Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T23:01:11.725787Z
Paper Citation Record · LEDGER
Unraveling the Potential of Diffusion Models in Small Molecule Generation
As of 15 August 2026, this Paper Citation Record lists 92 of 92 outbound references and 0 inbound Pith citation observations for arXiv:2507.08005.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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
Reference resolution
92 of 92 outbound references displayed
- verified exact2
- verified fuzzy67
- unresolved23
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation ff5e8d87-2f9b-4816-be72-f50fa830623a · outbound
This paper cites an unresolved cited work.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Unresolved cited work
Reference 1
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d12c1275-e709-4b5d-a845-03734ff2889e · outbound
This paper cites The process of structure-based drug design.
Unraveling the Potential of Diffusion Models in Small Molecule Generation The process of structure-based drug design
Reference 2
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d89ef177-0541-41fc-8557-24d10cfe7cdc · outbound
This paper cites Geom, energy-annotated molecular conformations for property prediction and molecular gen- eration.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Geom, energy-annotated molecular conformations for property prediction and molecular gen- eration
Reference 3
- Resolution
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- no resolver link, observed 2026-08-06T23:01:11.466194Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 457a32ec-71d9-4ed0-b375-b4a597838acc · outbound
This paper cites Benchmarking structure-based three-dimensional molecular generative models using GenBench3D: ligand conformation quality matters.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Benchmarking structure-based three-dimensional molecular generative models using GenBench3D: ligand conformation quality matters
Reference 4
- Resolution
- verified exact
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Source-reported events for the cited work
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Observation a87a0ffa-1ea8-4bc9-9e82-34ba98cfec8b · outbound
This paper cites Quantifying the chemical beauty of drugs.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Quantifying the chemical beauty of drugs
Reference 5
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1c79379b-b97d-4ef7-a9a1-e5f42f6a9765 · outbound
This paper cites Gua- camol: benchmarking models for de novo molecular design.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Gua- camol: benchmarking models for de novo molecular design
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:01:11.477749Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 24707744-01c5-432c-81b4-3b8942b27444 · outbound
This paper cites Analog bits: Generating discrete data using diffusion models with self-conditioning, in: The Eleventh International Conference on Learning Representations.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Analog bits: Generating discrete data using diffusion models with self-conditioning, in: The Eleventh International Conference on Learning Representations
Reference 7
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f950b33f-5545-4171-b14d-4561d709b311 · outbound
This paper cites Shape- conditioned3dmoleculegenerationviaequivariantdiffusionmodels.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Shape- conditioned3dmoleculegenerationviaequivariantdiffusionmodels
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:01:11.485051Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b868274a-0003-4b77-95dd-2964c61c925c · outbound
This paper cites Ilvr: Conditioning method for denoising diffusion probabilistic models, in: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Ilvr: Conditioning method for denoising diffusion probabilistic models, in: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE
Reference 9
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c58ce5d5-9ace-4d55-95df-f571b8e05f3a · outbound
This paper cites Diffdock:Diffusionsteps,twists,andturnsformoleculardocking,in: International Conference on Learning Representations (ICLR 2023).
Unraveling the Potential of Diffusion Models in Small Molecule Generation Diffdock:Diffusionsteps,twists,andturnsformoleculardocking,in: International Conference on Learning Representations (ICLR 2023)
Reference 10
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e9edfc03-5687-46e6-a5bd-852efd489b3a · outbound
This paper cites MolGAN: An implicit generative model for small molecular graphs.
Unraveling the Potential of Diffusion Models in Small Molecule Generation MolGAN: An implicit generative model for small molecular graphs
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:01:11.495388Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d86593df-ab0b-4c4a-8cd6-ec48cc8f682b · outbound
This paper cites Diffusion models beat gans on image synthesis.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Diffusion models beat gans on image synthesis
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:01:11.498671Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5e84bd65-e4bf-4998-a783-7355f655b8fb · outbound
This paper cites E(3)-equivariant models cannot learn chirality: Field-based molecular generation.
