For any research lab with published or open data

Validated computational models built from your lab's own data.

I received molecules from you Connect my lab

Enchanted Algo Labs is a computational discovery system. It reads a lab's published or openly licensed data through a connector, builds models validated on held-out data, and sends candidates back for the lab to measure. What comes back from the lab — confirmed, refuted, or inconclusive — is recorded against the candidate that produced it.

  • 1  No fabricated data: every number comes from a real record or a stated computation.
  • 2  Held-out validation before a model is trusted; never a random split reported as a result.
  • 3  Every claim carries what would refute it, and a refutation is recorded as readily as a confirmation.

What is connected today

Every figure below is rendered by the server from the live system when this page is served. None of them is typed into the page.

system digest written 2026-10-30T03:45+00:00

40
labs connected through a real connector
151
datasets those connectors list
34,882
real normalized records read from those labs
0
schema violations in the last read (shown even when zero)
65
candidates prepared for delivery back to labs
2
packages drafted and waiting for a person to send them
1
package a person has sent
0
packages answered by a lab so far, one per package however many replies it sent. Zero means no lab has answered yet, and it is shown as zero.
50
attempts on outside labs' data the system recorded as failed, each with the gate that refused it and the reason. Reopened now: 6 still hold, 44 were resolved at the source since and stay on file as history; 3 more on this project's own systems are not counted here
361,580
candidates the system has adopted from its own reviewed queue
41
research domains those adoptions span

A candidate prepared or a package drafted is an output, not an outcome. The only outcome this system counts is a result a lab measured and sent back; that count is the one above labelled results received.

How it works

Six steps. Two of them are a person's, and the system does not take those two by itself.

1

Register

Your lab's name, a contact email, and your institution. No account, no password.

2

Receive an API key

Shown once, at registration. Only a hash of it is stored, so it cannot be shown again.

3

Submit a dataset in the record contract

Each record is validated against the same contract every hand-built connector meets. Violations come back per record, by index.

4

A person reviews it

A valid submission is kept on file and waits for a human decision. Nothing is wired in automatically, however clean it validates.

5

A connector is built

Written by hand against your real data, and read on the same schedule and under the same checks as every other connected lab.

6

Candidates come back with a reply token

A person emails you a package. Each carries a token and states what result would refute it. You answer at /reply, and your answer is recorded against that package.

WHAT WE ASK FOR

Data you already have, with its provenance

  • Published or openly licensed measurements, one record per measured thing, in the record contract shown on the connect page.
  • Where each record came from: a non-empty source, a verification status, and for records taken from an open repository the license and a record id or DOI.
  • Your lab's stated research goal in a sentence, so candidates are ranked against what you are trying to do rather than against what happens to be easy to compute.
  • Optionally, unpublished measurements under whatever agreement your group needs. That is the one thing no literature search can reach, and it is never required.
  • A reply when a package arrives. A refutation is as useful as a confirmation, and both are recorded.
WHAT YOU GET

Ranked candidates, and the record of what failed

  • A connector that reads your data under the same contract, checks, and schedule as every other connected lab, with its record count shown on this page and its schema violations counted in the total above.
  • Candidates ranked by a model validated on held-out data, each stating the measurement that would refute it.
  • The failures too: what the system tried against your data and could not do, with the gate that refused it and why.
  • Nothing published or acted on without your review. The system computes; it does not synthesize, measure, or send.
  • What it does not give you: a wet-lab result. Every candidate is a computation until your lab measures it, and this site counts it that way.

Connected labs

Each entry is a connector as the system last read it. Institution and department are what the connector states; the goal is the lab's own stated research aim.

  • nci60_nci_cellminercdb
    National Cancer Institute
    25,929 records2 datasets read 2026-10-29
    Stated goal

    Predict which drugs a given cancer cell line will respond to via a structurally independent panel (NCI's own 60-line collection, distinct compound library and cell lines from GDSC/CTRP) -- the same applied purpose, a third real cross-check.

