Pith Number

pith:NDUFPPE6

pith:2026:NDUFPPE6BUZVWQXLDIPMDUND7H

not attested not anchored not stored refs pending

Advancing Ligand-based Virtual Screening and Molecular Generation with Pretrained Molecular Embedding Distance

Shiyun Wa, Simone Sciabola, Ye Wang, Yifei Wang

Pretrained embedding distances serve as an effective training-free measure of molecular similarity for virtual screening and generation.

arxiv:2604.24474 v2 · 2026-04-27 · cs.LG

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\pithnumber{NDUFPPE6BUZVWQXLDIPMDUND7H}

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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

pretrained embedding distance (PED) ... exhibits distinct correlations with traditional similarity metrics, and performs effectively in both ranking molecules for virtual screening and guiding molecular generation via reward design.

C2weakest assumption

That embeddings from general pretrained molecular models already capture the structural information needed for effective similarity measurement across targets, without requiring task-specific supervision or data curation.

C3one line summary

Pretrained molecular embedding distances provide an effective similarity metric for ligand-based virtual screening and molecular generation without task-specific training.

Receipt and verification

First computed	2026-06-09T01:04:43.145553Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

68e857bc9e0d335b42eb1a1ec1d1a3f9f595fbc2c9585cc47f89f7fe58c32310

Aliases

arxiv: 2604.24474 · arxiv_version: 2604.24474v2 · doi: 10.48550/arxiv.2604.24474 · pith_short_12: NDUFPPE6BUZV · pith_short_16: NDUFPPE6BUZVWQXL · pith_short_8: NDUFPPE6

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/NDUFPPE6BUZVWQXLDIPMDUND7H \
  | 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: 68e857bc9e0d335b42eb1a1ec1d1a3f9f595fbc2c9585cc47f89f7fe58c32310

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "3d0c638a16fbbb7270cf248c3f6dea40ce840f80c0f6b9c18dedce0418f647c9",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-04-27T13:43:20Z",
    "title_canon_sha256": "7d9661b1176009947aba37348c4e01aab75d5ae5d5dbdb744cb2278628bc30a3"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.24474",
    "kind": "arxiv",
    "version": 2
  }
}