Unraveling the Potential of Diffusion Models in Small Molecule Generation E(3)-equivariant models cannot learn chirality: Field-based molecular generation
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:01:11.501591Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 960ae2f7-ad5f-46dd-9b70-36f123982d08 · outbound
This paper cites Autodock vina 1.2.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Autodock vina 1.2
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:01:11.504974Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4690e86e-8ab9-4ec9-abc8-1d33692ab767 · outbound
This paper cites Translationbetweenmoleculesandnaturallanguage,in:Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Translationbetweenmoleculesandnaturallanguage,in:Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp
Reference 15
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d98da4ed-6445-4e84-a3ef-f7f3ca43b467 · outbound
This paper cites Estimationofsyntheticaccessibility score of drug-like molecules based on molecular complexity and fragment contributions.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Estimationofsyntheticaccessibility score of drug-like molecules based on molecular complexity and fragment contributions
Reference 16
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2717bf71-7bfc-4e16-8cd9-42f56740c2cd · outbound
This paper cites Three-dimensionalconvolutionalneural networksandacross-dockeddatasetforstructure-baseddrugdesign.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Three-dimensionalconvolutionalneural networksandacross-dockeddatasetforstructure-baseddrugdesign
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.395720Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8d7d4fd8-68b1-42be-92ae-77cf932b39ae · outbound
This paper cites Se (3)- transformers: 3d roto-translation equivariant attention networks.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Se (3)- transformers: 3d roto-translation equivariant attention networks
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.387672Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 307d033e-69ae-4dcb-831e-7b9c96aa30ef · outbound
This paper cites E (n) equivariant normalizing flows.
Unraveling the Potential of Diffusion Models in Small Molecule Generation E (n) equivariant normalizing flows
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.379723Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8a048741-ce5c-42f2-9b51-e7a443d35354 · outbound
This paper cites Autoregressive fragment-baseddiffusionforpocket-awareliganddesign,in:NeurIPS 2023 Generative AI and Biology (GenBio) Workshop.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Autoregressive fragment-baseddiffusionforpocket-awareliganddesign,in:NeurIPS 2023 Generative AI and Biology (GenBio) Workshop
Reference 20
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3c464007-1ed0-46b6-bf3e-73b9b89f5ba3 · outbound
This paper cites Pocketmol: a molecular visualization tool for the pocket pc, in: Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineer- ing (BIBE 2001), IEEE.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Pocketmol: a molecular visualization tool for the pocket pc, in: Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineer- ing (BIBE 2001), IEEE
Reference 21
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3be40a91-122e-4f0e-946f-3718b8969f70 · outbound
This paper cites Text-guided molecule generation with diffusion language model, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Text-guided molecule generation with diffusion language model, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp
Reference 22
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 7d024020-2ee2-4c90-8605-16579b905d33 · outbound
This paper cites Aligning target-aware molecule diffusionmodelswithexactenergyoptimization.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Aligning target-aware molecule diffusionmodelswithexactenergyoptimization
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.344625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 25402ac1-27b9-4c42-b06a-2453ce5bbb48 · outbound
This paper cites Linkernet: Fragment poses and linker co-design with 3d equivariant diffusion.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Linkernet: Fragment poses and linker co-design with 3d equivariant diffusion
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.335337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b188cd53-e7a4-4bd9-b749-466735243383 · outbound
This paper cites 3d equivariantdiffusionfortarget-awaremoleculegenerationandaffinity prediction, in: The Eleventh International Conference on Learning Representations.
