Topology · Themes in depth01 theme

Topology

In-depth examinations of the themes that run through Dr. Khozin’s work and research. Each follows one argument from the evidence to what should change, and links to the others where the ideas meet.

  1. 01 AI · Drug discovery · Human biology The Simulator’s Dilemma Why better molecules are not better medicines.
Theme 01 · The Simulator’s DilemmaAI · Drug discovery

The Simulator’s Dilemma

Better molecules are not better medicines.

Artificial intelligence can now predict how molecules fit together, read the genome’s regulatory code and write proteins evolution never made. Yet the productivity of drug development has not moved. This theme follows one molecule through the tools of its day and through today’s, and argues that AI’s biggest opportunity is not designing the molecule. It is discovering the human biology that decides whether the molecule works.

  1. 01

    The tools are real

    Structure prediction, genome models and protein language models are a genuine step change in how molecules are engineered.

  2. 02

    The outcomes have not changed

    AI-discovered molecules pass Phase I at 80–90% but Phase II at about 40%, in line with history. Drugs per R&D dollar keep falling.

  3. 03

    The bottleneck is biology

    Fragmented data, labels drawn from human perception, products shipped without clinical proof and how little of human biology is mapped limit what any model can learn.

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.

Figure 1Eighty years, two winters. Select a period.

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.

Figure 2RG7112, stage by stage

  1. Binding assays identified the nutlins, the first selective small-molecule antagonists of MDM2.

    Vassilev et al., Science 2004

  2. 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

  3. Structure-guided chemistry tuned potency and metabolism.

    Vu et al., ACS Med Chem Lett 2013

  4. 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

  5. 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

  6. Toxicology studies supported a favorable safety profile. Every preclinical gate was cleared.

    Vu et al., ACS Med Chem Lett 2013

  7. In patients with MDM2-amplified liposarcoma, the drug did exactly what it was designed to do. And still it failed.

    Benefit

    p53-pathway activation confirmed in tumor biopsies. Only one confirmed partial response.

    Harm

    Serious 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.

80–90%Phase I success for AI-discovered molecules
~40%Phase II success: in line with historical industry rates

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.

Figure 3RG7112 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.

Figure 4Drugs approved per billion R&D dollars, relative to 1950. Drag through the years.
1950The baseline: drugs approved per billion R&D dollars

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.

Figure 5Vemurafenib, same mutation (BRAF V600E), objective response

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.

108Oncology drugs studied
0.65Median positive predictive value
0.50Median negative predictive value
90%+Of drugs safe and effective in animals that are not approved in humans

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.

42%China-origin share of large-cap licensing deals ($50M+ upfront), H1 2026
68%Of upfront dollars in those deals

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.

  1. 1Fragmentation
  2. 2Arbitrary gold standards
  3. 3Medicine is not SaaS
  4. 4Limited knowledge of human 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.

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.

Surgeon’s eye

“Lung cancer”: an early surgical observation of where a tumor sits.

Pathologist’s eye

“Small cell” vs “non-small cell”: what a pathologist sees through a microscope. “Non-small cell” defines a cancer by what it is not.

Underneath

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

Software playbook

Ship it

A regulatory clearance feels like a launch. Technology companies and their investors shy away from clinical trials.

Medical product playbook

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.

  1. Oct 2024

    FDA Science Board report on new methods

  2. Apr 2025

    Plan to phase out animal testing; AI toxicity data welcomed in IND applications

  3. Apr 2025

    Roadmap: organ-on-chip, computational models, in vitro assays

  4. Mar 2026

    Draft guidance: a validation framework

  5. 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.

Current classification AI-driven classification
01Primary basisAnatomical location and histologyMolecular, imaging and histopathological data, integrated
02PrecisionLimited by inter- and intra-observer variabilityPotentially more objective and reproducible
03Novel featuresLimited ability to find new subtypesCan identify de novo features and patterns
04Biological relevanceMay group heterogeneous cancers togetherAims to reflect underlying disease mechanisms
05Treatment matchingOften suboptimalPotential 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

  1. 01
    Fund mechanism-based labels

    Replace labels from the human eye with molecular and multimodal ones.

  2. 02
    Build whole-patient data

    Link trials, records, imaging and omics.

  3. 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.

References

Dartmouth proposal, 1955A Proposal for the Dartmouth Summer Research Project on Artificial IntelligenceMcCarthy J, Minsky ML, Rochester N, Shannon CE. Bull Math Biophys, 1943A logical calculus of the ideas immanent in nervous activityMcCulloch WS, Pitts W. Mind, 1950Computing machinery and intelligenceTuring AM. Science, 2004In vivo activation of the p53 pathway by small-molecule antagonists of MDM2Vassilev LT, et al. ACS Med Chem Lett, 2013Discovery of RG7112: a small-molecule MDM2 inhibitor in clinical developmentVu B, et al. Lancet Oncol, 2012Effect of the MDM2 antagonist RG7112 on the P53 pathway in patients with MDM2-amplified, well-differentiated or dedifferentiated liposarcomaRay-Coquard I, et al. Nature, 2024Accurate structure prediction of biomolecular interactions with AlphaFold 3Abramson J, et al. Nature, 2023De novo design of protein structure and function with RFdiffusionWatson JL, et al. Nature, 2026Advancing regulatory variant effect prediction with AlphaGenomeAvsec Ž, et al. Nature, 2026Genome modelling and design across all domains of life with Evo 2Brixi G, et al. Science, 2025Simulating 500 million years of evolution with a language modelHayes T, et al. arXiv, 2025TxGemma: efficient and agentic LLMs for therapeuticsGoogle DeepMind and Google Research. arXiv, 2025Towards an AI co-scientistGoogle. Drug Discov Today, 2024How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessonsJayatunga MKP, et al. Nat Rev Drug Discov, 2012Diagnosing the decline in pharmaceutical R&D efficiencyScannell JW, et al. N Engl J Med, 2011Improved survival with vemurafenib in melanoma with BRAF V600E mutationChapman PB, et al. J Clin Oncol, 2015Phase II pilot study of vemurafenib in patients with metastatic BRAF-mutated colorectal cancerKopetz S, et al. Nat Rev Drug Discov, 2017From big data to smart data: FDA’s INFORMED initiativeKhozin S, Kim G, Pazdur R. Br J Cancer, 2020Pre-clinical animal models are poor predictors of human toxicities in phase 1 oncology clinical trialsAtkins JT, et al., Khozin S. U.S. FDA, 2025 · 2026Roadmap to Reducing Animal Testing in Preclinical Safety Studies; draft guidance on new approach methodologiesU.S. Food and Drug Administration. J.P. Morgan, 2026Q2 2026 Biopharma Licensing and Venture ReportJ.P. Morgan. J Telemed Telecare, 2005Centenary of tele-electrocardiography and telephonocardiographyHjelm NM, Julius HW. Clin Transl Sci, 2024From organs to algorithms: redefining cancer classification in the age of artificial intelligenceKhozin S. PLOS Biol, 2023Functional unknomics: systematic screening of conserved genes of unknown functionRocha JJ, et al., Freeman M, Munro S.