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Paper Citation Record · LEDGER

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities

As of 14 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2502.17456.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.17456 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:29:52.994563Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T12:33:27.860796Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-29T12:43:26.100936Z

Reference resolution

24 of 24 outbound references displayed

  • verified exact0
  • verified fuzzy10
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b4a3dd26-b0a8-4b92-adae-85cfd666a3c9 · outbound

This paper cites Accessed: 2024-12-29.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Accessed: 2024-12-29

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T17:29:53.359899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.872748Z digest=sha256:9aa48ce5edd81d949d09286435fc367534830c333790a134938cf5819e3bad8a

Observation f5505f10-ca42-4fed-8fcb-feed6bf58bf0 · outbound

This paper cites Language Models are Few-Shot Learners.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Language Models are Few-Shot Learners

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-08T17:29:52.887915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:29:52.887915Z digest=sha256:5d25938940b5acc7d18d8467e8093f247d7e2416cd016d2d25c4be93d3462bcb

Observation 7ca32800-0b62-4599-a571-412c7d82cbd0 · outbound

This paper cites Connor W Coley, Luke Rogers, William H Green, and Klavs F Jensen.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Connor W Coley, Luke Rogers, William H Green, and Klavs F Jensen

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-08T17:29:52.912452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:29:52.912452Z digest=sha256:b2ebfcb58157a0dd68ad9bfbe409b917b5e870c245b1c44fd8e3fdadac0dbd42

Observation 5e164d5e-07d0-4da3-b333-d77801fdabf5 · outbound

This paper cites Sourabh Katoch, Sumit Singh Chauhan, and Vijay Kumar.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Sourabh Katoch, Sumit Singh Chauhan, and Vijay Kumar

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T17:29:53.302647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.923073Z digest=sha256:8c4de87f2b58f06edefcb3f428e627e08d3a83c21a794e2696e185c2e663f98a

Observation a20c024b-04a6-47f2-8fe9-f560531b5df8 · outbound

This paper cites Are large language models superhuman chemists?.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Are large language models superhuman chemists?

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-08T17:29:52.937403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:29:52.937403Z digest=sha256:6980092daaad1ac7199e923dac45ea217feb8182b473500e610219ed29f27a10

Observation 3ff7ffcd-e2d4-4625-9aa6-a5c8c560d6ed · outbound

This paper cites Illuminating search spaces by mapping elites.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Illuminating search spaces by mapping elites

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-08T17:29:52.941853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:29:52.941853Z digest=sha256:bd80909e353377fb168e64358f7bdea161a605234264619d47c594f62c3959a3

Observation e8e179b1-d3c1-424f-a8d3-753d5cc3b4ad · outbound

This paper cites an unresolved cited work.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-08T17:29:53.257599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.956212Z digest=sha256:660a81c63186f7dc49de8109e162c027df9531470a8db0fd2278a22839bdfe3c

Observation 5a21b4fe-d188-437c-bb47-e9bada3e37d6 · outbound

This paper cites Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy.Chemical science, 11(12):3316–3325, 2020a.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy.Chemical science, 11(12):3316–3325, 2020a

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T17:29:53.243495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.961522Z digest=sha256:7918b572f6c00710a0fecc5c9aa4aede66af0df7748561f62f7089036670ecbd

Observation ff587074-65f0-4093-9189-069fe3a9e2e4 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities LLaMA: Open and Efficient Foundation Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-08T17:29:52.971471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:29:52.971471Z digest=sha256:f48c5720bf159ecb5b0285b8f92d3578397a866357ecb47ee300d782a0d0459b

Observation 766dbb46-bd47-459a-ab6e-5b3bb60dbf01 · outbound

This paper cites Graph Attention Networks.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Graph Attention Networks

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-08T17:29:52.975943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:29:52.975943Z digest=sha256:22bfb8dceda997834aee88ceacde9d703add6287165635383fac7bf5db22a73e

Observation 08072aa2-056e-4958-ba3f-cbc5d6fed52c · outbound

This paper cites The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.Nucleic Acids Research, 52(D1):D1180–D1192, 11.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.Nucleic Acids Research, 52(D1):D1180–D1192, 11

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T17:29:53.214240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.980556Z digest=sha256:52ccf10a1dcda3359c45c570c169109c3b29b44d57f56e461f3c2ea0b992f9fa

Observation a584e79b-646f-4bb2-b0bc-391be75d1fef · outbound

This paper cites ChemLLM: A Chemical Large Language Model.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities ChemLLM: A Chemical Large Language Model

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-08T17:29:52.989717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:29:52.989717Z digest=sha256:e5642b9a2439a286c6b81644a3364b0795f914af528c88b59306da0fa8806bd1

Observation 89a3e2de-3d8b-4758-90cf-6e33333e6017 · outbound

This paper cites Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T17:29:53.199048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.994563Z digest=sha256:960734a299c8505b2e48adb66ba069b2d15b0c7aeea8e681bc68be973c5f5ce1