  • doench_broad
    Broad Institute
    3,913 records4 datasets read 2026-10-29
    Stated goal

    Predict CRISPR-Cas9 sgRNA on-target editing efficiency reliably enough to design a working guide for any genomic target without empirical trial-and-error -- the practical purpose Rule Set 2 exists to serve, not merely 'fit this one dataset'.

  • mit_miller_lab
    Massachusetts Institute of Technology · Brain and Cognitive Sciences / Picower Institute for Learning and Memory
    1,046 records9 datasets read 2026-10-29
    Stated goal

    Our research examines the neural basis of cognition and consciousness, including executive functions like working memory, attention, and decision-making. We study how the brain learns abstract rules, categories, and concepts. We study how neural dynamics, like oscillatory brain rhythms, contribute to cognition and consciousness. Our aim is to understand cognition and consciousness to develop treatments for autism, schizophrenia, and attention deficit disorder, and to improve anesthesia safety (ekmillerlab.mit.edu/research, read directly 2026-09-07).

  • Shuibing Chen
    disc_chen_shuibing_weill_medical_coll_of_co
    WEILL MEDICAL COLL OF CORNELL UNIV
    942 records11 datasets read 2026-10-27
    Stated goal

    Dissecting the Role of Endogenous Retroviruses in Type 1 Diabetes Progression

  • gdsc_sanger_mgh
    Wellcome Sanger Institute
    400 records2 datasets read 2026-10-29
    Stated goal

    Predict which drugs a given cancer cell line/tumour genotype will actually respond to, and find the genomic biomarkers that explain why -- prioritizing real compounds for further testing and matching patients to therapies, the applied purpose behind the dose-response measurements, not the IC50/AUC numbers as an end in themselves.

  • gtex_v10_v11
    GTEx Consortium
    400 records2 datasets read 2026-10-29
    Stated goal

    Understand tissue-specific gene expression/regulation well enough to identify disease-relevant targets and assess on-target/off-tumour safety risk BEFORE a therapeutic candidate reaches the clinic -- the applied purpose GTEx feeds elsewhere in this codebase (omega_cure/antigen_specificity.py's CAR-T safety gate), not description for its own sake.

Show the other 34 connected labs
  • Alessandro Sette
    disc_sette_alessandro_la_jolla_institute_for_i
    LA JOLLA INSTITUTE FOR IMMUNOLOGY
    343 records15 datasets read 2026-10-30
    Stated goal

    Respiratory pathogen-specific T cell signatures following vaccination, natural infection, and treatment

  • mit_jazayeri_lab
    Massachusetts Institute of Technology · Brain and Cognitive Sciences / McGovern Institute for Brain Research
    231 records9 datasets read 2026-10-30
    Stated goal

    Understand how dynamic patterns of neural activity in the brain (as well as in artificial neural systems) give rise to algorithms that support mental computations -- reverse-engineer the brain's algorithms by seeing the brain as a system of recurrent neural networks generating complex neural dynamics, and develop a mathematical framework connecting these levels of description, working with humans, animal models and recurrent neural network models (jazlab.org, read directly 2026-09-09).

  • Joseph Thornton
    disc_thornton_joseph_university_of_chicago
    UNIVERSITY OF CHICAGO
    220 records9 datasets read 2026-10-24
    Stated goal

    Genetic and biophysical causes of historical protein evolution

  • ctrp_broad_ctd2
    Broad Institute · CTD2 Center
    200 records1 datasets read 2026-10-29
    Stated goal

    Predict which drugs a given cancer cell line will respond to and find the real gene targets behind that response -- the same applied purpose as GDSC's own end goal, from a structurally independent screen (different compound library, different institution).

  • lincs_l1000_broad
    Broad Institute
    200 records1 datasets read 2026-10-29
    Stated goal

    Infer a candidate drug's real mechanism of action and find repurposing candidates via connectivity mapping -- does a compound's own measured transcriptomic signature reverse a disease's real expression signature -- the applied purpose behind the perturbation signatures, distinct from viability screening (GDSC/CTRP/NCI-60/PRISM already cover that axis; this is a genuinely different, complementary readout of the same real compounds).

