https://github.com/anandsahuofficial/tbxt-hit-id

Multi-signal computational pipeline for TBXT G177D (Brachyury), chordoma's master regulator. TBXT Hackathon 2026.
https://github.com/anandsahuofficial/tbxt-hit-id
autodock-vina boltz-2 chordoma computational-chemistry drug-discovery gnina hit-identification mmgbsa muni-bio onepot openmm pillar-vc qsar rdkit rowan tbxt tbxt-hackathon-2026 virtual-screening
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Multi-signal computational pipeline for TBXT G177D (Brachyury), chordoma's master regulator. TBXT Hackathon 2026.
Host: GitHub
URL: https://github.com/anandsahuofficial/tbxt-hit-id
Owner: anandsahuofficial
License: mit
Created: 2026-05-08T03:10:51.000Z (26 days ago)
Default Branch: main
Last Pushed: 2026-05-10T18:39:38.000Z (23 days ago)
Last Synced: 2026-05-10T19:32:25.789Z (23 days ago)
Topics: autodock-vina, boltz-2, chordoma, computational-chemistry, drug-discovery, gnina, hit-identification, mmgbsa, muni-bio, onepot, openmm, pillar-vc, qsar, rdkit, rowan, tbxt, tbxt-hackathon-2026, virtual-screening
Language: Python
Homepage: https://github.com/anandsahuofficial/tbxt-hit-id
Size: 8.95 MB
Stars: 0
Watchers: 0
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
- Authors: AUTHORS.md
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README

          


  



# tbxt-hit-id

**Multi-signal computational pipeline for identifying small-molecule

inhibitors of TBXT (Brachyury) - chordoma's master transcription

factor and lineage-defining oncogenic driver.**

A reproducible end-to-end pipeline that integrates docking (Vina,

GNINA), generative co-folding (Boltz-2), free-energy refinement

(MMGBSA), target-specific QSAR (RF + XGBoost on 653 measured Naar SPR

Kd), and T-box paralog selectivity into a strict 7-criterion filter

chain - yielding 137 organizer-compliant hit candidates and a top-4

ranked submission.

> Built for the **TBXT Hit Identification Hackathon 2026**

> ([tbxtchallenge.org](https://tbxtchallenge.org)), hosted by

> [muni.bio](https://muni.bio) in Boston at

> [Pillar VC](https://www.pillar.vc/), with platform support from

> [Rowan](https://labs.rowansci.com) and

> [onepot.ai](https://www.onepot.ai).

> **Note on history.** This repository is the cleaned, post-hackathon

> version of the pipeline. The original development repository -

> including the full commit history from the pre-event build phase and

> the hackathon weekend - is preserved at

> [`anandsahuofficial/tbxt-hit-id-dev`](https://github.com/anandsahuofficial/tbxt-hit-id-dev).

---

## Citation

If you use this pipeline or adapt it for your own targets, please cite:

> Sahu, A. and contributors (2026). *tbxt-hit-id: Multi-signal

> computational pipeline for TBXT (Brachyury) hit identification.*

> https://github.com/anandsahuofficial/tbxt-hit-id

A machine-readable [`CITATION.cff`](CITATION.cff) is included for

automated citation tools (GitHub renders this as a "Cite this

repository" button in the right sidebar).

---

## Why this matters

Chordoma is a rare, locally aggressive cancer of the notochord remnant

(skull base, mobile spine, sacrum). It has no FDA-approved targeted

therapy and a 5-year survival around 70%. The transcription factor

**TBXT (Brachyury)** is its master regulator and is required for

tumor cell survival - silencing TBXT collapses chordoma cell viability.

This makes TBXT the most-validated drug target in chordoma, but it is

also a long-considered "undruggable" transcription factor with a

shallow DNA-binding domain pocket and high T-box paralog homology.

The **G177D variant** (`rs2305089`, allele frequency ~0.42) is enriched

in > 90% of Western chordoma cases and creates a unique pocket

(site F: Y88 / D177 / L42) that engages the variant residue directly -

a structural feature absent from the other 16 T-box paralogs and

therefore intrinsically selective.

This repo is the open computational pipeline that selected hit

candidates against that pocket from a 570-compound novelty-filtered

pool.