Observation 57e0f4bb-1d79-4c72-80d6-450678916beb · outbound

This paper cites Reaxys: A leading chemistry database.www.reaxys.com.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Reaxys: A leading chemistry database.www.reaxys.com

Reference 2002

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T17:29:53.317321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.918454Z digest=sha256:ea0838eff4a1d9ab389efdf02eb68f61b2b920a6fa6bcfec8c18e3d5cf4c5d73

Observation c6c605de-0f32-448b-a7ae-70d4988b0f25 · outbound

This paper cites Learning to Make Generalizable and Diverse Predictions for Retrosynthesis.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Learning to Make Generalizable and Diverse Predictions for Retrosynthesis

Reference 2015

Resolution
metadata mismatch
local_arxiv, observed 2026-08-08T17:29:53.141116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.898201Z digest=sha256:c7304673bda58f7eccf108dba0552211e0e31a1c3bbe668532e0b061384e61d2

Observation 05419490-7fa4-4d60-97c5-255490fcc134 · outbound

This paper cites Pubchem 2025 update.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Pubchem 2025 update

Reference 2016

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T17:29:53.288286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.927728Z digest=sha256:477bcb68db45d2100a061575e54fb0585f0e6b4535c2f71813ec77b413aef4a6

Observation 144fe077-4a50-402a-9a94-7f0af711ceb7 · outbound

This paper cites Smiles arbitrary target specification.https://www.daylight.com/ dayhtml_tutorials/languages/smarts/index.html.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Smiles arbitrary target specification.https://www.daylight.com/ dayhtml_tutorials/languages/smarts/index.html

Reference 2017

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T17:29:53.228107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.966576Z digest=sha256:d5ee84da7ed9659d0150451c69e80b2a10ef00c3c2a7b438e20f79a8e712b5e3

Observation 0b15ea26-6ccd-47c4-89ae-aac40c3a3ba0 · outbound

This paper cites Stefan Chmiela, Huziel E.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Stefan Chmiela, Huziel E

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-08T17:29:52.907792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:29:52.907792Z digest=sha256:8da3dbf3b794f41d4f97fb1fa66652bbff7a03c4591d1aa1361dd312ff6811df

Observation 3aa65a55-a679-48bc-8d9d-2d41257428f0 · outbound

This paper cites an unresolved cited work.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Unresolved cited work

Reference 2019

Resolution
unresolved
raw_fallback, observed 2026-08-08T17:29:53.331335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.903222Z digest=sha256:6a22bbb93d65ef357612230103e9f34af2ea5d3fcd54d91208bd898e3b54e68b

Observation 732e20cd-3ba5-44ff-93a3-0b87e92494f9 · outbound

This paper cites InternLM2 Technical Report.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities InternLM2 Technical Report

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-08T17:29:52.893162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:29:52.893162Z digest=sha256:33491adf736cffa988d2705eff5979dc8adf958f6762ce748e2c4e5c0df8a18a

Observation 6c01d929-a76c-4016-9055-4c8dd6329303 · outbound

This paper cites IAM graph database repository for graph based pattern recognition and machine learning.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities IAM graph database repository for graph based pattern recognition and machine learning

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T17:29:53.273457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.951703Z digest=sha256:864e20648929c800e9f7b6cfec34c2e2d6fad186dced6fa22548775a74f40b3c

Observation 389f5278-483d-4fcd-912e-f0d95db72d0c · outbound

This paper cites Distributionally Robust Optimization: A Review.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Distributionally Robust Optimization: A Review

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-08T17:29:52.946947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:29:52.946947Z digest=sha256:7698c0bb5ba2640397a28c131655e220ab61fc3f308375b0a66d5a2bab6f3cad

Observation eb23f5a7-747e-424e-9697-059fe5b6c23c · outbound

This paper cites GPT-4 Technical Report.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities GPT-4 Technical Report

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-08T17:29:52.878022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:29:52.878022Z digest=sha256:ee43e92ed1a36bc1becf0566d7df9eb10bd06bda9f8be5329e8ec78ad805645e

Observation 92ee6d72-eb42-450f-80fe-41049f596b74 · outbound

This paper cites Molgpt: molecular generation using a transformer-decoder model.

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Molgpt: molecular generation using a transformer-decoder model

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T17:29:53.345313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T17:29:52.883046Z digest=sha256:a70b55c7d3f63b7d2ab742783242b61679e4a1cbea24997f01d3325f2f290e08

Pith citing papers

Observation f6afc553-b311-4e06-9992-016e906d7cf9 · inbound

MACReD: A Multi-Agent Collaborative Reasoning Framework for Reaction Diagram Parsing cites this paper.

MACReD: A Multi-Agent Collaborative Reasoning Framework for Reaction Diagram Parsing Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-06-29T12:43:26.102756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-29T12:33:27.860796Z digest=sha256:6f623d964abdc39095b841c9211b356b3efac0086655e91fe699d245aadeb1f5