Model Summary
TxGravity-30B-A6B-Open is a therapeutics-focused, task-specialized model built on the Gravity-30B-A5B base. It predicts a broad range of therapeutic properties — small-molecule ADMET, toxicity, drug–target interaction, protein–protein and peptide–MHC interaction, and more — by converting the Therapeutic Data Commons (TDC) benchmark tasks into an instruction format for LLMs, in the same spirit as Google's TxGemma.
TxGravity is co-developed by SK Biopharmaceuticals (SKBP) and Trillion Labs.
It is trained on TDC tasks reformatted as single-turn instructions (instruction → answer), covering 57 tasks. Answers are formatted as (A)/(B) for binary classification or a normalized 000–1000 bin for regression.
Table with columns: Property, Value| Property | Value |
|---|
| Total Parameters | 29.56B |
| Active Parameters | ~6B |
| Architecture | GravityMoE (DeepSeek-V3-compatible) |
| Layers | 52 |
| Routed / Shared Experts | 64 (top-8) / 1 |
| Context Length | 8,192 tokens |
| Precision | bf16 |
| Base model | Gravity-30B-A5B |
⚠️ This is a task-specialized property predictor, not a general instruction-tuned or safety-aligned assistant. Its outputs are intended for the TDC-style therapeutic prediction prompts it was trained on. Predictions may be inaccurate, biased, or incomplete and must be independently verified before any experimental, clinical, or decision-making use.
Evaluated on the TDC therapeutic benchmark against a reproduced TxGemma-27B baseline. Over 60 comparable tasks: 16 wins / 26 ties / 18 losses (6 tasks excluded for lack of a paper-reported comparison). A win/loss requires the margin to exceed the tie threshold; otherwise the task is scored a tie.
Wins (16 tasks) — TxGravity beats TxGemma-27B on: bindingdb_ki, bindingdb_ic50, protein_sabdab, ppbr_az, caco2_wang, lipophilicity_astrazeneca, half_life_obach, clearance_hepatocyte_az, ld50_zhu, bioavailability_ma, buchwald_hartwig, drugcomb_css, drugcomb_loewe, drugcomb_zip, drugcomb_hsa, drugcomb_bliss — spanning binding affinity, ADMET, toxicity, and drug-combination synergy.
Highlights: bindingdb_ki PCC 0.750 vs -0.112, bindingdb_ic50 Spearman 0.766 vs 0.643, protein_sabdab MAE 0.969 vs 2.332, ld50_zhu MAE 0.651 vs 0.776.
Full results
Margin is TxGravity - TxGemma, in percentage points (%p) for score metrics or relative percent (%rel) for error metrics (MAE/MSE, lower is better). Up-arrow = higher is better, down-arrow = lower is better.
Table with columns: Task, Metric, N, TxGravity, TxGemma-27B, Margin, Verdict| Task | Metric | N | TxGravity | TxGemma-27B | Margin | Verdict |
|---|
| bindingdb_ic50 | Spearman ↑ | 35725 | 0.7660 | 0.6430 | +12.3%p | WIN |
| bindingdb_ki | PCC ↑ | 11857 | 0.7500 | -0.1120 | +86.2%p |
Also included in this repo as summary.csv.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("trillionlabs/TxGravity-30B-A6B-Open")
model = AutoModelForCausalLM.from_pretrained(
"trillionlabs/TxGravity-30B-A6B-Open", dtype="bfloat16", device_map="auto")
messages = [{"role": "user", "content": "<TDC-formatted instruction here>"}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=16)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
The model expects TDC-style prompts and answers in TDC format: (A)/(B) for classification, or a 000–1000 normalized bin for regression.
License
Apache 2.0
Citation
@misc{txgravity2026,
title = {TxGravity-30B-A6B-Open},
author = {SK Biopharmaceuticals and Trillion Labs},
year = {2026},
howpublished = {\url{https://huggingface.co/trillionlabs/TxGravity-30B-A6B-Open}}
}
This model builds on the Therapeutic Data Commons (TDC) benchmark:
@article{huang2021therapeutics,
title = {Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development},
author = {Huang, Kexin and Fu, Tianfan and Gao, Wenhao and Zhao, Yue and Roohani, Yusuf and Leskovec, Jure and Coley, Connor W and Xiao, Cao and Sun, Jimeng and Zitnik, Marinka},
journal = {Proceedings of Neural Information Processing Systems, NeurIPS Datasets and Benchmarks},
year = {2021}
}
