Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T17:05:37.191675Z
Paper Citation Record · LEDGER
As of 22 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2411.14157.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T17:05:37.191675Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
49 of 49 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation ee6fa058-67b5-4dde-bcd4-cefe151ea943 · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback AI-enabled organoids: construction, analysi s, and application,
Reference 1
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Defining and Exploring Chemical Space s,
Reference 2
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Why 90% of clinical drug development fails and how to improve it?,
Reference 3
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Generative Models for de Novo Drug Design,
Reference 4
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Observation 9f2933bb-ddad-4c0d-a034-77c61c965ad1 · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Deep generative molecular design res hapes drug discovery,
Reference 5
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback D e novo molecular design and generative models,
Reference 6
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback De novo generation of hit-like molecul es from gene expression signatures using artificial intelligence,
Reference 7
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback A Generative and Causal Pharmacokine tic Model for Factor VIII in Hemophilia A: A Machine Learning Framework for Conti nuous Model Refine- ment,
Reference 8
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Application of machine learning techniques to the analysis and prediction of drug pharmacokinetics,
Reference 9
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Using Generative Mod eling to Endow with Po- tency Initially Inert Compounds with Good Bioavailability and Low Toxicity,
Reference 10
Source-reported events for the cited work
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Revolutionizin g pharmacokinetics: the dawn of AI-powered analysis,
Reference 11
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Observation fcba613e-5199-4a6a-9dbe-a976774463d8 · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Using Domain-Specifi c Fingerprints Generated through Neural Networks to Enhance Ligand-Based Virtual Sc reening,
Reference 12
Source-reported events for the cited work
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Observation adb8deb1-7db1-42d2-b193-e43616b2af9f · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback AI in drug discovery and its c linical relevance,
Reference 13
Source-reported events for the cited work
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Observation 0de201d0-3537-457a-bef0-4bc835ea2959 · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Deep learning for dr ug discovery: A study of identi- fying high efficacy drug compounds using a cascade transfer le arning approach,
Reference 14
Source-reported events for the cited work
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Observation bca50d4c-51e2-4595-8271-5128dda830d1 · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Unleashing the power of generative AI in drug discovery,
Reference 15
Source-reported events for the cited work
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Observation ded9fda0-b443-4141-99ff-47177178693e · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretraine d Transformers,
Reference 16
Source-reported events for the cited work
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Observation 93a0e2ba-391a-49dc-8262-388f4f21bf08 · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback ProGen: Language Modeling for Protein Generation
Reference 17
Source-reported events for the cited work
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Observation b74529ef-b3d7-4a89-8ac6-7d6f870ba58c · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Language models are few -shot learners,
Reference 18
Source-reported events for the cited work
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback DrugGPT: A GPT-based Strategy for Desi gning Potential Ligands Targeting Specific Proteins,
Reference 19
Source-reported events for the cited work
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Observation 2c0cac93-f049-49d4-9d3c-9e8c45dd8772 · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback SMILES, a Chemical Language and Inf ormation System: 1: Intro- duction to Methodology and Encoding Rules,
Reference 20
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Observation 92bc3356-2f96-42fd-b615-af7794b837b0 · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Auto regressive models for gene reg- ulatory network inference: Sparsity, stability and causal ity issues,
Reference 21
Source-reported events for the cited work
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Observation ecbec435-4854-40cf-ad19-c142d277b47f · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Refining dataset curation methods for deep learning-based automated tuberculosis screening,
Reference 22
Source-reported events for the cited work
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback A Deep Learning App roach to Antibiotic Dis- covery,
Reference 23
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Observation 791344ff-1f0e-4474-8817-e3c18d3f2da8 · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback DrugTar Improves Druggability Pred iction by Integrating Large Language Models and Gene Ontologies,
Reference 24
Source-reported events for the cited work
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Properties of FDA-approved small m olecule protein kinase in- hibitors: A 2024 update,
Reference 25
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Observation e4052ccd-39e3-4349-b84b-bd8b6a8d605a · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Physicochemical Properties a nd Pharmacokinetics,
Reference 26
Source-reported events for the cited work
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Observation c6138dca-9c59-4ebb-afea-bd7234ce653d · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Chapter 1 - Introduction,
Reference 27
Source-reported events for the cited work
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback DrugMetric: quantitative drug-likeness scoring based on chemical space distance,
Reference 28
Source-reported events for the cited work
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Drug Repurposing: An Effective Tool in Modern D rug Discovery,
Reference 29
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Observation edced7a5-59d1-40f6-b7a7-f2904188cd0e · outbound
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Angiotensin-converting enzyme open for business: s tructural insights into the subdomain dynamics,
Reference 30
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback DrugBank: a comprehensive resource for in silico drug disc overy and exploration.,
Reference 31
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback UniProt: the Universal Prote in Knowledgebase in 2023. Nucleic Acids Res. 51:D523–D531 (2023),
Reference 32
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Basic local alignment search tool,
Reference 33
Source-reported events for the cited work
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback The ChE MBL Database in 2023: A drug discovery platform spanning multiple bioact ivity data types and time periods,
Reference 34
Source-reported events for the cited work
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback ZINC20 - A Free Ultralarge-Scale C hemical Database for Ligand Discovery,
Reference 35
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback A vailable at: https://huggingface.co/docs/trl/en/index
Reference 36
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback A vailable at:https://github.com/openai/summarize-from-feedback
Reference 37
Source-reported events for the cited work
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback A vailable at: https://www.rdkit.org/
Reference 38
Source-reported events for the cited work
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback DisGeNET: A comprehensive pl atform integrating infor- mation on human disease-associated genes and variants,
Reference 39
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Wh y is Tanimoto index an appropriate choice for fingerprint-based similarity calcu lations?,
Reference 40
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback The Generation of a Unique Machine Descr iption for Chemical Struc- tures—A Technique Developed at Chemical Abstracts Service ,
Reference 41
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Protein-Ligand Blind Docking Using QuickVina-W with Inter-Process Spatio-Temporal Integration,
Reference 42
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments,
Reference 43
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Reference 44
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Reference 45
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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback and Banks, Jay L
Reference 46
Source-reported events for the cited work
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Observation 7c9ce7c2-ca1c-420b-bd2b-00346b62854d · outbound
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Reference 49
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Observation 388e946d-58a7-48f3-94c8-c18fa2596610 · outbound
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Reference 2015
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Observation b02efcda-7e75-41e5-b25e-81ce1f94e09d · outbound
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Reference 2020
Source-reported events for the cited work
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No inbound Pith citation observations are available.