Structure-binding track: scaffold + ligand-retrieval baseline

Start the structure-based binding branch (PLAN §12), baseline-first.

- src/binding.py: validated RDKit ligand retrieval (morgan_fp, tanimoto,
  retrieve_nearest = the §12.9 engine) + dock() stub documenting the
  blocked ARM-Mac toolchain
- scripts/binding_ligand_baseline.py: 300 drugs vs known binders
- docs/structure_binding_notes.md: status, toolchain blocker, next steps
- pyproject: [structure] extra (rdkit); data/raw/structures/ for PDBs

Step-0 finding: retrieval engine VALIDATED on in-set classes
(decitabine->azacitidine 0.62; vorinostat->scriptaid/belinostat) but the
distinctive binders voxelotor/mitapivat have no analog in our 300-drug
set (Tanimoto ~0.2). Needs (a) bigger library, (b) real docking (§12.3),
which is blocked on the ARM-Mac docking toolchain (§12.6 pitfall 4).
Structures 5E83 (Hb+voxelotor) and 8XFD (PKR+mitapivat) fetched.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

PLAN.md

@@ -426,3 +426,143 @@ This MVP exists in a broader strategic context that was built through ~7 expert
 - **Synthetic trial arms and drug repurposing share data infrastructure.** This is a platform play, not a single product.
 
 The MVP's job is to produce one credible result. Everything else follows from that.
+
+---
+
+## 12. Phase 2 track — Structure-based binding (scoped 2026-06-23)
+
+> **Status: scoped, not committed.** This is a follow-on track proposed *after* the MVP and its
+> follow-up experiments. It does not change the MVP's locked decisions (§2); it responds to what
+> those experiments empirically showed. Read §9–11 and the experiment commits first.
+
+### 12.1 Why pivot modality (the evidence, not a hunch)
+
+The expression-connectivity approach was built, validated, and pushed hard (gene-space
+expansion, cell-composition deconvolution, reference-library tau, supervised learning). The
+empirical verdict:
+
+- Connectivity **recovers hydroxyurea** (top ~6–8%) but **cannot achieve specificity** —
+  unrelated drugs (norethindrone, ciprofloxacin) score as strong reversers. Unfixed by four
+  independent methods.
+- A supervised model on indication labels hit **0.925 CV AUC** — but it was a **degree-bias
+  mirage**: it learned drug popularity, not disease matching (it ranked hydroxyurea *231/300*).
+- The decisive test: with drug-popularity features removed, the model trained on the actual
+  drug↔disease connectivity scored **AUC 0.491 — pure chance**. **The expression-connectivity
+  modality contains essentially no disease-specific therapeutic signal for this task.**
+
+This is a *signal* problem, not a *model* problem — no amount of model sophistication (diffusion,
+GNNs, etc.) extracts signal that isn't in the data. The response is to **change modality** to one
+with a strong, physical, drug-specific signal: **does a molecule bind a sickle-relevant target?**
+A drug that binds HbS is mechanistically specific by construction — the opposite of a coincidental
+expression reverser. Structure is also where the generative-AI frontier (AlphaFold3, which is
+itself a diffusion model) actually has traction, because structure has physical ground truth.
+
+### 12.2 Targets (sickle-specific, druggable, structurally characterised)
+
+Small molecules only (§2). Curated shortlist with public structures and, crucially, **known
+small-molecule binders to serve as positive controls**:
+
+| Target | Mechanism in sickle | Known binder (positive control) |
+|---|---|---|
+| Hemoglobin (HBB/HBA tetramer, HbS) | Anti-polymerisation; R-state stabiliser | **voxelotor** (binds α-Val1) |
+| PKR (PKLR, red-cell pyruvate kinase) | Activator → ↓2,3-BPG → ↑O2 affinity | **mitapivat**, etavopivat |
+| DNMT1 | HbF induction (de-repress γ-globin) | **decitabine**, azacitidine |
+| LSD1 / KDM1A | HbF induction | tranylcypromine analogues |
+| HDAC1/2 | HbF induction | vorinostat, panobinostat |
+| EHMT2 (G9a) | HbF induction | UNC0642 (tool) |
+| PDE9 | ↑cGMP, anti-adhesion | PF-04447943 (sickle trial) |
+
+Hard/excluded for v1: **BCL11A** (transcription factor, no classic pocket — the γ-globin master
+repressor but not small-molecule-tractable yet) and any gene-therapy / biologic mechanism.
+
+### 12.3 Method (baseline → generative co-folding)
+
+1. **Prepare structures.** Pull target structures from the PDB; AF3/Boltz-predict any missing.
+2. **Prepare ligands.** Reuse the existing ~300-drug set (we already have canonical SMILES from
+   ChEMBL); expandable to the full ChEMBL/LINCS catalogue.
+3. **Dock + score**, in increasing sophistication:
+   - **Baseline:** classical docking (AutoDock Vina / smina) — fast, CPU, well-understood.
+   - **Generative co-folding:** an open AlphaFold3-class model — **Boltz-2** (predicts the
+     protein–ligand complex *and* a binding-affinity estimate, MIT-licensed), **Chai-1**, or
+     **DiffDock** (a diffusion model for docking — the legitimate home for the "diffusion"
+     instinct). These predict the bound pose directly and tend to beat classical docking.
+   - Report both; the baseline keeps us honest about whether the ML model adds anything.
+
+### 12.4 Validation (a real recovery test, like §6 Week 4)
+
+Pre-register before scoring: **the known structure-based sickle drugs must rank as top binders to
+their targets** — voxelotor→hemoglobin, mitapivat→PKR, decitabine→DNMT1. Negative controls
+(unrelated drugs) must *not* bind these pockets. This is a cleaner recovery test than the
+expression one, because binding is mechanistically specific — it should not have the
+coincidental-reverser problem that sank the connectivity approach.
+
+### 12.5 The real prize — integrate, don't replace
+
+The long-term value is **both modalities together**: a candidate that *reverses the disease
+signature* (expression) **and** *binds a sickle-relevant target* (structure) is far more credible
+than either alone. Structure supplies the specificity the expression layer lacks; expression
+supplies the systems-level, target-agnostic view structure lacks. The platform thesis (§11) —
+two databases + a matching engine — extends naturally to a third (structures) feeding the same
+confidence-tiered data layer.
+
+### 12.6 Honest pitfalls (do not ignore)
+
+1. **Binding ≠ efficacy.** A molecule can bind and do nothing therapeutic. Structure-based hits
+   are still hypotheses (cf. §9.7).
+2. **Only covers the enzyme/pocket subset.** Sickle's biggest lever (γ-globin reactivation via
+   BCL11A) is largely transcriptional and not small-molecule-tractable — structure-based screening
+   is blind to it. Be explicit about coverage.
+3. **Docking/affinity accuracy is limited.** Pose prediction is decent; absolute affinity is hard.
+   Validate on known binders before trusting novel scores.
+4. **Compute.** AF3-class models are GPU-heavy; the local Mac Studio (§2) may not suffice — this
+   track likely needs a GPU box or cloud, the first MVP dependency to break the "all local" rule.
+5. **Moat.** Structures and tools are public; the proprietary value is the curated target list,
+   the integration with the expression layer, and provenance/tiering — not the docker.
+
+### 12.7 Explicitly NOT in this track
+
+Free energy perturbation / MD-based affinity; covalent docking; **de novo generation of molecules
+as final candidates to synthesise** (design, not repurposing — but see §12.9 for the
+generate-then-retrieve hybrid, which *is* repurposing); BCL11A or any non-pocket target;
+biologics; combination binding.
+
+### 12.8 Open decisions before committing
+
+- **Tooling:** classical-docking baseline first, or straight to Boltz-2/DiffDock? (Recommend:
+  baseline first, for an honest reference — the lesson of the whole expression arc.)
+- **Compute:** secure a GPU environment (the "all local" §2 assumption breaks here).
+- **Scope of v1:** the 7-target shortlist above, or start with just Hb + PKR (the two with the
+  cleanest positive controls) to de-risk the harness before scaling targets.
+
+### 12.9 Door left open — generative-guided retrieval (generate → match existing)
+
+A legitimate way to bring generative models *into the repurposing frame* (vs the design frame
+excluded in §12.7): don't generate molecules to synthesise — generate them as a **search beacon**.
+
+**The idea.** Use a pocket-conditioned generative model (target-conditioned diffusion — e.g.
+TargetDiff, DiffSBDD, Pocket2Mol) to propose idealised binders for a sickle target. Then retrieve
+the **nearest existing drugs** to those proposals by chemical similarity (Tanimoto over Morgan
+fingerprints, or a learned molecular embedding). The retrieved neighbours — real, already-approved
+or clinical molecules — are the repurposing candidates. The generated molecule is never made; it
+only *defines a region of chemical space* worth searching. This is the user-proposed framing and
+it is sound: "generate the ideal, then find what we already have nearby."
+
+**Why it could add value.** It can point at scaffolds / regions a known-binder-seeded or
+brute-force docking sweep would miss, and it makes the target's binding requirements explicit as
+geometry rather than as a single reference ligand.
+
+**Honest caveats (why it's a *door*, not a commitment).**
+1. **Generated molecules are often synthetically unrealistic / invalid** — which is exactly why
+   they must be used only as beacons, never as candidates.
+2. **Similarity ≠ activity.** Activity cliffs mean a near-neighbour of a good binder can be inert.
+   So retrieved neighbours do **not** bypass validation — they must still be docked/scored (§12.3)
+   and clear the binding recovery test (§12.4). The generative step *proposes*; it does not *prove*.
+3. **Marginal-value question.** Directly docking the whole existing library (§12.3) already covers
+   chemical space. Whether generate-then-retrieve beats that — by efficiency or by surfacing
+   non-obvious scaffolds — is an open empirical question and needs a head-to-head before it earns
+   real investment.
+4. **Only as good as the pocket conditioning** of the generator, and the chemistry of the target.
+
+**Status:** explore only *after* the §12.3–12.4 docking harness works and is validated on the
+known binders — same discipline as everywhere else: prove the baseline, then test whether the
+fancier method adds anything.

docs/data_sources.md

@@ -61,9 +61,10 @@ Reproduce with `scripts/week1_explore.py` (download + DE + concordance) then
   38%, as expected). 43 drugs carry target annotations; 46 carry mechanism-of-action.
 - **Tier:** all signature-backed drugs are Tier B (LINCS is a single source → fails Tier A's
   not-single-source rule).
-- **Signature↔landmark overlap:** only 56/477 (12%) of the disease signature genes are LINCS
-  landmarks, so connectivity scoring (Week 3) uses a 30-up/26-down query. The erythroid hallmark
-  genes (CA1, AHSP, SLC4A1, HBG) are NOT landmarks. This is a key limitation for the recovery test.
+- **Gene space (v1.1):** scoring uses the full **12,328-gene** LINCS space, not just the 978
+  landmarks. Signature overlap is 406/477 (85%) vs 56/477 (12%) for landmark-only — the larger
+  space is what recovers hydroxyurea (see recovery_test_report.md). HBG1/HBG2 are absent from
+  LINCS entirely and remain unscoreable.
 - Reproduce: `week2_curate_drugset.py` → `week2_chembl.py` → download Level-5 GCTX →
   `week2_lincs_extract.py` → `week2_assemble.py`.
 

docs/known_limitations.md

@@ -34,20 +34,28 @@ Source: PLAN.md §9.
 7. **Top-ranked novel candidates are not wet-lab validated.** They are computational hypotheses
    to test, not discoveries. Use careful language in any write-up.
 
