Part I
A bet on simulation
“Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”
McCarthy, Minsky, Rochester & Shannon · Dartmouth proposal · August 31, 1955
The field began with a wager. If something can be described precisely enough, a machine can simulate it. Seventy years later the same wager has moved into biology: describe the protein, the cell or the patient precisely enough, and drug discovery becomes engineering.
The history of that wager is a reminder that progress is never a straight line. Eighty years of AI include two winters, each following a period when expectations ran ahead of what the methods could deliver.
Part II
The molecule that could not
RG7112 was designed to free the tumor suppressor p53 by blocking MDM2, the protein that holds p53 in check. Many tumors keep p53 silent this way, so releasing it is an elegant idea: switch the cell’s own brake back on.
The molecule was built with the best tools of its day, and it passed every test those tools could set. Step through its development to see where it was stopped.
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Binding assays identified the nutlins, the first selective small-molecule antagonists of MDM2.
Vassilev et al., Science 2004
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X-ray crystallography showed how the molecules sit in MDM2’s binding pocket (PDB 1RV1; RG7112 bound, 4IPF). In a binding assay, RG7112 reached an IC50 of 0.018 µM.
Vu et al., ACS Med Chem Lett 2013
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Structure-guided chemistry tuned potency and metabolism.
Vu et al., ACS Med Chem Lett 2013
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Mouse pharmacokinetic modeling showed about four times the exposure at half the dose, with a half-life of 8.8 hours.
Vu et al., ACS Med Chem Lett 2013
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In SJSA-1 osteosarcoma xenografts, RG7112 inhibited tumor growth by 74% at 50 mg/kg and caused regression at 100 mg/kg.
Vu et al., ACS Med Chem Lett 2013
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Toxicology studies supported a favorable safety profile. Every preclinical gate was cleared.
Vu et al., ACS Med Chem Lett 2013
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In patients with MDM2-amplified liposarcoma, the drug did exactly what it was designed to do. And still it failed.
Benefitp53-pathway activation confirmed in tumor biopsies. Only one confirmed partial response.
HarmSerious adverse events, including neutropenia and thrombocytopenia.
Ray-Coquard et al., Lancet Oncol 2012
It hit its target and missed the patient.
Part III
New tools of the trade
“For the very first time in human history, biology has the opportunity to be engineering, not science.”
Jensen Huang, 2023
A decade on, the toolkit looks transformed. Four families of models now cover the steps RG7112’s chemists did by hand.
Predicting how molecules fit together
AlphaFold 3
Predicts the joint structures of complexes: proteins, nucleic acids, small molecules and more.
Abramson et al., Nature 2024
RFdiffusion
A diffusion model for de novo design of protein structure and function, such as new binders.
Watson et al., Nature 2023
Reading the genome’s regulatory code
AlphaGenome
Takes 1 Mb of DNA in and predicts thousands of functional tracks at up to single-base resolution. Matches or beats the best external models in 25 of 26 variant-effect evaluations.
Avsec et al., Nature 2026
Evo 2
40 billion parameters, a 1 Mb context, trained on more than 9 trillion nucleotides. Predicts and designs across DNA, RNA and proteins.
Brixi et al., Nature 2026
Writing proteins evolution never made
ESM3
A multimodal generative language model that reasons over protein sequence, structure and function. It produced a bright new fluorescent protein only 58% identical to known ones: a distance equivalent to about 500 million years of evolution.
Hayes et al., Science 2025
Language models join the lab
TxGemma
Language models (2B, 9B and 27B) that predict therapeutic properties with natural-language reasoning. They match or beat the best generalist model on 64 of 66 tasks. Wet-lab validation is still a future step.
Google DeepMind, 2025
AI co-scientist
A multi-agent system built on Gemini 2.0 that generates novel hypotheses and research proposals.
Google, 2025
Part IV
Would today’s tools catch it?
“It takes ten years and billions of dollars to design just one drug. We can maybe reduce that down from years to maybe months or maybe even weeks.”
Demis Hassabis, 2025
The promise is speed. The first clinical results look encouraging, until they reach the stage where efficacy in patients is tested.