Unraveling the Potential of Diffusion Models in Small Molecule Generation 3d equivariantdiffusionfortarget-awaremoleculegenerationandaffinity prediction, in: The Eleventh International Conference on Learning Representations
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.326370Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5c774787-5623-48e9-8e84-a25bc54dd093 · outbound
This paper cites Decompdiff: diffusion models with decomposed priors for structure-based drug design, in: Proceedings of the 40th International Conference on Machine Learning, pp.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Decompdiff: diffusion models with decomposed priors for structure-based drug design, in: Proceedings of the 40th International Conference on Machine Learning, pp
Reference 26
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 279b6afb-d7cb-4329-b40e-1105c5c61152 · outbound
This paper cites Denoising diffusion probabilistic models.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Denoising diffusion probabilistic models
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:01:11.547832Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8eb7962b-4449-4784-a5bf-af9ec44a148e · outbound
This paper cites Classifier-free diffusion guidance, in: NeurIPS 2021WorkshoponDeepGenerativeModelsandDownstreamAppli- cations.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Classifier-free diffusion guidance, in: NeurIPS 2021WorkshoponDeepGenerativeModelsandDownstreamAppli- cations
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.302740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2c095018-0a82-4e1d-aee4-68b049aa0743 · outbound
This paper cites Equivariant diffusion for molecule generation in 3d, in: International conference on machine learning, PMLR.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Equivariant diffusion for molecule generation in 3d, in: International conference on machine learning, PMLR
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.294533Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c6f261db-5ad6-4ed5-86a6-2e91f51ac872 · outbound
This paper cites an unresolved cited work.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Unresolved cited work
Reference 30
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T23:01:12.286666Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 38f36182-fae7-4b7c-9100-0c213b9a1a78 · outbound
This paper cites Learning joint 2-d and 3-d graph diffusion models for complete molecule generation.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Learning joint 2-d and 3-d graph diffusion models for complete molecule generation
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.270318Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8e145c26-be9e-464e-bb24-496d29709258 · outbound
This paper cites an unresolved cited work.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Unresolved cited work
Reference 32
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T23:01:12.278610Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2a326ad2-d8de-44d5-8344-ee98be234e1e · outbound
This paper cites Mdm: Molecular diffusion model for 3d molecule generation, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Mdm: Molecular diffusion model for 3d molecule generation, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.254310Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8020606b-6e56-419e-8c22-6eb4e3acd818 · outbound
This paper cites Adualdiffusionmodelenables 3dmoleculegenerationandleadoptimizationbasedontargetpockets.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Adualdiffusionmodelenables 3dmoleculegenerationandleadoptimizationbasedontargetpockets
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.262718Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 7ce86915-b3a3-4365-9901-5171299e29e6 · outbound
This paper cites Binding-adaptivediffusionmodelsfor structure-baseddrugdesign,in:ProceedingsoftheAAAIConference on Artificial Intelligence, pp.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Binding-adaptivediffusionmodelsfor structure-baseddrugdesign,in:ProceedingsoftheAAAIConference on Artificial Intelligence, pp
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.237314Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d7e58207-a974-437e-a5b8-7781f22078df · outbound
This paper cites Re-dock: Towards flexible and realistic molecular dock- ing with diffusion bridge, in: International Conference on Machine Learning, PMLR.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Re-dock: Towards flexible and realistic molecular dock- ing with diffusion bridge, in: International Conference on Machine Learning, PMLR
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.245455Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 86e00927-f0e2-42a7-99b5-c0edf25c83bf · outbound
This paper cites Estimation of non-normalized statistical models by score matching.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Estimation of non-normalized statistical models by score matching
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.215987Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 633517eb-f5c7-4b30-9930-cbbc809120f4 · outbound
This paper cites Protein-ligand inter- action prior for binding-aware 3d molecule diffusion models, in: The Twelfth International Conference on Learning Representations.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Protein-ligand inter- action prior for binding-aware 3d molecule diffusion models, in: The Twelfth International Conference on Learning Representations
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.229150Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 6fdcf802-6ed4-4f7d-8c15-e791fa1a1484 · outbound
This paper cites Junction tree variational autoencoder for molecular graph generation, in: International confer- ence on machine learning, PMLR.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Junction tree variational autoencoder for molecular graph generation, in: International confer- ence on machine learning, PMLR
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.200495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a32938a1-e985-414f-b12e-4f2ecd41a73c · outbound
This paper cites Equiv- ariant 3d-conditional diffusion model for molecular linker design.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Equiv- ariant 3d-conditional diffusion model for molecular linker design
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.208408Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation bb40f25a-1a0c-4a2b-b3d5-8caf03eada26 · outbound
This paper cites Auto-encoding variational{Bayes}, in: Int.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Auto-encoding variational{Bayes}, in: Int
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.184287Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 76aec5d5-b438-48a2-a9c0-179e65cce7b4 · outbound
This paper cites Torsional diffusion for molecular conformer generation.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Torsional diffusion for molecular conformer generation
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.192731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d21f4cec-4e56-4ed1-8489-56b9bb34a892 · outbound
This paper cites Diffusion-lm improves controllable text generation.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Diffusion-lm improves controllable text generation
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.168944Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 42332a86-445f-4433-8cab-3f04f3877130 · outbound
This paper cites an unresolved cited work.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Unresolved cited work
Reference 44
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T23:01:12.176343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a12b6b64-5bf2-4e57-9595-cb134d57a101 · outbound
This paper cites Functional-group-based diffusion for pocket-specific moleculegenerationandelaboration.AdvancesinNeuralInformation Processing Systems 36.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Functional-group-based diffusion for pocket-specific moleculegenerationandelaboration.AdvancesinNeuralInformation Processing Systems 36
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.160821Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 210854da-2013-4327-bf18-9f2747964727 · outbound
This paper cites AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design.