-8. **Only 12% of the signature is LINCS-scorable (56/477 genes).** The 978 landmark genes (from
-   cancer cell lines) miss the erythroid hallmark genes (CA1, AHSP, SLC4A1, HBG). Connectivity
-   scoring runs on a thin inflammation/metabolic slice — the single biggest driver of the
-   recovery-test failure. v2 fix: signature prediction or a mechanism graph to score the other 88%.
+8. **Gene-space bottleneck (v1 → fixed in v1.1).** v1 scored on only the 978 landmark genes (12%
+   signature overlap) — the main driver of the v1 failure. v1.1 uses the full 12,328-gene space
+   (85% overlap) and recovers hydroxyurea. HBG1/HBG2 remain absent from LINCS entirely.
 
-## Recovery test outcome (Week 4)
+9. **No reference-signature library for tau.** Textbook CMap tau saturated at ±100 (a coherent
+   query always out-connects random gene sets). v1.1 substitutes a per-drug specificity z-score.
+   Proper tau needs a library of real reference signatures — a v2 / curated-data item.
 
-The MVP **failed** all three pre-registered criteria on the primary raw ranking (hydroxyurea
-rank 40/top 13%; L-glutamine rank 100/WTCS=0; 1/5 negative controls in bottom half). The failure
-is fully attributable to signature/assay data limitations above, not the matching algorithm. See
+10. **Negative-control criterion may be invalid for connectivity scoring.** Unrelated drugs
+    (norethindrone, ciprofloxacin) rank as top specific reversers — connectivity measures
+    expression reversal, not therapeutic relatedness.
+
+## Recovery test outcome
+
+Pre-registered test (**v1, confirmatory**): **FAILED** all three criteria (hydroxyurea rank
+40/top 13%; L-glutamine rank 100; 1/5 negative controls bottom-half). Post-hoc (**v1.1,
+exploratory**): hydroxyurea recovers to rank 18 (top 6%, passes), but L-glutamine (rank 213, does
+not reverse) and negative controls (2/5) still fail → overall still FAIL. See
 `recovery_test_report.md`.
 
-| Drug | Issue | Handling |
+| Drug | Issue | v1.1 status |
 |---|---|---|
-| hydroxyurea | HbF mechanism not in scorable gene space | scored (rank 40); recovered only by prior-weighted ranking |
-| L-glutamine | signature present but WTCS ambiguous (=0) | scored (rank 100); no reversal signal |
-| all 300 | had LINCS signatures | 0 marked "not scored" — coverage was not the issue; specificity was |
+| hydroxyurea | needed the full gene space | rank 18 (top 6%) — recovered post-hoc |
+| L-glutamine | metabolite, no reversal signal (positive connectivity) | rank 213 — genuine negative |
+| neg controls | reverse the generic inflammation signature | 2/5 bottom-half — criterion questionable |

docs/recovery_test_report.md

@@ -1,7 +1,7 @@
 # Sickle Cell Repurposing — Recovery Test Report
 
-> **Status: COMPLETE.** Reproduce with `scripts/week1_*` → `week2_*` → `week3_scoring.py` →
-> `week4_recovery_test.py`. ~2 pages, for a sceptical pharma scientist.
+> **Status: COMPLETE (v1 confirmatory + v1.1 exploratory).** Reproduce with `scripts/week1_*` →
+> `week2_*` → `week3_scoring.py` → `week4_recovery_test.py`. ~2 pages, for a sceptical pharma scientist.
 
 ## Pre-registered success criteria
 
@@ -12,118 +12,116 @@ The MVP passes if:
   missing LINCS signature, **AND**
 - At least **4 of 5** negative-control drugs rank in the **bottom half**.
 
-_Pre-registered in the scaffold commit (`b731478`) before any scoring was run. Primary ranking
-= raw connectivity. The 5 negative controls were pre-specified by category rule (one per
-category, alphabetically first available) without inspecting ranks._
+_Pre-registered in the scaffold commit (`b731478`) before any scoring. **Primary (confirmatory)
+analysis = v1**: 978 landmark genes, weighted connectivity score (WTCS). The 5 negative controls
+were pre-specified by category rule without inspecting ranks._
 
 ---
 
 ## Section 1 — Methodology
 
-We built a sickle cell disease signature from **two independent whole-blood microarray studies**
-(GSE35007, Illumina, SS vs AA; GSE16728, Affymetrix, patient vs control), keeping the **671
-genes concordant** (q<0.05, same direction) across both — a cross-platform, cross-population
-Tier-A signature (250 up / 227 down). We built profiles for **300 small molecules** (2
-ground-truth: hydroxyurea, L-glutamine; 32 related-mechanism; 26 negative controls; 240 random),
-each with a consensus **LINCS L1000** signature (mean of Level-5 MODZ z-scores across cell
-lines, 978 landmark genes, both CMap phases). We ranked drugs by **CMap connectivity scoring**
-(weighted-KS, Lamb 2006 / Subramanian 2017): strongly negative = strong reversal of the disease
-signature = candidate. A secondary ranking blends connectivity with a mechanistic prior over
-sickle-relevant target pathways.
+A sickle cell disease signature was built from **two whole-blood microarray studies** (GSE35007
+Illumina SS-vs-AA; GSE16728 Affymetrix patient-vs-control), keeping the **671 genes concordant**
+across both (q<0.05, same direction) → a cross-platform Tier-A signature (250 up / 227 down).
+Profiles were built for **300 small molecules** (2 ground-truth; 32 related-mechanism; 26
+negative controls; 240 random), each with a **LINCS L1000** consensus signature (mean Level-5
+MODZ across cell lines, both CMap phases). Drugs were ranked by **CMap connectivity scoring**
+(Kolmogorov-Smirnov, Lamb 2006 / Subramanian 2017): negative = reversal = candidate.
 
-## Section 2 — Recovery test result — **FAIL** (primary ranking)
+**v1 (pre-registered/confirmatory):** scored on the 978 landmark genes with WTCS.
+**v1.1 (post-hoc/exploratory):** after v1 failed, two changes were made to diagnose why — (a)
+score on the full **12,328-gene** space (landmark overlap 12% → 85%, bringing the erythroid
+markers in); (b) add a **per-drug specificity z-score** (`spec_z`): how many SDs the real
+connectivity is below a null of 1,000 random queries of the same size against that drug. Because
+these changes followed inspection of the v1 result, **v1.1 is exploratory, not a confirmatory
+test of the pre-registered hypothesis.**
 
-| Drug | Rank | Percentile | Pass? |
-|---|---|---|---|
-| Hydroxyurea | 40 / 300 | top 13.3% | ❌ (needs top 30) |
-| L-glutamine | 100 / 300 | top 33.3% | ❌ (WTCS=0, ambiguous; has a signature so not "missing") |
+## Section 2 — Recovery test result
 
-Negative controls (pre-specified; expected: bottom half):
+| Criterion | v1 (confirmatory) | v1.1 (exploratory) |
+|---|---|---|
+| Hydroxyurea top-10% (≤30) | rank **40** (13.3%) ❌ | rank **18** (6.0%) ✅ |
+| L-glutamine top-25% (≤75) | rank 100, WTCS=0 ❌ | rank 213, spec_z=+0.98 ❌ |
+| ≥4/5 neg controls bottom-half | 1/5 ❌ | 2/5 ❌ |
+| **Overall** | **FAIL** | **FAIL** (but hydroxyurea recovered) |
 
-| Control | Category | Rank | Bottom half? |
-|---|---|---|---|
-| clotrimazole | antifungal | 89 | ❌ |
-| astemizole | antihistamine | 291 | ✅ |
-| azithromycin | antibiotic | 82 | ❌ |
-| ethinyl-estradiol | hormone | 98 | ❌ |
-| caffeine | misc | 84 | ❌ |
+v1.1 negative controls: clotrimazole 258 ✅, astemizole 211 ✅, azithromycin 142 ❌,
+ethinyl-estradiol 114 ❌, caffeine 77 ❌.
 
-**Only 1/5 negative controls in the bottom half (need ≥4).**
+**Honest reading.** The **pre-registered test FAILED (v1).** The post-hoc v1.1 changes
+**recover hydroxyurea** (rank 40 → 18, passing top-10%) — strong evidence that the v1 failure was
+driven by the 978-landmark bottleneck, not the algorithm. But two failures survive into v1.1, and
+both are now precisely diagnosed:
 
-**Overall: FAIL on all three pre-registered criteria.** This is reported as-is, without
-adjustment. For context only (not the pre-registered criterion): the secondary
-mechanistic-prior ranking places hydroxyurea at **rank 7 (top 2.3%)** — but that ranking uses
-prior knowledge of the drug's target, so it cannot be claimed as a blind recovery.
+1. **L-glutamine does not reverse the signature** (positive connectivity, spec_z=+0.98). This is
+   intrinsic to its LINCS data — a metabolite with no reversal signal — not a coverage gap. More
+   genes cannot fix it.
+2. **The negative-control criterion is arguably invalid for connectivity scoring.** Two
+   "negative controls" (norethindrone, ciprofloxacin) rank in the top 3 by spec_z. Connectivity
+   measures *expression reversal*, not *therapeutic relatedness* — an antibiotic or contraceptive
+   can still down-regulate the inflammation genes that dominate the scorable signature. The test
+   design conflates the two.
 
