investment banking, CEO strategy, philanthropy, and federal policy. This wiki distills and cross-references the full proceedings.
The Seven Theses of SDDS 2026
1. The Productivity Paradox Is an Operating-Model Problem
Better science plus more investment has not equalled more productivity. The bottleneck is upstream of the bench — in decision systems, portfolio management, and how organizations source and advance programs. Quigley (Sanofi) articulated this most sharply; Plump (Takeda) and Reed (J&J) provided complementary evidence from their own failures and perseverance.
2. Static Biology Is the Bottleneck; Dynamic Biology Is the Next Decade
Cryptic pockets, IL-17F dynamics, dose-dependent biomarker reversals, 96% of PPIs without structures — Akinsanya’s dynamic proteome thesis runs through the entire summit. The training data for this new biology mostly doesn’t exist yet. Whoever builds those datasets owns what comes next.
3. Human Pathobiology Beats Animal Models — and the Field Is Finally Willing to Say So
From Henry’s IL-17 mouse-vs-human divergence, to Schekman’s “persistent embarrassment” of neurodegeneration models, to Südhof’s iPSC A-beta data contradicting the plaque narrative, to NIH’s formal pivot away from animal-model-exclusive funding — the consensus is clear and new.
→ Human Pathobiology, NAMs, Translation Cliff
4. Elimination Is the New Modulation
The defining therapeutic ambition of the summit: don’t manage disease, eliminate its cellular root causes. CAR-T in cancer and autoimmunity, neoantigen-targeted cell therapy in solid tumors, Baker’s bioPROTACs for complete protein clearance, Adamis’s retinal repair pathways — the word “cure” is no longer forbidden.
→ Immune Reset, Neoantigen Therapy, CAR-T
5. AI Is Load-Bearing, Not Decorative — But Data Is the Constraint
AI rescued Takeda’s failed antibody. Genentech’s models doubled target-selection impact. BMS’s AI broke a months-long chemistry impasse. But the binding constraint has shifted from algorithms to data provenance: public datasets are mostly tapped, and the clinical data corpus is “tiny, tiny, tiny” (Baker). The biggest AI alpha may be in trials, not target ID (Pande).
→ AI in Drug Discovery, Lab in the Loop
6. China Is Simultaneously Competitor, Supplier, and Accelerator
48% of new molecular entities entering clinical development in 2025 were from China (up from 17% in 2015). 150 east-to-west deals last year generated $135B in bio-bucks. Every investor and CEO panelist has a China strategy — or admits they need one.
7. The Regulatory Environment Is Shifting Faster Than Industry Realizes
FDA’s one-trial default, the Plausible Mechanism Framework, the BMD surrogate endpoint, the RAPID program, NIH’s pivot away from animal-model-exclusive funding — these are structural changes, not rhetoric. Companies that understand the new regulatory landscape as an integrated cross-agency system (FDA + CMS + NIH + ARPA-H) will have a competitive advantage.
→ Session XI — Federal Perspectives
The Summit at a Glance
| Session | Theme | Key Speakers |
|---|---|---|
| I — session-i-drug-discovery-i | Productivity paradox, dynamic proteome, human pathobiology | Quigley, Akinsanya, Henry |
| II — session-ii-drug-discovery-ii | Lab in the loop, compute stack, learning from failure | Regev, Cohen, Plump, Reed |
| III — session-iii-vc-perspectives | VC perspectives, China, AI for trials | Hudson, Dadoo, Sinha, Pande |
| IV — session-iv-nobel-laureate-panel-i | Translation cliff, amyloid cautionary tale, exosomes | Bertozzi, Schekman, Südhof |
| V — session-v-rosenberg | CAR-T pioneers: IL-2 → TIL → neoantigen therapy → immune reset | Rosenberg, Sadelain, June |
| VI — session-vi-philanthropy-award | Philanthropy as force multiplier | Joan & Sandy Weill |
| VII — session-vii-drug-discovery-iii | Retinal repair, Maritide, incretin century | Adamis, Bradner, Custer |
| VIII — session-viii-ceo-perspectives | Policy threats, industry advocacy, IRA damage | Magargee, Monia, Bradway |
| IX — session-ix-drug-discovery-iv | Elimination imperative: BMS, Gilead, Bayer | Plenge, Martin, Rose |
| X — session-x-investment-banking | M&A recovery, TechBio, funding playbook | Gupta, Lee, Tokat |
| XI — session-xi-federal-perspectives | FDA one-trial default, NIH animal model pivot | Kozlowski, Tarver, Høeg, Kleinstreuer |
| XII — session-xii-nobel-laureate-panel-ii | A-beta paradox, opioid allosteric modulators, protein design | Südhof, Kobilka, Baker |
Most-Connected Themes
The wiki’s densest cross-reference clusters — the ideas that touch the most pages:
- CAR-T therapy — Sessions II, V (x4), IX; connected to immune reset, neoantigen therapy, multiple myeloma, glioblastoma, lupus, and at least 8 drug/molecule pages
- AI in drug discovery — Sessions I, II, III, VII, VIII, IX, X; touches every company and multiple concept pages
- China strategy — Sessions III, VIII, IX, X; investment, competitive landscape, geopolitics
- Immune reset — Sessions V, IX; the most surprising therapeutic paradigm of the summit
- incretin therapeutics — Session VII; the GLP-1 revolution and its next wave