Pith Number

pith:YKQZDIQ3

pith:2026:YKQZDIQ3HBRUY4IARCBELST6B3

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Atoms as Language: VQ-Atom: Semantic Discretization for Molecular Representation Learning

Takayuki Kimura

Vector quantization on atom embeddings yields discrete tokens for chemical contexts that boost protein-ligand prediction.

arxiv:2605.16823 v1 · 2026-05-16 · cs.LG

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Claims

C1strongest claim

Experimental results show that VQ-Atom consistently improves predictive performance compared to conventional tokenization approaches in protein-ligand interaction prediction under a protein-cold split setting without relying on 3D structural information.

C2weakest assumption

That the codebook entries learned via vector quantization on GNN embeddings correspond to chemically meaningful atomic contexts that are relevant to the downstream prediction task and generalize beyond the training distribution.

C3one line summary

VQ-Atom discretizes continuous GNN atom embeddings into chemically meaningful discrete tokens via vector quantization to improve molecular language modeling for downstream chemistry tasks.

References

15 extracted · 15 resolved · 0 Pith anchors

[1] Bert: Pre-training of deep bidirectional transformers for language understanding 2019

[2] Language models are few-shot learners.NeurIPS, 2020 2020

[3] Neural machine translation of rare words with subword units 2016

[4] Smiles, a chemical language and information system 1988

[5] Shortcut learning in deep neural networks.Nature Machine Intelligence, 2020 2020

Formal links

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Receipt and verification

First computed	2026-05-20T00:03:24.471740Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c2a191a21b38634c7100888245ca7e0ed1ca381a5d26119b6188b72df2a4ab11

Aliases

arxiv: 2605.16823 · arxiv_version: 2605.16823v1 · doi: 10.48550/arxiv.2605.16823 · pith_short_12: YKQZDIQ3HBRU · pith_short_16: YKQZDIQ3HBRUY4IA · pith_short_8: YKQZDIQ3

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YKQZDIQ3HBRUY4IARCBELST6B3 \
  | 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: c2a191a21b38634c7100888245ca7e0ed1ca381a5d26119b6188b72df2a4ab11

Canonical record JSON

{
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    "abstract_canon_sha256": "fe146792818b3478d278f05cbcc09f4bec68d135d66cbb89666e7b1da47364b3",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-16T05:45:54Z",
    "title_canon_sha256": "75dfe0046e564b7eb28ab56df1838b9d494fe63fdbc1e7112fa72ddff8666343"
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  "source": {
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    "kind": "arxiv",
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