-**Why it failed — the honest diagnosis.** The disease signature is dominated by erythroid /
-reticulocyte biology (CA1, AHSP, SLC4A1) and the HbF axis that hydroxyurea actually acts on
-(HBG1/HBG2) was lost (flat in GSE35007; removed by GSE16728's globin-depleted prep). Worse,
-only **56 of 477 signature genes (12%) are LINCS landmark genes** — and none of the erythroid
-hallmark genes are. So connectivity scoring ran on a thin, inflammation-heavy 30-up/26-down
-query. The engine is effectively scoring reversal of sickle's *inflammation* axis, not its
-*erythroid* axis — which is why hydroxyurea (an HbF inducer / antiproliferative) is not
-recovered, and why unrelated drugs get spurious mild-reversal scores (poor specificity).
+A note on the calibration: textbook CMap **tau** (percentile vs a reference population) was
+implemented but **saturated at ±100** here, because a coherent real query always out-connects
+random gene sets — proper tau needs a library of *real* reference signatures, which this MVP
+lacks. The continuous `spec_z` is the workable substitute.
 
-## Section 3 — Top 10 candidates (raw connectivity)
+## Section 3 — Top 10 candidates (v1.1 spec_z)
 
-| Rank | Drug | Score | Known target / mechanism | Plausibility |
+| Rank | Drug | spec_z | Inclusion | Read |
 |---|---|---|---|---|
-| 1 | laropiprant | −0.417 | Prostaglandin D2 receptor antagonist | Anti-inflammatory — coherent with inflammation-axis reversal |
-| 2 | BRD-K62768824 | −0.396 | (tool compound, no annotation) | Likely broad-effect false positive |
-| 3 | BRD-K71353154 | −0.393 | (tool compound) | Likely false positive |
-| 4 | lisinopril | −0.358 | ACE inhibitor | **Non-obvious; see §4** |
-| 5 | BRD-K53443165 | −0.358 | (tool compound) | Likely false positive |
-| 6 | talnetant | −0.347 | Neurokinin-3 (NK3) receptor antagonist | No obvious sickle rationale |
-| 7 | BRD-K46936109 | −0.342 | (tool compound) | Likely false positive |
-| 8 | lawsone | −0.340 | Naphthoquinone (henna pigment) | No obvious rationale; possible redox effect |
-| 9 | BRD-K85763971 | −0.338 | (tool compound) | Likely false positive |
-| 10 | BRD-K36516410 | −0.323 | (tool compound) | Likely false positive |
+| 1 | reserpic-acid | −3.80 | random | reserpine metabolite; non-obvious |
+| 2 | norethindrone | −3.78 | **negative control** | false positive (see §2) |
+| 3 | ciprofloxacin | −3.61 | **negative control** | false positive |
+| 4 | resveratrol | −3.46 | related-mechanism | antioxidant studied in SCD — coherent |
+| 5 | BRD-K57490754 | −3.37 | random | tool compound |
+| 6 | anastrozole | −3.27 | random | aromatase inhibitor |
+| 7–10 | BRD-* / palmitoylethanolamide | ~−3.1 | random | mostly tool compounds |
 
-As anticipated (PLAN §9.4), the raw top-10 is dominated by unannotated broad-effect tool
-compounds — these are **not** credible candidates and are not over-interpreted.
+That two negative controls outrank hydroxyurea is the single most informative result here — see §4.
 
-## Section 4 — One non-obvious candidate worth investigating
+## Section 4 — One non-obvious result worth investigating
 
-**Lisinopril (ACE inhibitor), rank 4.** This is the most interesting non-obvious hit: ACE
-inhibitors are already used clinically in sickle cell disease for **renal protection**
-(reducing albuminuria / progression of sickle nephropathy), via mechanisms independent of the
-HbF pathway. Surfacing an agent with a genuine, mechanistically distinct sickle-cell rationale —
-from an inflammation/vascular-flavoured signature — is a small but real signal that the matching
-approach can point at non-obvious biology. **This is a computational hypothesis, not a
-discovery**, and the connectivity rationale here (inflammation-axis reversal) is not the same as
-lisinopril's known renal mechanism, so the match should be treated as suggestive only.
+The most useful finding is **not** a candidate drug but the **negative-control failure**:
+unrelated drugs (norethindrone, ciprofloxacin) score as strong specific reversers. This is a
+real, generalizable lesson — for a signature whose *scorable* portion is generic
+inflammation/metabolic genes, connectivity rewards any broad transcriptional perturbation that
+touches those genes. The honest implication: **this signature is not specific enough to
+discriminate true repurposing candidates from incidental expression reversers.** Of the
+plausibly-real hits, **resveratrol (rank 4)** — an antioxidant with prior sickle cell literature
+— is the most defensible, but it is a hypothesis, not a discovery.
 
 ## Section 5 — Honest limitations
 
-1. **Cell-composition confound** — the whole-blood signature is dominated by reticulocyte/
-   erythroid markers (composition, not pure disease-state regulation). v2 needs deconvolution.
-2. **Missing HbF axis** — HBG1/HBG2 absent (globin depletion + flat in GSE35007), so the
-   signature cannot encode the pathway hydroxyurea acts on.
-3. **12% signature↔landmark overlap** — only 56/477 genes are LINCS landmarks; the erythroid
-   hallmark genes are not scorable. The query collapses to a generic inflammation/metabolic slice.
-4. **LINCS cell-line bias** — landmark signatures come from cancer cell lines (PLAN §9.2); poorly
-   suited to a blood disease.
-5. **Poor negative-control specificity** — unrelated drugs received mild reversal scores; the
-   thin query yields a noisy connectivity distribution.
-6. **No mechanistic validation** — these are connectivity hypotheses, not validated predictions.
+1. **Pre-registered test failed; the pass is post-hoc.** v1.1's hydroxyurea recovery is
+   exploratory and must be re-validated on a held-out disease before any claim is made.
+2. **Missing HbF axis** — HBG1/HBG2 are absent from LINCS entirely (not just landmarks), so the
+   pathway hydroxyurea acts on can never be scored by this method.
+3. **Signature specificity** — scorable genes are inflammation/metabolic; negative controls
+   reverse them too. Connectivity ≠ therapeutic relatedness.
+4. **Cell-composition confound** — the whole-blood signature is reticulocyte-dominated.
+5. **LINCS cancer-cell-line bias**, and **no reference-signature library** for proper tau.
+6. **No mechanistic validation** — all hits are computational hypotheses.
 
 ## Section 6 — What v2 would fix
 
-- **Cell-type deconvolution** of the disease signature to separate disease-state regulation from
-  composition, recovering specificity.
-- **A non-globin-depleted, RNA-seq whole-blood study** to retain the HbF axis.
-- **Signature prediction** (DeepCE-style) or a mechanism/knowledge graph to score the ~88% of
-  the signature that has no LINCS landmark — the single biggest lever on this result.
-- **A second disease** to test generalization (sickle results alone do not prove the platform —
-  PLAN §9.5).
+- **A reference-signature library** to make tau (proper specificity calibration) work — the
+  single biggest fix to the negative-control problem, and a direct use of the curated-data moat.
+- **Cell-type deconvolution** + a non-globin-depleted RNA-seq study to recover a more specific,
+  HbF-containing signature.
+- **Signature prediction / mechanism graph** to score genes with no LINCS measurement.
+- **A second disease** to test generalization and to honestly re-validate the v1.1 method
+  (PLAN §9.5).
 
 ---
 
 ### Bottom line
 
-The pipeline is reproducible end-to-end and the method is sound, but on this signature it **does
-not recover the known sickle cell drugs**. The failure is fully explained by signature/assay
-data limitations (erythroid biology lost; 12% landmark overlap), not by a flaw in the matching
-algorithm. The most valuable output of this MVP is therefore a precise, honest map of *what data
-quality the method needs to work* — which is exactly the de-risking the proof-of-concept was
-meant to deliver.
+The pre-registered recovery test **failed**. Post-hoc diagnosis shows the dominant cause was a
+fixable gene-space bottleneck — correcting it **recovers hydroxyurea** — but also surfaces a
+deeper, genuine limitation: this whole-blood signature is **not specific enough** for
+connectivity scoring to separate real candidates from incidental reversers (negative controls
+rank at the top). The MVP's real deliverable is a precise, honest map of *what it takes to make
+this method work*: a more specific (deconvolved, HbF-containing) signature and a reference library
+for calibration — exactly the curated-data investments the platform thesis is built on.

docs/structure_binding_notes.md

@@ -0,0 +1,34 @@
+# Structure-based binding track — working notes
+
+Branch `structure-based-binding`. Implements PLAN §12. Baseline-first, start with the two cleanest
+targets (Hemoglobin + PKR), de-risk the harness before scaling.
+
+## Status (2026-06-23)
+
+**Toolchain check (PLAN §12.6 pitfall 4, confirmed real):**
+- ✅ RDKit installs on ARM Mac — ligand side ready.
+- ❌ AutoDock Vina does NOT pip-install on ARM Mac; no docking binary available. Docking (§12.3)
+  is **blocked on toolchain** — must resolve via conda/micromamba (`vina`/`smina`), a GPU AF3-class
+  model (Boltz-2/Chai-1/DiffDock), or an x86 Vina binary under Rosetta.
+
+**Structures obtained:** `5E83` (hemoglobin + voxelotor), `8XFD` (PKR + mitapivat) in
+`data/raw/structures/`.
+
+**Step 0 — ligand-based retrieval baseline (`scripts/binding_ligand_baseline.py`):**
+RDKit Tanimoto of our 300 drugs vs known sickle binders.
+- Engine VALIDATED on in-set classes: `decitabine`→azacitidine (0.62); `vorinostat`→scriptaid
+  (0.42), belinostat (0.28). Correctly clusters DNMT1 / HDAC HbF-inducers.
+- But voxelotor / mitapivat have **no analog** in our set (max Tanimoto ~0.20–0.26). A 300-drug
+  library is too sparse to contain look-alikes of distinctive scaffolds.
+
+**Takeaways:**
+1. Ligand retrieval works but needs a **bigger drug library** to be useful for distinctive targets.
+2. The targets without in-set analogs (Hb, PKR) need **actual docking** (§12.3) — which scores
+   binding directly, no look-alike required. That is the gating next step, and it needs the
+   toolchain solved.
+
+## Next steps
+- [ ] Resolve the docking toolchain (recommend: micromamba + smina/vina, CPU, no GPU needed for baseline).
+- [ ] Dock the known binders (voxelotor→5E83, mitapivat→8XFD) as positive controls (§12.4 recovery test).
+- [ ] Expand the ligand library (full ChEMBL/LINCS) for retrieval to have reach.
+- [ ] Only then: AF3-class co-folding (Boltz-2/DiffDock) vs the docking baseline; and §12.9 generative beacon.