Analysis of AI-native biotech pipelines · Jayatunga et al., Drug Discov Today 2024
Phase I mostly tests whether a molecule is safe enough to dose. Phase II is where it first has to help patients, and that is where the advantage disappears. To see why, run RG7112 through today’s toolkit.
Within the models’ view
- Binding pose in MDM2 from AlphaFold-class structure prediction
- Potency and drug-like properties from property models
Outside it
- On-target p53 activation in bone-marrow progenitors, which led to neutropenia in 6 patients and thrombocytopenia in 3
- The therapeutic window in humans
The toxicity was not an error in the chemistry. RG7112 did what it was designed to do, in human tissue the models never see. Ray-Coquard et al., Lancet Oncol 2012
Part V
Why Eroom’s law won’t break
Moore’s law, spelled backward. Since 1950, the number of new drugs approved per billion US dollars of R&D has halved roughly every nine years: about an 80-fold decline, adjusted for inflation, despite huge advances in science, technology and management. AI-designed molecules have not bent the curve yet.
Schematic of the published trend, not individual data points: an 80-fold decline from 1950 to 2010, inflation-adjusted, halving roughly every nine years. Scannell et al., Nat Rev Drug Discov 2012
What we know often surprises us
Single driver mutations are the exception: most patients with metastatic non-small cell lung cancer and other advanced cancers may not have tumors with a single driver. And even a true driver depends on context.
Feedback signaling through EGFR in colorectal tumors explains part of the gap. Chapman et al., NEJM 2011 · Kopetz et al., J Clin Oncol 2015 · Khozin, Kim & Pazdur, Nat Rev Drug Discov 2017
Our gold standards can be blunt tools
Animal models are the standard gate between the bench and the clinic. In oncology, they predict human toxicity little better than a coin toss.
Animal-to-human toxicity in oncology · Atkins et al., Br J Cancer 2020 · FDA, Roadmap to Reducing Animal Testing, 2025
Part VI
What’s working today
Engineering at full speed
Where the biology is already understood, AI-assisted design is paying off. The clearest signal is in licensing: China-origin assets now account for a large share of big-ticket deals, built by applying fast, AI-assisted design at full scale to validated targets.
J.P. Morgan, Q2 2026 Biopharma Licensing and Venture Report
These molecules are mostly not first in class. They are better versions of known answers, and no new biological understanding is required to make them.
The quiet wins are in the back office
Ambient scribes
Capturing clinical documentation.
Documents
Document management and drafting of regulatory submissions.
Workflow
Automation across business operations.
These improve efficiency, not biological insight.
Part VII
Why biology isn’t moving
Four obstacles keep AI’s progress in engineering from turning into progress in biology.
1Fragmentation
Of data. Trials, health records, imaging, omics and outcomes sit in separate silos. A model learns only the slice it can see, and no single slice contains the patient.
Of work. AI itself was the by-product of one conversation across disciplines. In the 1940s and 1950s, neuroscience (McCulloch and Pitts’s neuron as a logic unit, 1943), mathematics (Turing’s work on logic and computability, 1936 and 1950) and computer science (stored programs and the Dartmouth workshop, 1956) were in the same room. Today they are super-specialized. Medicine and AI both need multidisciplinary work, and multidisciplinary data.
Suppose someone invented a biosensor and wanted to use it to make life-and-death diagnoses.
The reasonable answer today: that’s premature. Convene a workshop. Talk to the FDA.
It is the electrocardiogram.
- 1887Augustus Waller records the heart’s electrical potential from the skin.
- 1903Willem Einthoven’s string galvanometer is ready for use.
- 1905On March 22, Einthoven, a physiologist, and engineer Johannes Bosscha transmit an ECG by telephone line, about 1.5 km.
- 1924Einthoven receives the Nobel Prize.
Physiology and engineering, working together, moved it quickly into practice. The ECG, the X-ray (a physicist’s discovery turned clinical tool) and MRI (physics, chemistry and medicine combined) all came from medicine and engineering sharing a bench. Each would be hard to develop today, across disciplinary and regulatory barriers.
