Paper Citation Record · LEDGER

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements

As of 5 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2606.22731.

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

pith.paper-citation-record.v1
2606.22731 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T09:13:18.849983Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4baca36c-e7c3-41ff-a0fc-26424f36806e · outbound

This paper cites Auto Research with Specialist Agents Develops Effective and Non-Trivial Training Recipes.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Auto Research with Specialist Agents Develops Effective and Non-Trivial Training Recipes

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-04T09:59:45.807088Z

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Observation 69eb4e77-7e57-48db-b9c4-2c3eb4780e89 · outbound

This paper cites ADMET property prediction through combinations of molecular fingerprints.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements ADMET property prediction through combinations of molecular fingerprints

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:59:45.804585Z

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Observation 4589e704-df56-4e32-87e1-066c20123567 · outbound

This paper cites Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

Reference 3

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Observation 35642e0a-cc18-4d90-851d-7f242bb289ae · outbound

This paper cites Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik

Reference 4

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Observation 6100da5d-0885-4c6e-a8f3-e7175492c9be · outbound

This paper cites Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S

Reference 5

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Observation 2445a34c-55c7-467f-8014-914a3658e5fe · outbound

This paper cites Journal of Chemical Information and Modeling59, 895–913 (2019) https://doi.org/10.1021/acs.jcim.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Journal of Chemical Information and Modeling59, 895–913 (2019) https://doi.org/10.1021/acs.jcim

Reference 6

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Observation 9a391b94-16c7-43f1-b55f-89d656083466 · outbound

This paper cites Accessed 2026.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Accessed 2026

Reference 7

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Observation 5a4e5e17-b837-40b4-9312-193e9f024126 · outbound

This paper cites ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 8

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Observation 7c95ec05-bd8f-46ec-a331-b8e3ae50b269 · outbound

This paper cites Self-Supervised Graph Transformer on Large-Scale Molecular Data.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Self-Supervised Graph Transformer on Large-Scale Molecular Data

Reference 9

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Observation 6514a088-a09e-4f89-b6ea-375e407b8003 · outbound

This paper cites doi: 10.1038/s42256-022-00447-x.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements doi: 10.1038/s42256-022-00447-x

Reference 10

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Observation 7972e594-567f-41a6-beb5-6c61a1840328 · outbound

This paper cites Uni-Mol: A Universal 3D Molecular Representation Learning Framework.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Reference 11

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Observation 764c149a-eadc-4bbd-b9ec-bad75bc98a3f · outbound

This paper cites CatBoost: unbi- ased boosting with categorical features.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements CatBoost: unbi- ased boosting with categorical features

Reference 12

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Observation 7020fccc-3758-4346-b7c7-5387c3a25a2e · outbound

This paper cites Extended-Connectivity Fingerprints.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Extended-Connectivity Fingerprints

Reference 13

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Observation f2a68252-92b9-4ac5-b892-375a161363a3 · outbound

This paper cites QSAR: How Good Is It in Practice? Comparison of Descriptor Sets on an Unbiased Cross Section of Corporate Data Sets.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements QSAR: How Good Is It in Practice? Comparison of Descriptor Sets on an Unbiased Cross Section of Corporate Data Sets

Reference 14

Resolution: verified exact; doi, observed 2026-06-26T09:19:16.826546Z

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Observation 5d099491-bd5f-4d32-b41c-75c89d892b0d · outbound

This paper cites ErG: 2D Pharmacophore Descrip- tions for Scaffold Hopping.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements ErG: 2D Pharmacophore Descrip- tions for Scaffold Hopping

Reference 15

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Observation db9a7f0f-8715-4a59-9ad3-93192fcac50b · outbound

This paper cites Augmenting large language models with chemistry tools.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Augmenting large language models with chemistry tools

Reference 16

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Observation b6bf973c-780b-4490-9ade-ea6658e2e552 · outbound

This paper cites Autonomous chemical research with large language models.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Autonomous chemical research with large language models

Reference 17

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Observation d1bbe85d-17bc-46f8-b3aa-a0ee0d8c6764 · outbound

This paper cites The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies.Nature.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies.Nature