pyproject.toml

@@ -33,6 +33,12 @@ dev = [
     "pytest>=8.0",
     "ruff>=0.5",
 ]
+# Structure-based binding track (PLAN §12). Docking tool (vina/smina) is NOT pip-installable on
+# ARM Mac — install via conda/micromamba or use a GPU AF3-class model; see docs/structure_binding_notes.md.
+structure = [
+    "rdkit>=2024.3",
+    "requests>=2.31",
+]
 
 [tool.setuptools.packages.find]
 where = ["."]

scripts/binding_ligand_baseline.py

@@ -0,0 +1,66 @@
+"""Structure-based track, step 0: ligand-based retrieval baseline (PLAN §12.9 engine).
+
+Docking (§12.3) needs a toolchain that doesn't pip-install on ARM Mac (AutoDock Vina) — that's the
+next dependency to solve. Meanwhile this runs now with pure RDKit: do any of our 300 drugs sit near
+the KNOWN sickle binders (voxelotor, mitapivat, decitabine) in chemical space? This is the
+retrieval engine §12.9 would point a generative beacon at, and a sanity check on the ligand data.
+
+NOT docking and NOT a binding claim — chemical similarity only. Similarity != activity (§12.9).
+"""
+
+from __future__ import annotations
+
+import pandas as pd
+import requests
+from rdkit import Chem, DataStructs, RDLogger
+from rdkit.Chem import rdFingerprintGenerator
+
+RDLogger.DisableLog("rdApp.*")
+MORGAN = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
+
+# Known sickle binders = positive-control beacons (target in parens).
+BINDERS = ["voxelotor", "mitapivat", "decitabine", "vorinostat"]
+
+
+def pubchem_smiles(name: str) -> str | None:
+    for prop in ("SMILES", "ConnectivitySMILES", "IsomericSMILES", "CanonicalSMILES"):
+        try:
+            u = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/{name}/property/{prop}/JSON"
+            d = requests.get(u, timeout=30).json()["PropertyTable"]["Properties"][0]
+            if prop in d:
+                return d[prop]
+        except Exception:
+            continue
+    return None
+
+
+def fp(smi: str):
+    if not isinstance(smi, str) or smi in ("-666", ""):
+        return None
+    m = Chem.MolFromSmiles(smi)
+    return MORGAN.GetFingerprint(m) if m else None
+
+
+def main() -> None:
+    binder_smi = {b: pubchem_smiles(b) for b in BINDERS}
+    print("known-binder SMILES:", {k: (v[:34] + "..." if v else "MISSING") for k, v in binder_smi.items()})
+
+    drugs = pd.read_csv("data/processed/drug_set_v1.csv")[["pert_iname", "canonical_smiles", "inclusion_reason"]]
+    reason = dict(zip(drugs.pert_iname, drugs.inclusion_reason))
+    drug_fp = {r.pert_iname: fp(r.canonical_smiles) for r in drugs.itertuples()}
+    drug_fp = {k: v for k, v in drug_fp.items() if v is not None}
+    print(f"fingerprinted {len(drug_fp)}/{len(drugs)} drugs\n")
+
+    for b, smi in binder_smi.items():
+        bfp = fp(smi)
+        if bfp is None:
+            print(f"{b}: no SMILES\n"); continue
+        sims = sorted(((DataStructs.TanimotoSimilarity(bfp, v), k) for k, v in drug_fp.items()), reverse=True)
+        print(f"nearest drugs to {b}:")
+        for s, k in sims[:5]:
+            print(f"    {s:.3f}  {k:22s} [{reason.get(k,'?')}]")
+        print()
+
+
+if __name__ == "__main__":
+    main()

scripts/exp_deconv_signature.py

@@ -0,0 +1,137 @@
+"""Experiment: composition-adjusted sickle signature, to fix specificity (option 1).
+
+The v1 signature is confounded by cell composition (SS patients have very different WBC/RBC
+than AA controls). GSE35007 *measured* those counts per sample, so we adjust the differential
+expression for them directly (a measured-covariate alternative to estimated deconvolution):
+
+    expression ~ disease + WBC + RBC + MCV + age + sex     (per gene, vectorized OLS)
+
+We compare the composition-ADJUSTED signature against an UNADJUSTED single-study signature
+(same samples, model without the covariates), score both with the v1.1 engine (full gene space
++ spec_z), and report the recovery test for each. Writes nothing to committed artifacts.
+"""
+
+from __future__ import annotations
+
+import warnings
+from pathlib import Path
+
+import GEOparse
+import numpy as np
+import pandas as pd
+from scipy.stats import false_discovery_control
+from scipy.stats import t as tdist
+
+import sys
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+from src.disease import collapse_probes_to_symbols  # noqa: E402
+from src.scoring import tau_calibrate  # noqa: E402
+
+warnings.filterwarnings("ignore")
+PROCESSED = Path("data/processed")
+NEG5 = ["clotrimazole", "astemizole", "azithromycin", "ethinyl-estradiol", "caffeine"]
+SYMBOL_COLS = ["Symbol", "ILMN_Gene", "Gene Symbol", "GeneSymbol"]
+
+
+def cval(gsm, key):
+    for c in gsm.metadata.get("characteristics_ch1", []):
+        if c.lower().startswith(key.lower()):
+            return c.split(":", 1)[1].strip()
+    return None
+
+
+def load_data():
+    gse = GEOparse.get_GEO(geo="GSE35007", destdir="data/raw/geo", silent=True)
+    meta = []
+    for gid, g in gse.gsms.items():
+        meta.append({"gsm": gid, "hb": cval(g, "hb phenotype"), "wbc": cval(g, "white blood cells"),
+                     "rbc": cval(g, "red blood cells"), "mcv": cval(g, "mean corpuscular volume"),
+                     "age": cval(g, "age"), "sex": cval(g, "Sex")})
+    meta = pd.DataFrame(meta).set_index("gsm")
+    for c in ["wbc", "rbc", "mcv", "age"]:
+        meta[c] = pd.to_numeric(meta[c], errors="coerce")
+    meta["disease"] = meta["hb"].map({"SS": 1.0, "AA": 0.0})
+    meta["sex_m"] = (meta["sex"] == "M").astype(float)
+    keep = meta[meta["hb"].isin(["SS", "AA"])].dropna(subset=["wbc", "rbc", "mcv", "age", "disease"])
+
+    expr = pd.DataFrame({gid: gse.gsms[gid].table.set_index("ID_REF")["VALUE"] for gid in keep.index})
+    expr = expr.apply(pd.to_numeric, errors="coerce").dropna(how="any")
+    if float(np.nanmax(expr.to_numpy())) > 50:
+        expr = np.log2(expr.clip(lower=0) + 1.0)
+
+    gpl = list(gse.gpls.values())[0]
+    col = next((c for c in SYMBOL_COLS if c in gpl.table.columns), None)
+    sym = gpl.table.set_index("ID")[col].astype(str).str.strip().replace({"": np.nan, "nan": np.nan})
+    return expr, keep, sym.dropna()
+
+
+def ols_de(expr, design, disease_idx):
+    """Vectorized per-gene OLS; return DE table (log_fc=disease coef, pvalue, qvalue)."""
+    X = design.to_numpy(dtype=float)
+    Y = expr.T.to_numpy(dtype=float)  # samples x genes
+    n, p = X.shape
+    XtX_inv = np.linalg.pinv(X.T @ X)
+    B = XtX_inv @ X.T @ Y
+    resid = Y - X @ B
+    dof = n - p
+    sigma2 = (resid ** 2).sum(0) / dof
+    se = np.sqrt(sigma2 * XtX_inv[disease_idx, disease_idx])
+    t = B[disease_idx] / se
+    pval = 2 * tdist.sf(np.abs(t), dof)
+    out = pd.DataFrame({"log_fc": B[disease_idx], "pvalue": pval}, index=expr.index).dropna()
+    out["qvalue"] = false_discovery_control(out["pvalue"].to_numpy(), method="bh")
+    return out
+
+
+def make_signature(de, sym, expr, top_n=250):
+    de_sym = collapse_probes_to_symbols(de, sym, expression_for_ranking=expr)
+    sig = de_sym[de_sym["qvalue"] < 0.05]
+    up = sig[sig["log_fc"] > 0].nsmallest(top_n, "qvalue").index.tolist()
+    down = sig[sig["log_fc"] < 0].nsmallest(top_n, "qvalue").index.tolist()
+    return up, down
+
+
+def evaluate(label, up, down, lincs):
+    ranked = tau_calibrate(up, down, lincs, n_null=1000)
+    n = len(ranked)
+    top10, top25, half = int(n * .10), int(n * .25), n // 2
+    profiles = pd.read_parquet(PROCESSED / "drug_profiles_v1.parquet").set_index("name")
+    ranked = ranked.join(profiles[["inclusion_reason"]])
+    hu, glut = int(ranked.loc["hydroxyurea", "rank"]), int(ranked.loc["glutamine", "rank"])
+    negs = {d: int(ranked.loc[d, "rank"]) for d in NEG5 if d in ranked.index}
+    n_bottom = sum(r > half for r in negs.values())
+    n_ov = len((set(up) | set(down)) & set(lincs.columns))
+    print(f"\n### {label}: {len(up)} up / {len(down)} down (query overlap {n_ov})")
+    print(f"  hydroxyurea rank {hu}/{n} (top {100*hu/n:.1f}%)  top-10%? {hu <= top10}")
+    print(f"  L-glutamine rank {glut}/{n} (top {100*glut/n:.1f}%)  top-25%? {glut <= top25}")
+    print(f"  neg controls bottom-half: {n_bottom}/5  {negs}")
+    print("  top 8: " + ", ".join(
+        f"{name}[{r['inclusion_reason'][:3]}]" for name, r in ranked.nsmallest(8, "spec_z").iterrows()))
+    return ranked
+
+
+def main():
+    expr, meta, sym = load_data()
+    print(f"loaded {expr.shape[1]} samples x {expr.shape[0]} probes; "
+          f"{int(meta.disease.sum())} SS / {int((meta.disease==0).sum())} AA")
+    lincs = pd.read_parquet(PROCESSED / "lincs_signatures_v1.parquet")
+
+    base = pd.DataFrame({"intercept": 1.0, "disease": meta["disease"]}, index=meta.index)
+    adj = base.assign(wbc=meta["wbc"], rbc=meta["rbc"], mcv=meta["mcv"], age=meta["age"], sex_m=meta["sex_m"])
+
+    de_unadj = ols_de(expr, base, disease_idx=1)
+    de_adj = ols_de(expr, adj, disease_idx=1)
+
+    up_u, dn_u = make_signature(de_unadj, sym, expr)
+    up_a, dn_a = make_signature(de_adj, sym, expr)
+
+    # how much does adjustment change the gene set?
+    overlap = len((set(up_u) | set(dn_u)) & (set(up_a) | set(dn_a)))
+    print(f"\nsignature gene overlap unadjusted vs adjusted: {overlap}/{len(set(up_u)|set(dn_u))}")
+
+    evaluate("UNADJUSTED (GSE35007 only)", up_u, dn_u, lincs)
+    evaluate("COMPOSITION-ADJUSTED", up_a, dn_a, lincs)
+
+
+if __name__ == "__main__":
+    main()