Hjelm & Julius, J Telemed Telecare 2005 · National Academy of Sciences
2Arbitrary gold standards
Human sensory input is an unreliable instrument. Much of the ground truth AI is trained on comes from people looking at images. In an FDA meta-analysis of 13,677 patients in registration trials, the investigator and an independent radiologist reading the same scans disagreed on tumor response about 30% of the time, and up to 45% in ovarian and pancreatic cancer. Assessments by eye vary within and between observers, in radiology and histopathology alike.
Explore the dataThe full reader-agreement matrix, cell by cell →
The labels themselves are historical accidents. Consider what “lung cancer” means.
“Lung cancer”: an early surgical observation of where a tumor sits.
“Small cell” vs “non-small cell”: what a pathologist sees through a microscope. “Non-small cell” defines a cancer by what it is not.
Distinct molecular drivers, grouped together as one disease.
Train AI on these labels and, at best, it learns to reproduce them faster: faster horses, not biological insight.
Khozin et al., FDA meta-analysis, 2017 · Khozin, Clin Transl Sci 2024
3Medicine is not SaaS
Ship it
A regulatory clearance feels like a launch. Technology companies and their investors shy away from clinical trials.
Prove it
Medical products need clinical validation. A 510(k) clearance alone does not win clinicians or payers.
The cost of confusing the two is real: Pear Therapeutics, a pioneer of FDA-authorized prescription digital therapeutics, filed for bankruptcy in 2023. Meanwhile the regulatory door to simulation has opened faster than industry has used it.
- Oct 2024
FDA Science Board report on new methods
- Apr 2025
Plan to phase out animal testing; AI toxicity data welcomed in IND applications
- Apr 2025
Roadmap: organ-on-chip, computational models, in vitro assays
- Mar 2026
Draft guidance: a validation framework
- Next
No validated simulation submission yet
The door to simulation is open. No one has walked through.
4Limited knowledge of human biology
1–5%?
A plausible share of human biology that has been mapped: a sliver. And that counts only the known unknowns. What is truly known may be far less.
The human genome encodes about 20,000 proteins, many still uncharacterized, and research keeps returning to the ones already well studied. By one systematic ranking of how much is known about each protein family, nearly a quarter of human families (23%) still sit at the very bottom. A simulator cannot simulate what no one has measured.
Rocha et al., PLOS Biol 2023
Part VIII
The way forward
Labels should come from mechanism, not from human sensory input
From organs to algorithms: a bottom-up classification of disease that reduces reliance on subjective human interpretation.
| Dimension | Current classification | AI-driven classification |
|---|---|---|
| 01Primary basis | Anatomical location and histology | Molecular, imaging and histopathological data, integrated |
| 02Precision | Limited by inter- and intra-observer variability | Potentially more objective and reproducible |
| 03Novel features | Limited ability to find new subtypes | Can identify de novo features and patterns |
| 04Biological relevance | May group heterogeneous cancers together | Aims to reflect underlying disease mechanisms |
| 05Treatment matching | Often suboptimal | Potential for more precise selection |
Adapted from Table 1, Khozin, Clin Transl Sci 2024
From big data to smart data: study the whole patient
Moving from a reductionist center, a single structured table, to a holistic edge that spans the volume, variety, velocity and veracity of data: records, trials, sensors, imaging and omics, joined around one patient.
Explore the figureThe four V’s capacity map, interactive →
Three moves to consider
- 01
Fund mechanism-based labels
Replace labels from the human eye with molecular and multimodal ones.
- 02
Build whole-patient data
Link trials, records, imaging and omics.
- 03
Validate where decisions are made
Measure AI against human outcomes, not benchmarks.
Better molecules are not better medicines.
AI’s biggest opportunity is not designing the molecule. It is discovering the human biology that decides whether it works. RG7112 hit its target. It missed the patient.
“We can only see a short distance ahead, but we can see plenty there that needs to be done.”
Alan Turing, 1950
Connections
Where this theme meets the rest of the work. Future themes will link here as they are added.
- ResearchInter-reader response classification: the 13,677-patient matrix behind “arbitrary gold standards”
- INFORMEDFrom big data to smart data: the four V’s and the systems view of drug development
- ConvergenceDisease classified by mechanism instead of anatomy, inside a learning system