  • matbench_materials_project
    Materials Project
    200 records1 datasets read 2026-10-29
    Stated goal

    Discover and rank NEW, not-yet-synthesized stable crystal structures via ML-predicted formation energy -- the benchmark exists to validate models for real materials discovery, not to re-fit the 132,752 already-known Materials Project entries it ships with.

  • Monika Jadi
    disc_jadi_monika_yale_university
    YALE UNIVERSITY
    153 records7 datasets read 2026-10-30
    Stated goal

    Discovering brain state dependent dynamics in large scale perceptual ensembles.

  • aprahamian_dartmouth
    Dartmouth College · Chemistry
    152 records4 datasets read 2026-10-26
    Stated goal

    Design and validate new hydrazone-based molecular switches/motors that convert controlled molecular motion into mechanical work (drug delivery, energy storage); new hydrazone-based fluorophores for bio-imaging, sensing, and OLED applications; and new hydrazone-based sensing systems for chemical/biological detection -- the group's own 3 stated research pillars (aprahamiangroup.com/research.html, verified live 2026-08-11), not just the switching-kinetics subset this connector's existing data happens to cover.

  • prism_repurposing_broad
    Broad Institute
    112 records1 datasets read 2026-10-29
    Stated goal

    Predict which (often already-approved) drugs a given cancer cell line will respond to, the repurposing angle specifically -- surfacing existing drugs for a new cancer indication rather than discovering a wholly new compound.

  • Haribabu Arthanari
    disc_arthanari_haribabu_dana_farber_cancer_inst
    DANA-FARBER CANCER INST
    87 records4 datasets read 2026-10-25
    Stated goal

    Empowering NMR Utilization in Mechanistic Biology: Extending Size Limits and Catalyzing Automation

  • Roland Dunbrack
    disc_dunbrack_roland_research_inst_of_fox_cha
    RESEARCH INST OF FOX CHASE CAN CTR
    46 records6 datasets read 2026-10-23
    Stated goal

    Structural Bioinformatics of Proteins and Protein Complexes and Applications to Cancer Biology

  • Peter Bandettini
    disc_bandettini_peter_national_institute_of_me
    NATIONAL INSTITUTE OF MENTAL HEALTH
    41 records2 datasets read 2026-10-27
    Stated goal

    Functional MRI Core Facility

  • turk_dartmouth_nccc
    Dartmouth College · Norris Cotton Cancer Center
    32 records1 datasets read 2026-10-29
    Stated goal

    Generate durable memory T-cell responses to cancer -- since tumors are altered self-tissue rather than foreign pathogens, lasting immune memory against them does not form the way it does against pathogens. The lab's own stated findings: autoimmune disease is a critical determinant for maintaining T-cell memory to tumor antigens; the most functional tumor-specific memory T cells reside in peripheral tissues, not blood; tissue-resident memory (TRM) T cells are a critical component of long-lived anti-tumor immunity (geiselmed.dartmouth.edu faculty page, read directly 2026-08-22).

  • Jay Shendure
    disc_shendure_jay_university_of_washington
    UNIVERSITY OF WASHINGTON
    29 records4 datasets read 2026-10-29
    Stated goal

    Versatile, exponentially scalable methods for single cell molecular profiling

  • Gaurav Varshney
    disc_varshney_gaurav_oklahoma_medical_researc
    OKLAHOMA MEDICAL RESEARCH FOUNDATION
    26 records2 datasets read 2026-10-29
    Stated goal

    Development of Precision Genome Editing Tools for the Functional Characterization of Genetic Variants

  • rosenberg_nci_surgery_branch
    National Cancer Institute · Surgery Branch
    25 records1 datasets read 2026-10-29
    Stated goal

    Develop adoptive cell transfer immunotherapy -- isolating and expanding a patient's own tumor-reactive T cells (TIL) ex vivo, then reinfusing them with IL-2 support -- to durably regress metastatic cancer; pioneered at the NCI Surgery Branch (Steven A. Rosenberg) and extended by real methodological advances (e.g. NeoExpand neoantigen-specific stimulation) that isolate more, and functionally better (stem-like memory), tumor-reactive TCRs than conventional screening.