scripts/exp_genespace.py

@@ -0,0 +1,102 @@
+"""Experiment (v1.1): re-score on a larger LINCS gene space and re-run the recovery test.
+
+v1 used only the 978 landmark genes (12% signature overlap). This re-slices the SAME GCTX files
+to the BING space (~10,174) and the full 12,328-gene space, re-aggregates per-drug consensus
+signatures, re-scores connectivity, and evaluates the pre-registered recovery criteria — so we
+can see whether hydroxyurea recovers. Writes nothing to the committed v1 artifacts.
+"""
+
+from __future__ import annotations
+
+import gzip
+import io
+import json
+from pathlib import Path
+
+import pandas as pd
+
+import sys
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+from src.scoring import rank_drugs  # noqa: E402
+
+LINCS = Path("data/raw/lincs")
+PROCESSED = Path("data/processed")
+GCTX = {1: LINCS / "phase1_level5.gctx", 2: LINCS / "phase2_level5.gctx"}
+SIG_INFO = {1: "GSE92742_sig_info.txt.gz", 2: "GSE70138_sig_info.txt.gz"}
+NEG5 = ["clotrimazole", "astemizole", "azithromycin", "ethinyl-estradiol", "caffeine"]
+
+
+def read_gz(name):
+    return pd.read_csv(io.BytesIO(gzip.decompress((LINCS / name).read_bytes())), sep="\t", low_memory=False)
+
+
+def gene_ids_for_space(space: str):
+    g = pd.read_csv(LINCS / "GSE92742_gene_info.txt.gz", sep="\t")
+    if space == "bing":
+        g = g[g.pr_is_bing == 1]
+    # 'all' -> keep everything
+    ids = [str(x) for x in g.pr_gene_id]
+    id_to_symbol = {str(r.pr_gene_id): r.pr_gene_symbol for r in g.itertuples()}
+    return ids, id_to_symbol
+
+
+def extract(space, drug_names, gene_ids, id_to_symbol):
+    from cmapPy.pandasGEXpress.parse import parse
+    frames = []
+    for ph in (1, 2):
+        sig = read_gz(SIG_INFO[ph])
+        sig = sig[(sig.pert_type == "trt_cp") & (sig.pert_iname.isin(drug_names))]
+        if sig.empty:
+            continue
+        gct = parse(str(GCTX[ph]), rid=gene_ids, cid=sig.sig_id.tolist())
+        data = gct.data_df
+        s2d = dict(zip(sig.sig_id, sig.pert_iname))
+        frames.append(data.T.groupby(data.columns.map(s2d)).mean())
+        print(f"    [{space}] phase {ph}: {sig.pert_iname.nunique()} drugs sliced", flush=True)
+    combined = pd.concat(frames).groupby(level=0).mean()
+    combined.columns = [id_to_symbol.get(c, c) for c in combined.columns]
+    combined = combined.loc[:, ~combined.columns.duplicated()]  # drop dup symbols
+    return combined
+
+
+def evaluate(space, sig_matrix, up, down):
+    landmark_overlap = None
+    ranked = rank_drugs(up, down, sig_matrix)
+    n = len(ranked)
+    top10, top25, half = int(n * 0.10), int(n * 0.25), n // 2
+    profiles = pd.read_parquet(PROCESSED / "drug_profiles_v1.parquet").set_index("name")
+    ranked = ranked.join(profiles[["inclusion_reason"]])
+
+    hu, glut = int(ranked.loc["hydroxyurea", "rank"]), int(ranked.loc["glutamine", "rank"])
+    glut_s = ranked.loc["glutamine", "connectivity_score"]
+    n_overlap = len((set(up) | set(down)) & set(sig_matrix.columns))
+    negs = {d: int(ranked.loc[d, "rank"]) for d in NEG5 if d in ranked.index}
+    n_bottom = sum(r > half for r in negs.values())
+
+    print(f"\n=== gene space: {space.upper()} ({sig_matrix.shape[1]} genes; query overlap {n_overlap}) ===")
+    print(f"  hydroxyurea: rank {hu}/{n} (top {100*hu/n:.1f}%)  top-10%? {hu <= top10}")
+    print(f"  L-glutamine: rank {glut}/{n} (top {100*glut/n:.1f}%), WTCS={glut_s:.3f}  top-25%? {glut <= top25}")
+    print(f"  neg controls in bottom half: {n_bottom}/5  {negs}")
+    crit = (hu <= top10) and (glut <= top25) and (n_bottom >= 4)
+    print(f"  OVERALL: {'PASS' if crit else 'FAIL'}")
+    print("  top 8:")
+    for name, r in ranked.nsmallest(8, "connectivity_score").iterrows():
+        print(f"    {int(r['rank']):2d} {name:18s} {r['connectivity_score']:+.3f} [{r['inclusion_reason']}]")
+    return ranked
+
+
+def main():
+    sig = json.loads((PROCESSED / "sickle_cell_signature_v1.json").read_text())
+    up = [g["gene"] for g in sig["up_regulated"]]
+    down = [g["gene"] for g in sig["down_regulated"]]
+    drug_names = set(pd.read_csv(PROCESSED / "drug_set_v1.csv").pert_iname)
+
+    for space in ("bing", "all"):
+        print(f"\n>>> extracting {space} ...", flush=True)
+        ids, id2sym = gene_ids_for_space(space)
+        mat = extract(space, drug_names, ids, id2sym)
+        evaluate(space, mat, up, down)
+
+
+if __name__ == "__main__":
+    main()

scripts/phaseA_reference_tau.py

@@ -0,0 +1,118 @@
+"""Phase A: calibrate sickle connectivity against a REAL disease-signature reference population.
+
+The v1.1 tau saturated because it used random gene-set nulls. Proper specificity calibration
+asks: does a drug reverse SICKLE more than it reverses diseases in general? We download a library
+of real disease signatures (Enrichr "Disease Signatures from GEO", up+down), compute each drug's
+connectivity to every reference disease, and express its sickle connectivity as a z-score within
+that per-drug reference distribution. Broad-effect drugs (reverse everything) -> z~0 -> down-ranked.
+
+Re-runs the recovery test and compares to v1.1 (random-null). Writes nothing committed.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import requests
+
+import sys
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+from src.scoring import _ks_connectivity  # noqa: E402
+
+PROCESSED = Path("data/processed")
+RAW = Path("data/raw/disease_sigs")
+ENRICHR = "https://maayanlab.cloud/Enrichr/geneSetLibrary?mode=text&libraryName={}"
+LIBS = {"up": "Disease_Signatures_from_GEO_up_2014", "down": "Disease_Signatures_from_GEO_down_2014"}
+NEG5 = ["clotrimazole", "astemizole", "azithromycin", "ethinyl-estradiol", "caffeine"]
+
+
+def fetch_gmt(name: str) -> dict[str, list[str]]:
+    RAW.mkdir(parents=True, exist_ok=True)
+    path = RAW / f"{name}.gmt"
+    if not path.exists():
+        r = requests.get(ENRICHR.format(name), timeout=120)
+        r.raise_for_status()
+        path.write_text(r.text)
+    out = {}
+    for line in path.read_text().splitlines():
+        parts = line.split("\t")
+        if len(parts) < 3:
+            continue
+        term = parts[0]
+        genes = [g.split(",")[0].strip().upper() for g in parts[2:] if g.strip()]
+        out[term] = genes
+    print(f"  {name}: {path.stat().st_size/1e6:.1f} MB, {len(out)} terms")
+    return out
+
+
+def build_reference() -> list[dict]:
+    up = fetch_gmt(LIBS["up"])
+    down = fetch_gmt(LIBS["down"])
+    shared = set(up) & set(down)
+    refs = []
+    for term in shared:
+        if "sickle" in term.lower():
+            continue  # exclude the target disease from its own reference population
+        refs.append({"name": term, "up": up[term], "down": down[term]})
+    print(f"reference disease signatures (paired up+down, sickle excluded): {len(refs)}")
+    return refs
+
+
+def cols_for(genes, gene_to_col):
+    return np.array([gene_to_col[g] for g in set(genes) if g in gene_to_col], dtype=int)
+
+
+def main():
+    import json
+    sig = json.loads((PROCESSED / "sickle_cell_signature_v1.json").read_text())
+    sk_up = [g["gene"] for g in sig["up_regulated"]]
+    sk_down = [g["gene"] for g in sig["down_regulated"]]
+
+    lincs = pd.read_parquet(PROCESSED / "lincs_signatures_v1.parquet")
+    genes = list(lincs.columns)
+    gene_to_col = {g: i for i, g in enumerate(genes)}
+    n = len(genes)
+    R = lincs.rank(axis=1, ascending=False).to_numpy()
+
+    refs = build_reference()
+    # connectivity of every drug to every reference disease -> (n_drugs, n_refs)
+    C = np.empty((R.shape[0], len(refs)))
+    for j, d in enumerate(refs):
+        C[:, j] = _ks_connectivity(R, cols_for(d["up"], gene_to_col), cols_for(d["down"], gene_to_col), n)
+    # drop reference diseases with too few mapped genes (degenerate columns)
+    mapped = np.array([len(cols_for(d["up"], gene_to_col)) + len(cols_for(d["down"], gene_to_col)) for d in refs])
+    keep = mapped >= 10
+    C = C[:, keep]
+    print(f"usable reference diseases (>=10 mapped genes): {keep.sum()}")
+
+    real = _ks_connectivity(R, cols_for(sk_up, gene_to_col), cols_for(sk_down, gene_to_col), n)
+    ref_mean, ref_std = C.mean(axis=1), C.std(axis=1)
+    ref_std[ref_std == 0] = np.nan
+    spec_z = (real - ref_mean) / ref_std  # negative = reverses sickle more than diseases-in-general
+
+    ranked = pd.DataFrame({"spec_z": spec_z, "connectivity": real}, index=lincs.index).sort_values("spec_z")
+    ranked.insert(0, "rank", range(1, len(ranked) + 1))
+    profiles = pd.read_parquet(PROCESSED / "drug_profiles_v1.parquet").set_index("name")
+    ranked = ranked.join(profiles[["inclusion_reason"]])
+
+    N = len(ranked)
+    top10, top25, half = int(N * .10), int(N * .25), N // 2
+    hu, glut = int(ranked.loc["hydroxyurea", "rank"]), int(ranked.loc["glutamine", "rank"])
+    negs = {d: int(ranked.loc[d, "rank"]) for d in NEG5 if d in ranked.index}
+    n_bottom = sum(r > half for r in negs.values())
+
+    print("\n==== RECOVERY TEST (reference-calibrated tau) ====")
+    print(f"  hydroxyurea rank {hu}/{N} (top {100*hu/N:.1f}%)  top-10%? {hu <= top10}  z={ranked.loc['hydroxyurea','spec_z']:.2f}")
+    print(f"  L-glutamine rank {glut}/{N} (top {100*glut/N:.1f}%)  top-25%? {glut <= top25}")
+    print(f"  neg controls bottom-half: {n_bottom}/5  {negs}")
+    crit = (hu <= top10) and (glut <= top25) and (n_bottom >= 4)
+    print(f"  OVERALL: {'PASS' if crit else 'FAIL'}")
+    print("\n  top 12 by reference-calibrated z:")
+    for name, r in ranked.nsmallest(12, "spec_z").iterrows():
+        print(f"    {int(r['rank']):2d} {name:20s} z={r['spec_z']:6.2f} [{r['inclusion_reason']}]")
+
+
+if __name__ == "__main__":
+    main()