---

## Results

| | |

|---|---|

| Pool screened | **570** novelty-filtered compounds |

| Pass all 7 strict criteria | **137** organizer-compliant candidates |

| Submitted to experimental program | **24** compounds (4 to judges + 20 first batch) |

| Best predicted Boltz-2 Kd | **3.2 µM** (dual-engine 1.02× agreement) |

| Cost to source all 4 picks | **$875** via onepot.ai 100% catalog match |

| Site coverage on top 4 picks | **4 / 4** at site F (variant residue D177) |

The **4 picks** (`results/top4.csv`):

| # | ID | Boltz Kd (run A / B) | gnina Vina/pKd | Cost | Risks |

|---:|---|---:|---:|---:|:---:|

| 1 | FM002150_analog_0083 | 3.2 / 3.26 µM | -5.01 / 3.94 | $125 | low/low |

| 2 | FM001452_analog_0104 | 3.7 / 4.97 µM | -5.77 / 4.03 | $250 | med/med |

| 3 | FM001452_analog_0201 | 8.16 / 8.76 µM | -6.07 / 4.69 | $375 | high/med |

| 4 | FM001452_analog_0171 | 8.32 / 8.17 µM | -6.19 / 4.44 | $250 | med/med |

The full 137-candidate pool with every per-criterion pass/fail flag

is in `results/all_candidates_tiered.csv`.

---

## Approach - 6 orthogonal signals + 7-criterion strict gate

![Pipeline architecture](docs/architecture.png)

### Six orthogonal scoring signals

| Signal | What it catches |

|---|---|

| **Vina ensemble** (6 receptor confs) | Geometric fit, receptor flexibility |

| **GNINA CNN** pose + pKd | Vina-trap detection, ML affinity |

| **TBXT-specific QSAR** (RF + XGBoost on 653 measured Naar SPR Kd) | Target-specific affinity |

| **Boltz-2 generative co-folding** (two independent backends) | Independent affinity + binder/non-binder classifier |

| **MMGBSA implicit-solvent refinement** (top 30) | Free-energy refinement |

| **T-box paralog selectivity** (16 paralogs) | Off-target risk |

Each signal has a known failure mode that another signal in the

stack catches. No pick depends on a single score.

### Seven-criterion strict filter (T-0 hard gate)

```

C1  onepot.ai 100% catalog match (similarity = 1.000)

C2  strictly non-covalent

C3  Chordoma rule: MW ≤ 600, LogP ≤ 6, HBD ≤ 6, HBA ≤ 12

C4  lead-like ideal: 10–30 HA, HBD+HBA ≤ 11, < 5 rings, ≤ 2 fused

C5  PAINS-clean + no acid halides / aldehydes / diazo / imines /

      polycyclic > 2 fused / long alkyl

C6  Tanimoto < 0.85 to Naar / TEP / prior_art_canonical

C7  ESOL log S > -5 (DMSO @ 10 mM + aqueous @ 50 µM)

```

570 → 137 strict-pass → 4 picks. See [`docs/filter_chain.md`](docs/filter_chain.md).

### Four-tier ranking

| Tier | Definition | Count |

|---|---|---:|

| **T1 GOLD** | All criteria + Kd ≤ 5 µM + low/low risk | **0** (empty by design - honest finding, not overclaimed) |

| **T2 SILVER** | All criteria + Kd ≤ 10 µM + soluble | 16 |

| **T3 BRONZE** | All criteria + Kd ≤ 50 µM, borderline solubility | 89 |

| **T4 RELAXED** | All criteria + Kd ≤ 100 µM | 32 |

See [`docs/tier_definitions.md`](docs/tier_definitions.md).

---

## Cross-validation

- **Two independent Boltz-2 runs** on separate compute backends:

  4 / 4 picks agree within 1.34×

- **10-seed GNINA pose-stability** at site F across all 570 compounds:

  identifies pose-stable picks (σ < 0.05)

- **Rowan ADMET** (49 properties × 4 picks): all 4 ADMET-profiled

- **Rowan pose-analysis MD** (explicit-solvent, 5 ns × 1 traj +

  1 ns equil): protein-ligand RMSD trajectories captured per pick

- **muni.bio `onepot` tool**: all 4 picks at similarity = 1.000 with

  price + chemistry_risk + supplier_risk attached

Every pick is supported by multiple independent lines of evidence.

