Pith Number

pith:QYZHTCMS

pith:2026:QYZHTCMSZTYIZHFKVE5RK7C3CD

not attested not anchored not stored refs resolved

Controllable Molecular Generative Foundation Models

Meng Jiang, Tengfei Luo, Weijiang Li, Yihan Zhu, Yuhan Liu

Molecular generation gains reliable control by operating on motifs rather than atoms in a diffusion process.

arxiv:2605.15354 v1 · 2026-05-14 · cs.LG

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\usepackage{pith}
\pithnumber{QYZHTCMSZTYIZHFKVE5RK7C3CD}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Across three heterogeneous benchmarks spanning materials and drug discovery, CoMole ranks first in controllability on all nine targets, reduces MAE by up to 48.2% relative to the strongest baselines, and maintains validity above 0.94 without rule-based correction or post-hoc filtering.

C2weakest assumption

The central premise that learning a motif-aware graph space successfully transfers pretrained structural priors into controllable generation and enables RL to optimize conditional reverse policies over chemically meaningful decisions without the bottlenecks of atom-level action spaces.

C3one line summary

CoMole uses a motif-aware graph diffusion pipeline with RL to rank first in controllability on nine targets across materials and drug benchmarks while keeping validity above 0.94 without post-processing.

References

38 extracted · 38 resolved · 1 Pith anchors

[1] DiGress: Discrete Denoising diffusion for graph generation , author=. 2023 , eprint= 2023

[2] Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation , author=. 2023 , eprint= 2023

[3] Graph Diffusion Transformers for Multi-Conditional Molecular Generation , author=. 2024 , eprint= 2024

[4] Junction Tree Variational Autoencoder for Molecular Graph Generation , author=. 2019 , eprint= 2019

[5] Advances in Neural Information Processing Systems , volume=

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:54.077691Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8632798992ccf08c9caaa93b157c5b10f8dd6790accc1480086f4ff25c0fc3a7

Aliases

arxiv: 2605.15354 · arxiv_version: 2605.15354v1 · doi: 10.48550/arxiv.2605.15354 · pith_short_12: QYZHTCMSZTYI · pith_short_16: QYZHTCMSZTYIZHFK · pith_short_8: QYZHTCMS

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/QYZHTCMSZTYIZHFKVE5RK7C3CD \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 8632798992ccf08c9caaa93b157c5b10f8dd6790accc1480086f4ff25c0fc3a7

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "efee410f7132209f6858c3fd0af3641e000c34b6632f4649eda05fa86c07d291",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T19:27:01Z",
    "title_canon_sha256": "e80cbc36f7c861e320df05616abae2574915a210c825a0b3e18d5ec20e6327cf"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15354",
    "kind": "arxiv",
    "version": 1
  }
}