scripts/phaseD_supervised.py

@@ -0,0 +1,137 @@
+"""Phase D: supervised cross-disease repurposing — can labels break the specificity ceiling?
+
+Connectivity alone can't tell therapeutic from coincidental reversal. A supervised model trained
+on known drug-disease pairs CAN learn that pattern — given features that expose drug "broadness"
+(a drug that reverses everything is non-specific). We train on 839 GEO disease signatures with
+Repurposing-Hub indications as labels, evaluate with disease-grouped CV, then apply to HELD-OUT
+sickle and check whether the coincidental reversers (norethindrone, ciprofloxacin) finally drop.
+
+Baseline = rank by raw connectivity (the single conn feature). Win = model down-ranks the
+negative controls vs baseline while keeping hydroxyurea high.
+"""
+
+from __future__ import annotations
+
+import json
+import re
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.model_selection import GroupKFold, cross_val_predict
+from sklearn.metrics import roc_auc_score
+
+import sys
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+from src.scoring import _ks_connectivity  # noqa: E402
+
+PROCESSED = Path("data/processed")
+SIGS = Path("data/raw/disease_sigs")
+NEG5 = ["clotrimazole", "astemizole", "azithromycin", "ethinyl-estradiol", "caffeine"]
+ID_RE = re.compile(r"\s*(c\d{6,}|doid[-\s]?\d+|gse\d+|umls\S+)\s*", re.I)
+
+
+def clean_disease(term: str) -> str:
+    return ID_RE.sub(" ", term).strip().lower()
+
+
+def load_disease_sigs():
+    def parse(name):
+        out = {}
+        for line in (SIGS / name).read_text().splitlines():
+            p = line.split("\t")
+            if len(p) < 3:
+                continue
+            out[p[0]] = [g.split(",")[0].strip().upper() for g in p[2:] if g.strip()]
+        return out
+    up = parse("Disease_Perturbations_from_GEO_up.gmt")
+    down = parse("Disease_Perturbations_from_GEO_down.gmt")
+    terms = sorted(set(up) & set(down))
+    return [{"term": t, "disease": clean_disease(t), "up": up[t], "down": down[t]} for t in terms]
+
+
+def cols_for(genes, g2c):
+    return np.array([g2c[g] for g in set(genes) if g in g2c], dtype=int)
+
+
+def featurize(conn_col, C):
+    """Per-(drug,disease) features for one disease column conn_col, given full matrix C."""
+    drug_mean, drug_std = C.mean(1), C.std(1)
+    drug_min = C.min(1)
+    broad = (C < -0.10).mean(1)  # fraction of diseases this drug strongly reverses
+    dmean, dstd = conn_col.mean(), conn_col.std() or 1.0
+    return np.column_stack([
+        conn_col, drug_mean, drug_std, drug_min, broad,
+        np.full_like(conn_col, dmean),
+        (conn_col - drug_mean) / np.where(drug_std == 0, 1, drug_std),  # specificity within drug
+        (conn_col - dmean) / dstd,                                       # specificity within disease
+    ])
+
+
+FEATS = ["conn", "drug_mean", "drug_std", "drug_min", "broadness",
+         "disease_mean", "z_within_drug", "z_within_disease"]
+
+
+def main():
+    lincs = pd.read_parquet(PROCESSED / "lincs_signatures_v1.parquet")
+    drugs = list(lincs.index)
+    g2c = {g: i for i, g in enumerate(lincs.columns)}
+    n = len(lincs.columns)
+    R = lincs.rank(axis=1, ascending=False).to_numpy()
+
+    refs = [d for d in load_disease_sigs()
+            if len(cols_for(d["up"], g2c)) + len(cols_for(d["down"], g2c)) >= 10]
+    C = np.column_stack([_ks_connectivity(R, cols_for(d["up"], g2c), cols_for(d["down"], g2c), n) for d in refs])
+    print(f"{len(drugs)} drugs x {len(refs)} disease signatures; connectivity matrix {C.shape}")
+
+    # labels from Repurposing Hub indications
+    hub = pd.read_csv("data/raw/repurposing_drugs.txt", sep="\t", comment="!", low_memory=False)
+    ind = {r.pert_iname: [i.strip().lower() for i in re.split(r"[|,]", r.indication) if len(i.strip()) > 3]
+           for r in hub.itertuples() if isinstance(r.indication, str)}
+    drug_idx = {d: i for i, d in enumerate(drugs)}
+
+    X_rows, y, grp = [], [], []
+    for j, d in enumerate(refs):
+        feats = featurize(C[:, j], C)
+        dz = d["disease"]
+        for i, drug in enumerate(drugs):
+            inds = ind.get(drug, [])
+            label = int(any(dz == k or (len(dz) > 4 and dz in k) or (len(k) > 4 and k in dz) for k in inds))
+            X_rows.append(feats[i]); y.append(label); grp.append(j)
+    X = np.array(X_rows); y = np.array(y); grp = np.array(grp)
+    print(f"pairs: {len(y)}, positives: {y.sum()} ({100*y.mean():.2f}%)")
+
+    # disease-grouped CV (generalize to unseen diseases)
+    clf = GradientBoostingClassifier(random_state=0)
+    proba = cross_val_predict(clf, X, y, cv=GroupKFold(5), groups=grp, method="predict_proba")[:, 1]
+    print(f"disease-grouped CV AUC: {roc_auc_score(y, proba):.3f}  (conn-only AUC: {roc_auc_score(y, X[:,0]*-1):.3f})")
+
+    clf.fit(X, y)
+    print("feature importances: " + ", ".join(f"{f}={imp:.2f}" for f, imp in
+          sorted(zip(FEATS, clf.feature_importances_), key=lambda t: -t[1])))
+
+    # apply to HELD-OUT sickle
+    sig = json.loads((PROCESSED / "sickle_cell_signature_v1.json").read_text())
+    sk = _ks_connectivity(R, cols_for([g["gene"] for g in sig["up_regulated"]], g2c),
+                          cols_for([g["gene"] for g in sig["down_regulated"]], g2c), n)
+    Xs = featurize(sk, C)
+    p_sickle = clf.predict_proba(Xs)[:, 1]
+
+    res = pd.DataFrame({"model_p": p_sickle, "conn": sk}, index=drugs)
+    res["model_rank"] = res["model_p"].rank(ascending=False).astype(int)
+    res["conn_rank"] = res["conn"].rank(ascending=True).astype(int)  # most negative = best
+    N = len(res)
+    print(f"\n{'drug':14s} {'model_rank':>10s} {'conn_rank(base)':>16s}")
+    for d in ["hydroxyurea", "glutamine"] + NEG5 + ["norethindrone", "ciprofloxacin"]:
+        if d in res.index:
+            r = res.loc[d]
+            print(f"  {d:14s} {int(r['model_rank']):6d}/{N}      {int(r['conn_rank']):6d}/{N}")
+    nb_model = sum(res.loc[d, "model_rank"] > N/2 for d in NEG5 if d in res.index)
+    nb_conn = sum(res.loc[d, "conn_rank"] > N/2 for d in NEG5 if d in res.index)
+    print(f"\nneg controls bottom-half: model {nb_model}/5  vs  baseline {nb_conn}/5")
+    print("model top 10:", ", ".join(res.sort_values('model_p', ascending=False).head(10).index))
+
+
+if __name__ == "__main__":
+    main()

scripts/week2_lincs_extract.py

@@ -36,9 +36,15 @@ def read_gz_tsv(name: str) -> pd.DataFrame:
 
 
 def landmark_ids_and_symbols() -> tuple[list[str], dict[str, str]]:
-    lm = pd.read_csv(LINCS / "landmark_genes.csv")
-    ids = [str(x) for x in lm["pr_gene_id"]]
-    id_to_symbol = {str(r.pr_gene_id): r.pr_gene_symbol for r in lm.itertuples()}
+    """Gene row-ids + id->symbol map for the scored gene space.
+
+    v1.1: use the FULL 12,328-gene space (landmark + inferred), not just the 978 landmarks.
+    This lifts disease-signature overlap from 12% to ~85% and brings the erythroid markers into
+    scoring (see docs/recovery_test_report.md). Inferred genes are model-predicted (noisier).
+    """
+    g = pd.read_csv(LINCS / "GSE92742_gene_info.txt.gz", sep="\t")
+    ids = [str(x) for x in g["pr_gene_id"]]
+    id_to_symbol = {str(r.pr_gene_id): r.pr_gene_symbol for r in g.itertuples()}
     return ids, id_to_symbol
 
 

scripts/week3_scoring.py

@@ -1,13 +1,11 @@
-"""Week 3: run connectivity scoring over all drugs -> ranked_candidates_v1.csv (PLAN §6).
+"""Week 3 (v1.1): connectivity scoring over the full gene space with tau calibration.
 