  • Chandramouli Chandrasekaran
    disc_chandrasekaran_chandramouli_trustees_of_boston_unive
    Trustees of Boston University
    23 records6 datasets read 2026-10-29
    Stated goal

    CAREER: Multi-Area Neural Dynamics Underlying Perceptual Decision-Making

  • Forrest Collman
    disc_collman_forrest_allen_institute
    ALLEN INSTITUTE
    22 records2 datasets read 2026-10-25
    Stated goal

    Virtual Observatory of the Cortex: Organelles, Cells, Circuits, and Dynamics

  • Kiran Musunuru
    disc_musunuru_kiran_children_s_hosp_of_phila
    CHILDREN'S HOSP OF PHILADELPHIA
    16 records2 datasets read 2026-10-29
    Stated goal

    Postnatal and Prenatal Therapeutic Base Editing for Metabolic Diseases

  • Jan Zimmermann
    disc_zimmermann_jan_university_of_minnesota
    UNIVERSITY OF MINNESOTA
    15 records2 datasets read 2026-10-30
    Stated goal

    Neural basis of behavioral timescale coordination

  • Jesse Engreitz
    disc_engreitz_jesse_stanford_university
    STANFORD UNIVERSITY
    14 records4 datasets read 2026-10-29
    Stated goal

    An integrative genomics toolkit for rewriting regulatory DNA to reprogram gene expression

  • Bonnie Berger
    disc_berger_bonnie_massachusetts_institute
    MASSACHUSETTS INSTITUTE OF TECHNOLOGY
    13 records5 datasets read 2026-10-30
    Stated goal

    Manifold representations and active learning for 21 st century biology

  • Alejandro De La Vega
    disc_de_la_vega_alejandro_university_of_texas_at_a
    UNIVERSITY OF TEXAS AT AUSTIN
    11 records11 datasets read 2026-10-30
    Stated goal

    Large-scale meta-analysis of functional MRI data

  • Daniel Bauer
    disc_bauer_daniel_boston_children_s_hospit
    BOSTON CHILDREN'S HOSPITAL
    7 records2 datasets read 2026-10-29
    Stated goal

    Computational tools for precision genome editing

  • Andreas Baxevanis
    disc_baxevanis_andreas_national_human_genome_re
    NATIONAL HUMAN GENOME RESEARCH INSTITUTE
    7 records1 datasets read 2026-10-30
    Stated goal

    NHGRI/DIR Scientific Computing

  • Raymundo Arroyave
    disc_arroyave_raymundo_texas_a_m_engineering_ex
    Texas A&M Engineering Experiment Station
    5 records5 datasets read 2026-10-30
    Stated goal

    CSSI: Elements: PhaseForge---Building an Open, Reproducible Infrastructure for Machine-Learning-Assisted CALPHAD Modeling

  • Angelica Babei
    disc_babei_angelica_howard_university
    Howard University
    4 records4 datasets read 2026-10-26
    Stated goal

    Collaborative Research: AIMing: Advancing Combinatorics and Number Theory with AI

  • Riyue Bao
    disc_bao_riyue_university_of_pittsburgh
    UNIVERSITY OF PITTSBURGH AT PITTSBURGH
    4 records2 datasets read 2026-10-30
    Stated goal

    Immune Exclusion in Cancer Immunotherapy

  • Michael Rust
    disc_rust_michael_university_of_chicago
    UNIVERSITY OF CHICAGO
    4 records1 datasets read 2026-10-30
    Stated goal

    Connecting Circadian Clock Dynamics to Metabolism and Fitness

  • Wei-Qi Wei
    disc_wei_wei_qi_vanderbilt_university_me
    VANDERBILT UNIVERSITY MEDICAL CENTER
    3 records2 datasets read 2026-10-30
    Stated goal

    Systematically screening and validating drug repurposing candidates for Alzheimer's Disease and Related Dementias