Reference 18

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Observation 3e79d91a-7b70-49f4-8560-4801644fd8b1 · outbound

This paper cites URL https://doi.org/ 10.1038/s41586-026-10644-y.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements URL https://doi.org/ 10.1038/s41586-026-10644-y

Reference 19

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Observation d80b62eb-6a13-44b0-a2b6-4a1ed62b6aa0 · outbound

This paper cites DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration

Reference 20

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Observation 81b31355-74c5-4d94-90e7-3df32c865dd6 · outbound

This paper cites MolAgent: Biomolecular Property Estimation in the Agentic Era.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements MolAgent: Biomolecular Property Estimation in the Agentic Era

Reference 21

Resolution: verified exact; doi, observed 2026-06-26T09:19:16.815085Z

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Observation 758214d9-b3a8-4bb1-bcf8-999f4e8ed793 · outbound

This paper cites Large language models for scientific discovery in molecular property prediction.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Large language models for scientific discovery in molecular property prediction

Reference 22

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Observation b29f2023-9bec-4c53-9097-d08ac15a5a84 · outbound

This paper cites MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation

Reference 23

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Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements MLE- bench: Evaluating Machine Learning Agents on Machine Learning Engineering

Reference 24

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Observation 7db60cd2-9fd7-4f46-a7f1-f9d0e66887f8 · outbound

This paper cites AIDE: AI-Driven Exploration in the Space of Code.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements AIDE: AI-Driven Exploration in the Space of Code

Reference 25

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This paper cites URL https: //doi.org/10.1038/s41586-026-10265-5.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements URL https: //doi.org/10.1038/s41586-026-10265-5

Reference 26

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Observation 741a4fac-b3f0-406f-856d-03240dc45b79 · outbound

This paper cites The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

Reference 27

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Observation 9c519e0a-6c3e-459c-aa60-a223f3be3432 · outbound

This paper cites FIRE-Bench: Evaluating AI Agents on the Rediscovery of Scientific Insights.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements FIRE-Bench: Evaluating AI Agents on the Rediscovery of Scientific Insights

Reference 28

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Observation 7e3d2dfa-9823-4de6-84a6-9ebbe8694272 · outbound

This paper cites Researchgym: Evaluating language model agents on real-world ai research.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Researchgym: Evaluating language model agents on real-world ai research

Reference 29

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Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research

Reference 30

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Observation f6e5c930-6965-43e6-a2da-62e60bc558b8 · outbound

This paper cites Benchmarking AI Agents for Addressing Scientific Challenges Across Scales.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Benchmarking AI Agents for Addressing Scientific Challenges Across Scales

Reference 31

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Observation 02fa077b-4678-4c5a-8fd1-a4802656fb18 · outbound

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Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Practical Bayesian Optimization of Machine Learning Algorithms

Reference 32

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Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms

Reference 33

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Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:59:45.799065Z

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Observation 95fbb528-1f1a-463f-bd3c-dcf3cbd5b160 · outbound

This paper cites On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

Reference 35

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Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Preserving Statistical Validity in Adaptive Data Analysis

Reference 36

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This paper cites Science , volume =.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Science , volume =

Reference 37

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Observation 490cdcc3-b7ad-4371-bd29-480918d40ee8 · outbound

This paper cites Lo-Hi: Practical ML Drug Discovery Benchmark.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Lo-Hi: Practical ML Drug Discovery Benchmark

Reference 38

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Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Blumenthal, and Olga V

Reference 39

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Observation 2cfd09a0-935b-4e72-922a-a6de29c3959b · outbound

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Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Masset, R

Reference 40

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Observation a16bfff3-bf14-4b9b-b8b1-9f8c22a20847 · outbound

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Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements XGBoost: A Scalable Tree Boosting System

Reference 41

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Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements LightGBM: A Highly Efficient Gradient Boosting Decision Tree

Reference 42

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Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements FLAML: A Fast and Lightweight AutoML Library

Reference 43

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Observation 81c01262-a2ec-478e-9ae0-d477e1a341d6 · outbound

This paper cites Accessed 2026.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements Accessed 2026

Reference 44

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Pith citing papers

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