-Loads the disease signature + the 300 drug LINCS signatures, computes the weighted-KS
-connectivity score per drug, and produces two rankings:
-  1. raw connectivity (most negative = strongest reversal = rank 1)
-  2. a secondary ranking blending connectivity with a mechanistic prior (sickle-relevant
-     target pathways), to temper broad-effect drugs (HDAC/kinase) that dominate raw rankings.
+Primary ranking is now **tau** (KS connectivity expressed as a signed percentile vs a null of
+random queries) — this calibrates out broad-effect drugs that connect to random signatures too,
+the specificity fix. The weighted connectivity score (WTCS) is retained as a reference column,
+and a secondary ranking blends tau with the sickle-pathway mechanistic prior.
 
-The formal recovery test (ground-truth + negative-control evaluation against the pre-registered
-criteria) is Week 4; this script only prints a sanity peek.
+Output: data/results/ranked_candidates_v1.csv.
 """
 
 from __future__ import annotations
@@ -19,10 +17,13 @@ import pandas as pd
 
 import sys
 sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
-from src.scoring import mechanistic_prior, persist_ranking, rank_drugs  # noqa: E402
+from src.scoring import (  # noqa: E402
+    connectivity_score, mechanistic_prior, normalize_scores, persist_ranking, tau_calibrate,
+)
 
 PROCESSED = Path("data/processed")
-PRIOR_LAMBDA = 0.5  # weight of the mechanistic prior in the secondary ranking
+N_NULL = 1000
+PRIOR_LAMBDA = 0.5  # spec_z credit per matched sickle pathway, for the blended ranking
 
 
 def main() -> None:
@@ -30,52 +31,51 @@ def main() -> None:
     up = [g["gene"] for g in sig["up_regulated"]]
     down = [g["gene"] for g in sig["down_regulated"]]
 
-    sig_matrix = pd.read_parquet(PROCESSED / "lincs_signatures_v1.parquet")  # drug x 978 symbols
+    sig_matrix = pd.read_parquet(PROCESSED / "lincs_signatures_v1.parquet")  # drug x 12,328 genes
     profiles = pd.read_parquet(PROCESSED / "drug_profiles_v1.parquet").set_index("name")
 
-    landmark = set(sig_matrix.columns)
-    n_up_ov = len(set(up) & landmark)
-    n_down_ov = len(set(down) & landmark)
-    print(f"query overlap with 978 landmarks: {n_up_ov} up + {n_down_ov} down = {n_up_ov + n_down_ov}")
-    print(f"scoring {len(sig_matrix)} drugs (all scored; 0 without signature)")
+    n_up = len(set(up) & set(sig_matrix.columns))
+    n_down = len(set(down) & set(sig_matrix.columns))
+    print(f"gene space: {sig_matrix.shape[1]} genes; query overlap {n_up} up + {n_down} down = {n_up + n_down}")
 
-    ranked = rank_drugs(up, down, sig_matrix)
+    # primary: tau calibration
+    print(f"computing tau over {N_NULL} random-query permutations ...", flush=True)
+    ranked = tau_calibrate(up, down, sig_matrix, n_null=N_NULL)
 
-    # attach metadata + mechanistic prior
+    # reference: weighted connectivity score (WTCS) + NCS
+    wtcs = pd.Series({d: connectivity_score(up, down, sig_matrix.loc[d]) for d in sig_matrix.index},
+                     name="connectivity_score")
+    ranked["connectivity_score"] = wtcs
+    ranked["normalized_score"] = normalize_scores(wtcs)
+
+    # metadata + mechanistic prior
     ranked = ranked.join(profiles[["chembl_id", "inclusion_reason", "targets", "mechanism_of_action"]])
     ranked["mechanistic_prior"] = ranked["targets"].apply(
-        lambda t: mechanistic_prior(list(t) if t is not None else [])
-    )
+        lambda t: mechanistic_prior(list(t) if t is not None else []))
     ranked["known_targets"] = ranked["targets"].apply(
-        lambda t: "; ".join(t) if t is not None and len(t) else ""
-    )
+        lambda t: "; ".join(t) if t is not None and len(t) else "")
     ranked = ranked.rename(columns={"mechanism_of_action": "mechanism_summary"})
 
-    # secondary, prior-weighted ranking: relevant drugs pushed toward better (more negative)
-    ranked["blended_score"] = ranked["normalized_score"] - PRIOR_LAMBDA * ranked["mechanistic_prior"]
+    # secondary, prior-weighted ranking (relevant drugs pushed toward more-negative spec_z)
+    ranked["blended_score"] = ranked["spec_z"] - PRIOR_LAMBDA * ranked["mechanistic_prior"]
     ranked["blended_rank"] = ranked["blended_score"].rank(method="first").astype(int)
 
     out = ranked.rename_axis("drug_name").reset_index()[[
-        "rank", "drug_name", "chembl_id", "connectivity_score", "normalized_score",
+        "rank", "drug_name", "chembl_id", "spec_z", "tau", "connectivity_ks", "connectivity_score",
         "inclusion_reason", "mechanistic_prior", "blended_rank", "known_targets", "mechanism_summary",
     ]]
     path = persist_ranking(out)
     print(f"wrote {path} ({len(out)} drugs)")
 
-    # --- sanity peek (formal recovery test is Week 4) ---
-    print("\n--- sanity peek (raw connectivity rank) ---")
+    print("\n--- sanity peek (spec_z ranking) ---")
     for gt in ["hydroxyurea", "glutamine"]:
         r = ranked.loc[gt]
-        pct = 100 * r["rank"] / len(ranked)
-        print(f"  {gt:12s} rank {int(r['rank'])}/{len(ranked)} (top {pct:.0f}%), "
-              f"score={r['connectivity_score']:.3f}")
-    neg = ranked[ranked["inclusion_reason"] == "negative_control"]
-    print(f"  negative controls in bottom half: "
-          f"{(neg['rank'] > len(ranked) / 2).sum()}/{len(neg)}")
-    print("\n  top 5 raw candidates:")
-    for name, r in ranked.nsmallest(5, "connectivity_score").iterrows():
-        print(f"    {int(r['rank']):3d}  {name:18s} {r['connectivity_score']:+.3f}  "
-              f"[{r['inclusion_reason']}] {r['known_targets'][:50]}")
+        print(f"  {gt:12s} rank {int(r['rank'])}/{len(ranked)} (top {100*r['rank']/len(ranked):.0f}%), "
+              f"spec_z={r['spec_z']:.2f}")
+    print("  top 10 by spec_z:")
+    for name, r in ranked.nsmallest(10, "spec_z").iterrows():
+        print(f"    {int(r['rank']):2d} {name:18s} z={r['spec_z']:6.2f} [{r['inclusion_reason']:16s}] "
+              f"{str(r['known_targets'])[:38]}")
 
 
 if __name__ == "__main__":

scripts/week4_recovery_test.py

@@ -36,6 +36,7 @@ def main() -> None:
         return int(df.loc[name, "rank"]) if name in df.index else None
 
     hu, glut = rk("hydroxyurea"), rk("glutamine")
+    glut_z = df.loc["glutamine", "spec_z"]
 
     # pick negative controls present in the ranking
     negs = {}
@@ -47,9 +48,8 @@ def main() -> None:
     print("=" * 60)
     print(f"N = {n}; top10 cut = {top10_cut}, top25 cut = {top25_cut}, bottom-half > {half}")
     print(f"\nhydroxyurea: rank {hu} (top {100*hu/n:.1f}%)  -> top-10%? {hu <= top10_cut}")
-    glut_score = df.loc["glutamine", "connectivity_score"]
-    print(f"L-glutamine: rank {glut} (top {100*glut/n:.1f}%), WTCS={glut_score:.3f}  "
-          f"-> top-25%? {glut <= top25_cut}  (has signature, so NOT 'missing-signature unscorable')")
+    print(f"L-glutamine: rank {glut} (top {100*glut/n:.1f}%), spec_z={glut_z:+.2f}  "
+          f"-> top-25%? {glut <= top25_cut}  (positive z => does not reverse; has a signature)")
     print("\nnegative controls (pre-specified, 1 per category):")
     n_bottom = 0
     for d, (cat, r) in negs.items():
@@ -70,10 +70,10 @@ def main() -> None:
     print(f"\nsecondary (mechanistic-prior) ranking: hydroxyurea blended_rank {hu_b} "
           f"(top {100*hu_b/n:.1f}%)")
 
-    print("\n--- TOP 10 (raw connectivity) ---")
-    top10 = df.nsmallest(10, "connectivity_score")
+    print("\n--- TOP 10 (primary spec_z ranking) ---")
+    top10 = df.sort_values("rank").head(10)
     for name, r in top10.iterrows():
-        print(f"  {int(r['rank']):2d}  {name:18s} {r['connectivity_score']:+.3f}  "
+        print(f"  {int(r['rank']):2d}  {name:18s} z={r['spec_z']:+.2f}  "
               f"[{r['inclusion_reason']}]  {str(r['known_targets'])[:45]}")
 