  • Nathan Gianneschi
    disc_gianneschi_nathan_northwestern_university
    NORTHWESTERN UNIVERSITY
    2 records1 datasets read 2026-10-27
    Stated goal

    Synthetic Melanin for Accelerating Wound Repair

  • Sanjay Jain
    disc_jain_sanjay_washington_university
    WASHINGTON UNIVERSITY
    2 records1 datasets read 2026-10-27
    Stated goal

    Single cell multiomic and spatial atlas of acute and chronic kidney injury

  • Charles Rice
    disc_rice_charles_rockefeller_university
    ROCKEFELLER UNIVERSITY
    2 records1 datasets read 2026-10-24
    Stated goal

    Launching HBV with RNA to assess antiviral resistance and explore fundamental aspects of virus-host biology

  • Jade Benjamin-Chung
    disc_benjamin_chung_jade_stanford_university
    STANFORD UNIVERSITY
    1 records1 datasets read 2026-10-29
    Stated goal

    Neighborhood-Level Spillover Effects of Income Transfers on Health

Our own systems, read the same way

6 of this project's own systems are connected through the same contract as the labs above. They are counted separately and are not part of any number in this page's connected-labs figures: every one of those means an outside research lab, and a system reading itself is not one. Their own measurements of themselves (7 so far) are not counted as answers, and the 44 candidate(s) in their internal draft are not counted as prepared for a lab.

  • omega_clay_math
    internal
    Stated goal

    Every claim the system makes about a Clay problem carries a proof a reader can check or sits in the queue as unproved, and every route it has already killed is on record with the evidence that killed it so no generator revives it: the counts here are dead routes and queued claims, and a proof would arrive as a change in the queue, never as a confidence number.

  • omega_cure
    internal
    Stated goal

    Make every candidate the cure agent proposes checkable by a lab and reachable from the solver's documented entry point: the memory's acceptance rate is a critic's opinion until a connected lab measures the proposal, and a disease the solver cannot reach (cure_disease_coverage) or an engine no entry point calls (omega_cure_reachability) is a gap this lab reports by count, never a cure.

  • omega_human_cell
    internal
    Stated goal

    A species-level simulation of a human cell -- the red blood cell first -- that reproduces every observable in a pre-registered validation suite of MEASURED values (metabolite concentrations, fluxes, ion contents, volume, deformability, oxygen binding, ageing) within their measurement error, held out from the fitting; fidelity is the share of that suite reproduced, stated per observable, and never a single word like 100%.

  • omega_tech_stack
    internal
    Stated goal

    A blueprint that can be opened for every corpus problem the state files claim passed: the verifiable count is 13 of 2,022 and the claimed count is what the shard files say, and the gap between them is this lab's reading, closed only by files on the shared tree, never by a state flag.

  • t_agency_self_study
    science (reads this project)
    Stated goal

    Understand how human/LLM collaborative scientific creativity, intuition, and innovation actually manifest as a process -- structurally, with real checkable evidence, not narrative -- well enough to deliberately improve the loop itself, not just document what happened after the fact. Unlike this connector's 5 wet-lab/public-dataset siblings, the relevant 'other labs' here are cognitive-science/HCI/computational-creativity research groups studying human-AI collaboration in general, not a domain-adjacent chemistry/biology dataset.

  • nomos_execution_engine
    trading
    Stated goal

    Make the engine's weekly edge DISTINGUISHABLE from zero on a real denominator: the 95% bootstrap interval of per-expiry return on risk, over SETTLED expiries only, measured against the broker's own cash, excluding zero -- with every booked number reconcilable to the broker to the cent. Not a return target: the operator's 2-5%/week target (2026-10-23) is the Goal Engine's, which grows weekly log NAV and reports the gap, and an edge nobody can distinguish from zero is nothing for it to grow. On 2026-09-11 the mean read 3.58% at n=19, inside the band the operator had named; on 2026-10-23 it read -0.87% at n=45 with empirical Kelly 0, and in both the interval included zero, so what is missing is settled expiries, never parameters.

Contact

Questions about the contract, a data-use agreement, attribution, or a lost API key: write to us. A person reads it.

[email protected]