---

## Quickstart - reproduce the top 4 from a fresh clone

The recommended path is **container-first** - one CUDA-enabled image

shipped from GHCR carries every binary and Python dep, including

GNINA (which is otherwise glibc-fragile on HPC). A native conda

path is also available - see [`setup/README.md`](setup/README.md).

```bash

# 1. Clone

git clone https://github.com/anandsahuofficial/tbxt-hit-id

cd tbxt-hit-id

# 2. Pull the all-batteries-included container (~6-12 GB; one-time)

bash setup/pull_container.sh

# → ./tbxt-hit-id.sif

# 3. Fetch the receptor + bulk data assets

bash setup/fetch_receptor.sh           # PDB 6F59:A from RCSB

bash setup/fetch_data.sh               # candidate pool + Naar SPR Kd + receptor ensemble

bash setup/fetch_data.sh --include-poses   # OPTIONAL: pre-computed scores (~600 MB, enables --demo)

# 4. Confirm setup is ready (recommended before a multi-hour pipeline run)

apptainer exec --bind $PWD tbxt-hit-id.sif bash setup/smoke_test.sh

# 5. Run the pipeline end-to-end (the container has every binary + Python dep baked in)

apptainer exec --nv --bind $PWD tbxt-hit-id.sif bash examples/reproduce_top4.sh

# or:

apptainer exec --bind $PWD tbxt-hit-id.sif bash examples/reproduce_top4.sh --demo

# 6. Inspect results

cat results/top4.csv

```

Total runtime - full mode: **~6 hours** on a single RTX 4090 (24 GB)

for the 570-compound pool; ~12 hours for the full HPC variant matrix.

Demo mode: **< 2 minutes**, no GPU. See

[`setup/README.md`](setup/README.md) for HPC notes, the native conda

path, and the container-internals reference.

> **Data availability.** The bulk data bundle (570-compound pool,

> Naar SPR Kd training set, 6-conformer crystallographic ensemble, and optional

> pre-computed scoring outputs) is hosted as a separate Hugging Face

> dataset and downloaded by `setup/fetch_data.sh`. If the dataset is

> not yet published, `fetch_data.sh` will print a clear error

> message; the curated **post-pipeline outputs** in

> [`results/`](results/) are committed directly to this repo and can

> be inspected without any data fetch.

---

## Reuse - adapt for your own target

This pipeline is **target-agnostic**. To screen against a different

protein:

1. Replace the receptor and pocket centroid in `src/pipeline/define_pockets.py`

2. Drop your candidate compound pool into `data/pool.csv` (SMILES + ID columns)

3. (Optional) Re-train the QSAR model on your target's measured-affinity data

4. Re-run `examples/reproduce_top4.sh`

The 6-signal architecture, 7-criterion filter chain, tier ranking,

and cross-validation harness all generalize.

---

## What's in this repo

```

tbxt-hit-id/

├── README.md                  ← you are here

├── LICENSE  NOTICE          ← MIT + provenance

├── AUTHORS.md  CITATION.cff

├── environment.yml            ← one-command conda env

│

├── docs/                      ← deep-dive methodology

│   ├── methodology.md         ← 6-signal pipeline

│   ├── filter_chain.md        ← 7-criterion strict gate

│   ├── tier_definitions.md

│   └── architecture.png

│

├── slides/                    ← judges-facing deck

│   ├── slides.md  slides.pdf

│   ├── architecture.png       ← pipeline graphic (in deck)

│   └── renders/               ← 2D + 3D pose PNGs (4 picks)

│

├── results/                   ← curated post-pipeline outputs (committed)

│   ├── top4.csv  top5to24.csv  all_candidates_tiered.csv

│   └── selected/              ← cross-val summary, MD attempt log, variants

│

├── src/                       ← pipeline source (33 Python modules)

│   ├── pipeline/              ← vina, gnina, boltz, mmgbsa, qsar, paralog, consensus

│   ├── filters/               ← 7-criterion strict gate (+ PAINS + onepot membership)

│   ├── ranking/               ← 4-tier classifier

│   ├── enumeration/           ← one-pot reaction enumeration

│   └── viz/                   ← render helpers (2D + 3D pose)

│

├── setup/                     ← container + env + cold-start data fetchers

│   ├── Containerfile          ← CUDA + GNINA + conda env + src baked in (CI builds → GHCR)

│   ├── pull_container.sh      ← apptainer/singularity pull from ghcr.io

│   ├── fetch_receptor.sh      ← PDB 6F59:A from RCSB

│   ├── fetch_data.sh          ← candidate pool + Naar Kd + receptor ensemble (HF)

│   ├── HPC.md                 ← Singularity GNINA recipe + Boltz cache + SLURM

│   └── README.md              ← quick-start + troubleshooting

│

├── .github/workflows/

│   └── container.yml          ← builds + pushes container to ghcr.io on every push to main

│

├── tools/

│   └── render_slides.py       ← Markdown → HTML → Chromium PDF renderer

│

└── examples/

    └── reproduce_top4.sh      ← one-command end-to-end (--full or --demo)