 

src/binding.py

@@ -0,0 +1,89 @@
+"""Structure-based binding track (PLAN §12).
+
+Two capabilities:
+- ligand-based retrieval (RDKit, works now): find existing drugs near a query molecule in
+  chemical space — validated, and the engine behind §12.9 generative-guided retrieval.
+- structure-based docking (§12.3): score whether a ligand binds a target pocket. Blocked on an
+  ARM-Mac docking toolchain (AutoDock Vina does not pip-install); see ``dock`` for options.
+
+Caveat carried throughout: chemical similarity != activity, and docking != efficacy (§12.6).
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from rdkit import Chem, DataStructs, RDLogger
+from rdkit.Chem import rdFingerprintGenerator
+
+RDLogger.DisableLog("rdApp.*")
+_MORGAN = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
+
+STRUCT_DIR = Path("data/raw/structures")
+
+# Known sickle small-molecule binders, by target (positive controls for the §12.4 recovery test).
+KNOWN_BINDERS = {
+    "hemoglobin": "voxelotor",
+    "PKR": "mitapivat",
+    "DNMT1": "decitabine",
+    "HDAC": "vorinostat",
+}
+
+# Curated target structures (PLAN §12.2). Add PDB ids as the harness grows.
+TARGET_PDB = {
+    "hemoglobin": "5E83",  # hemoglobin + voxelotor (GBT440)
+    "PKR": "8XFD",         # pyruvate kinase R + mitapivat
+}
+
+
+def morgan_fp(smiles: str):
+    """Morgan (ECFP4) fingerprint, or None for invalid / missing SMILES ('-666', '')."""
+    if not isinstance(smiles, str) or smiles in ("-666", ""):
+        return None
+    mol = Chem.MolFromSmiles(smiles)
+    return _MORGAN.GetFingerprint(mol) if mol else None
+
+
+def tanimoto(smiles_a: str, smiles_b: str) -> float | None:
+    fa, fb = morgan_fp(smiles_a), morgan_fp(smiles_b)
+    if fa is None or fb is None:
+        return None
+    return DataStructs.TanimotoSimilarity(fa, fb)
+
+
+def retrieve_nearest(
+    query_smiles: str,
+    library: dict[str, str],
+    top_n: int = 5,
+) -> list[tuple[float, str]]:
+    """Rank a library of {name: smiles} by Tanimoto similarity to a query molecule.
+
+    This is the §12.9 retrieval step: the query may be a known binder (positive-control beacon)
+    or a generated idealised binder; the returned existing drugs are repurposing candidates that
+    STILL require docking/validation (similarity != activity).
+    """
+    qfp = morgan_fp(query_smiles)
+    if qfp is None:
+        raise ValueError("invalid query SMILES")
+    sims = []
+    for name, smi in library.items():
+        fp = morgan_fp(smi)
+        if fp is not None:
+            sims.append((DataStructs.TanimotoSimilarity(qfp, fp), name))
+    return sorted(sims, reverse=True)[:top_n]
+
+
+def dock(target: str, ligand_smiles: str) -> float:
+    """Dock a ligand into a target pocket and return a binding score (PLAN §12.3).
+
+    Blocked: AutoDock Vina does not pip-install on ARM Mac and no docking binary is on PATH.
+    Resolve the toolchain first (one of):
+      - conda/micromamba: ``vina`` (conda-forge) or ``smina`` (bioconda), osx-arm64 builds
+      - an AF3-class co-folding model on GPU: Boltz-2 / Chai-1 / DiffDock (also predicts affinity)
+      - x86 Vina binary under Rosetta 2
+    Then: fetch TARGET_PDB[target], define the pocket box, prep the ligand (Meeko), score.
+    """
+    raise NotImplementedError(
+        "Docking toolchain unresolved on ARM Mac (PLAN §12.6 pitfall 4 / §12.8). "
+        "See docstring for options."
+    )

src/scoring.py

@@ -16,7 +16,7 @@ from pathlib import Path
 import numpy as np
 import pandas as pd
 
-from . import RESULTS_DIR
+from . import RANDOM_SEED, RESULTS_DIR
 
 # Sickle-cell-relevant target pathways for the mechanistic prior (PLAN §6 Week 3 task 3).
 # Keys are pathway categories; values are substrings matched (case-insensitive) against a
@@ -134,6 +134,92 @@ def mechanistic_prior(targets: list[str]) -> float:
     return float(sum(any(kw in text for kw in kws) for kws in SICKLE_PATHWAYS.values()))
 
 
+# --- KS connectivity + tau calibration (v1.1) -----------------------------------------------
+# Unweighted Kolmogorov-Smirnov connectivity (Lamb 2006) is O(k) per query (depends only on the
+# ranks of the query genes), which makes a permutation null over many random queries cheap. tau
+# expresses each drug's real connectivity as a signed percentile within its own null — so
+# broad-effect drugs that connect to *random* signatures too get down-weighted (specificity).
+
+
+def _ks_es(rank_matrix: np.ndarray, query_cols: np.ndarray, n_genes: int) -> np.ndarray:
+    """Vectorized unweighted KS enrichment score of a query gene set for every drug.
+
+    ``rank_matrix`` is (n_drugs, n_genes) of 1..N rank positions (1 = most up-regulated).
+    Returns one ES per drug; ES>0 => query enriched among up-regulated genes.
+    """
+    k = len(query_cols)
+    if k == 0:
+        return np.zeros(rank_matrix.shape[0])
+    p = np.sort(rank_matrix[:, query_cols], axis=1)  # (n_drugs, k), positions ascending
+    j = np.arange(1, k + 1)
+    a = (j / k - p / n_genes).max(axis=1)
+    b = (p / n_genes - (j - 1) / k).max(axis=1)
+    return np.where(a >= b, a, -b)
+
+
+def _ks_connectivity(rank_matrix: np.ndarray, up_cols: np.ndarray, down_cols: np.ndarray,
+                     n_genes: int) -> np.ndarray:
+    """KS connectivity per drug: (ES_up - ES_down)/2. Negative=reversal.
+
+    Note: unlike WTCS, this does NOT zero same-sign (ambiguous) connections — same-sign ES
+    partially cancel and land near 0 naturally. Hard-zeroing would collapse the random-query
+    null to a spike at 0 and make tau saturate, so the continuous form is required for calibration.
+    """
+    es_up = _ks_es(rank_matrix, up_cols, n_genes)
+    es_down = _ks_es(rank_matrix, down_cols, n_genes)
+    return (es_up - es_down) / 2.0
+
+
+def tau_calibrate(
+    up_genes: list[str],
+    down_genes: list[str],
+    signature_matrix: pd.DataFrame,
+    n_null: int = 1000,
+    seed: int = RANDOM_SEED,
+) -> pd.DataFrame:
+    """Rank drugs by tau: each drug's KS connectivity as a signed percentile vs a null of
+    random queries of the same size (PLAN §6; CMap tau, Subramanian 2017).
+
+    tau in [-100, 100]: -100 => reverses our signature more specifically than any random query
+    (strong, specific candidate); ~0 => connects to our signature no more than to random ones
+    (broad-effect / non-specific). Ranked by tau ascending (rank 1 = most specific reversal).
+    """
+    genes = list(signature_matrix.columns)
+    gene_to_col = {g: i for i, g in enumerate(genes)}
+    n = len(genes)
+    rank_matrix = signature_matrix.rank(axis=1, ascending=False).to_numpy()
+
+    up_cols = np.array([gene_to_col[g] for g in set(up_genes) if g in gene_to_col], dtype=int)
+    down_cols = np.array([gene_to_col[g] for g in set(down_genes) if g in gene_to_col], dtype=int)
+    real = _ks_connectivity(rank_matrix, up_cols, down_cols, n)
+
+    rng = np.random.default_rng(seed)
+    k_up, k_down = len(up_cols), len(down_cols)
+    null = np.empty((rank_matrix.shape[0], n_null))
+    for m in range(n_null):
+        samp = rng.choice(n, size=k_up + k_down, replace=False)
+        null[:, m] = _ks_connectivity(rank_matrix, samp[:k_up], samp[k_up:], n)
+
+    null_mean = null.mean(axis=1)
+    null_std = null.std(axis=1)
+    null_std[null_std == 0] = np.nan
+    # Per-drug standardized connectivity: how many SDs the real reversal is below what random
+    # queries of the same size produce against THIS drug. Continuous (no percentile floor), so it
+    # discriminates within the strong-reversal tail. Negative = specific reversal.
+    spec_z = (real - null_mean) / null_std
+
+    frac = (null <= real[:, None]).mean(axis=1)
+    tau = 100.0 * (2.0 * frac - 1.0)  # retained for reference; saturates at +/-100 in the tail
+
+    df = pd.DataFrame(
+        {"connectivity_ks": real, "null_mean": null_mean, "spec_z": spec_z, "tau": tau},
+        index=signature_matrix.index,
+    )
+    df = df.sort_values("spec_z")  # most negative z = most specific reversal
+    df.insert(0, "rank", range(1, len(df) + 1))
+    return df
+
+
 def persist_ranking(ranking: pd.DataFrame, out_path: Path | None = None) -> Path:
     """Write the ranked candidate list to ``data/results/ranked_candidates_v1.csv``."""
     out_path = out_path or (RESULTS_DIR / "ranked_candidates_v1.csv")

tests/test_scoring.py

@@ -114,6 +114,33 @@ class TestMechanisticPrior:
         assert mechanistic_prior(["Some unrelated kinase"]) == 0.0
 
 
+class TestTauCalibration:
+    """tau should reward a SPECIFIC reverser and give a near-zero score to a noise drug."""
+
+    @staticmethod
+    def _matrix() -> pd.DataFrame:
+        genes = [f"U{i}" for i in range(5)] + [f"D{i}" for i in range(5)] + [f"G{i}" for i in range(40)]
+        rng_vals = {g: 0.01 * ((hash(g) % 7) - 3) for g in genes}  # tiny deterministic noise
+        # specific reverser: query-up genes at the bottom, query-down at the top, rest ~0
+        specific = dict(rng_vals)
+        for i in range(5):
+            specific[f"U{i}"] = -8 - i
+            specific[f"D{i}"] = 8 + i
+        noise = dict(rng_vals)
+        return pd.DataFrame([specific, noise], index=["specific", "noise"])[genes]
+
+    def test_specific_reverser_has_strongly_negative_tau(self):
+        from src.scoring import tau_calibrate
+        up = [f"U{i}" for i in range(5)]
+        down = [f"D{i}" for i in range(5)]
+        out = tau_calibrate(up, down, self._matrix(), n_null=300, seed=0)
+        # Ranked by spec_z (continuous); the specific reverser is the most negative.
+        assert out.loc["specific", "spec_z"] < -2
+        assert out.loc["specific", "spec_z"] < out.loc["noise", "spec_z"]
+        assert out.loc["specific", "tau"] < -50  # tau also flags it (may saturate near -100)
+        assert out.loc["specific", "rank"] == 1
+
+
 def test_rank_drugs_orders_by_reversal():
     from src.scoring import rank_drugs
     genes = ["U1", "U2", "D1", "D2"] + [f"N{i}" for i in range(10)]