Session II — Drug Discovery II
Closing the Loop on Biology
Four leaders — Aviv Regev (Genentech), Jonathan Cohen (NVIDIA), Andy Plump (Takeda), and John Reed (J&J) — converge on a single thesis: drug discovery is becoming an iterative learning machine, and the companies that fail fastest and learn cleanest will own the next decade.
Aviv Regev — Lab in the Loop
Lab in the loop: experiment, model, predict, repeat — across targets, indications, medicines, and patients.
Four loops
- Targets — hundreds of thousands of perturbations; single-cell readouts; cross-screen foundation models with Recursion
- Indications — multimodal patient atlases; pathobiology foundation models; target-selection impact has more than doubled
- Medicines — AI delivered 50× better hit rate than a medicinal chemist using standard tools on the same data; de novo antibody design routine
- Patients — AI on 2,000+ OCTs would have flagged a failed phase 3 from phase 2 data; personalized neoantigen vaccine autogene cevumeran (with BioNTech) in pancreatic cancer
Jonathan Cohen — The Compute Stack Coming for Biology
Multiplicative stack
- Instruments → 2. Predictive models → 3. Autonomous agents → 4. Robotic wet labs — each layer multiplies the one beneath it.
Architecture: not one super-agent
Specialized sub-agents under generic reasoning orchestrators (like Claude Code), not a single oracle. Software vendors will ship agents that drive their tools.
Examples
- Parabricks — 30× whole-genome assembly from ~half a day to ~10 minutes on GPU
- CodonFM — mRNA foundation model tokenized at the codon level (~130M sequences, ~5B parameters)
Andy Plump — No Fear, No Ego
Two scars
- TAK-994 (orexin 2 agonist for narcolepsy) — phase 2b liver toxicity; backup molecule from first principles about to be registered
- Myasthenia gravis biologic — years of failed engineering; Nabla Bio returned an in-silico-designed sequence that worked; flipped Takeda’s stance on AI
Takeda’s new Cambridge wet lab will be AI-driven by default, not merely AI-enabled.
Company context
Founded 1781. Under Plump’s tenure: ~15K → ~50K employees; ~30B revenue; Japan from ~50% to ~10%.
John Reed — Pipelines Built on Persistence
Myeloma: average life expectancy from 2 to nearly 20 years
- Velcade → Darzalex → Carvykti (BCMA CAR-T, ~half show functional cure) → Tecvayli + Talvey (T-cell engagers)
- Tecvayli: FDA Commissioner’s National Priority Review Voucher; 55-day approval
Prostate and bladder cancer
- KLK2 — exquisitely prostate-specific, retained on ~90% of prostate cancers, cleaner than PSMA
- Pasritamig — KLK2×CD3 bispecific; durable PSA responses, near-doubled PFS, only 15% all-grade-1 CRS
- RIPTAC platform (Halda) — instead of PROTAC’s E3 ligase, engages an essential protein the cell can’t live without
- Inlexzo — intravesical drug-eluting device (gemcitabine); >80% complete response in high-risk NMIBC
Immunology
- Icotrokinra (Icotide) — oral cyclic peptide blocking IL-23 receptor; five-of-five positive phase 3s
Neuroscience
- Spravato — intranasal esketamine; first rapid-acting antidepressant
- Caplyta — balanced dopamine/serotonin; approved for both bipolar I and II depression
Cross-Cutting Themes
- The loop is the unit of work — Regev’s lab-in-the-loop, Cohen’s agent-plus-robot factory, Plump’s failure cycles, Reed’s persevere-and-redesign
- AI is now load-bearing, not decorative — Nabla’s sequence rescued Takeda’s mAb; Genentech’s models doubled target-selection impact
- Biology — not chemistry — is the bottleneck
- Data sharing is the unlock — pre-competitive consortia, multimodal atlases, Protein Data Bank precedent
- Necessity is forcing the change — cost pressure, pricing politics, unsustainable failure rate
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