```

---

## Citation

If you use this work, please cite:

```bibtex

@software{sahu2026tbxt,

  author       = {Sahu, Anand and contributors},

  title        = {tbxt-hit-id: Multi-signal computational pipeline

                  for TBXT (Brachyury) hit identification},

  year         = {2026},

  url          = {https://github.com/anandsahuofficial/tbxt-hit-id},

  note         = {TBXT Hit Identification Hackathon 2026, Boston}

}

```

A machine-readable [`CITATION.cff`](CITATION.cff) is included.

---

## Honest expectations (calibration)

- Public computational methods over-predict Kd by **6–25×** at the µM

  regime. Realistic SPR for these 4 picks: **18–200 µM range**.

- The pipeline is designed for **hit identification**, not lead

  optimization - none of the picks are expected to be sub-µM at SPR

  without follow-up SAR.

- The **T1 GOLD tier is empty by design** - no compound in the pool

  simultaneously hits Kd ≤ 5 µM AND low/low chemistry/supplier risk.

  This is surfaced honestly rather than gamed by relaxing the gate.

---

## Credits

- **Team Lead:** [Anand Sahu](https://github.com/anandsahuofficial) -

  pipeline architecture, methodology, multi-signal consensus

  integration, final pick selection, live demo

- **Team contributors:** see [`AUTHORS.md`](AUTHORS.md) for the full

  list of simulation, data-generation, and analysis contributors

### Platform & venue partners

- [muni.bio](https://muni.bio) - `onepot` catalog membership tool, CLI,

  and platform credits

- [Rowan](https://labs.rowansci.com) - ADMET, docking, and

  pose-analysis MD platform

- [onepot.ai](https://www.onepot.ai) - virtual catalog and one-pot

  synthesis library

- [Pillar VC](https://www.pillar.vc/) - Boston event venue

- [TBXT Hackathon](https://tbxtchallenge.org) - organizers, mentors,

  and TEPs

### Datasets

- **Naar SPR Kd dataset** (653 measured TBXT-binding affinities) -

  TBXT Hackathon 2026

- **PDB 6F59** chain A - TBXT G177D + DNA construct (matches the

  hackathon CF Labs SPR assay)

- **muni.bio onepot.ai catalog** - ~1.4M one-pot-accessible virtual

  compounds

### AI implementation assistance

Substantial portions of code, scripts, and prose were drafted with

AI assistance (Claude) under the team lead's direction. All

scientific decisions, parameter choices, and final outputs were

reviewed and accepted by the team lead, who is responsible for

all outcomes of this work.

---

## License

MIT - see [`LICENSE`](LICENSE). Use it, adapt it, share it.

Attribution required; see [`NOTICE`](NOTICE) for the authorship +

provenance record and [`CITATION.cff`](CITATION.cff) for the

preferred citation.

---

## Keywords

`chordoma` · `TBXT` · `Brachyury` · `T-box transcription factor` ·

`G177D` · `rs2305089` · `drug discovery` · `virtual screening` ·

`hit identification` · `multi-signal consensus` · `Vina` · `GNINA` ·

`Boltz-2` · `co-folding` · `MMGBSA` · `QSAR` · `ADMET` ·

`MD pose analysis` · `paralog selectivity` · `onepot.ai` ·

`muni.bio` · `Rowan` · `Pillar VC` · `TBXT Hackathon 2026`
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