Unraveling the Potential of Diffusion Models in Small Molecule Generation AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design
Reference 46
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T23:01:11.754252Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8e86eada-d882-4b1d-9e26-c8387e5a827e · outbound
This paper cites Generating 3d molecules for target protein binding, in: International Conference on Machine Learning (ICML).
Unraveling the Potential of Diffusion Models in Small Molecule Generation Generating 3d molecules for target protein binding, in: International Conference on Machine Learning (ICML)
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.144069Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation cf057aa2-085f-4f9a-aa23-2915da48eb29 · outbound
This paper cites Flow matching for generative modeling, in: The Eleventh International Conference on Learning Representations.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Flow matching for generative modeling, in: The Eleventh International Conference on Learning Representations
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.152353Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 6ccdc613-f3ba-4ac3-b2a3-428c738192c7 · outbound
This paper cites Classifier-free graph diffusionformolecularpropertytargeting,in:JointEuropeanConfer- ence on Machine Learning and Knowledge Discovery in Databases, Springer.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Classifier-free graph diffusionformolecularpropertytargeting,in:JointEuropeanConfer- ence on Machine Learning and Knowledge Discovery in Databases, Springer
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.126258Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 96d74deb-936a-49e7-b004-4d2a00a490ce · outbound
This paper cites Repaint: Inpainting using denoising diffusion probabilisticmodels,in:ProceedingsoftheIEEE/CVFconferenceon computer vision and pattern recognition, pp.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Repaint: Inpainting using denoising diffusion probabilisticmodels,in:ProceedingsoftheIEEE/CVFconferenceon computer vision and pattern recognition, pp
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.135032Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 24fb7834-dafa-44ec-9f67-d2faed7325de · outbound
This paper cites Open babel: An open chemical toolbox.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Open babel: An open chemical toolbox
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.109173Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c143127c-2388-4238-b318-040bcfe3db6f · outbound
This paper cites 3d molecule generationbydenoisingvoxelgrids.
Unraveling the Potential of Diffusion Models in Small Molecule Generation 3d molecule generationbydenoisingvoxelgrids
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.117415Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c2de0ee5-c024-4147-a9d6-6f3213549259 · outbound
This paper cites Scalablediffusionmodelswithtransform- ers, in: Proceedings of the IEEE/CVF International Conference on P.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Scalablediffusionmodelswithtransform- ers, in: Proceedings of the IEEE/CVF International Conference on P
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.094859Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e1a39137-df64-4854-a33c-210348bb83dd · outbound
This paper cites Training language models to follow instructions with human feedback.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Training language models to follow instructions with human feedback
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:01:11.620238Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 548a8c68-1430-4c4f-adda-e06b1a19a1a0 · outbound
This paper cites Hitting stride by degrees: Fine grained molecular generation via diffusion model.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Hitting stride by degrees: Fine grained molecular generation via diffusion model
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.077620Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a4289a24-1bd1-4543-8b65-f34e8f8d8b7d · outbound
This paper cites Moldiff: Addressing the atom-bond inconsistency problem in 3d molecule diffusion genera- tion, in: International Conference on Machine Learning, PMLR.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Moldiff: Addressing the atom-bond inconsistency problem in 3d molecule diffusion genera- tion, in: International Conference on Machine Learning, PMLR
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.085615Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 06f65c17-6e3b-40d6-bbb0-5c62764f019d · outbound
This paper cites an unresolved cited work.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Unresolved cited work
Reference 57
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T23:01:12.060484Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 303d4760-b477-4815-821d-f4bc45fedb89 · outbound
This paper cites Rcsb protein data bank.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Rcsb protein data bank
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.069362Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2fb7d9f4-e022-45da-82f7-35d89f4bc1ad · outbound
This paper cites Quantum chemistry structures and properties of 134 kilo molecules.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Quantum chemistry structures and properties of 134 kilo molecules
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.042566Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 571a2eb0-9c22-4c81-a429-6e1420415473 · outbound
This paper cites Coarse-to-fine: a hierarchical diffusion model for molecule generation in 3d, in: International Conference on Machine Learning, PMLR.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Coarse-to-fine: a hierarchical diffusion model for molecule generation in 3d, in: International Conference on Machine Learning, PMLR
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.051241Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 08c46a22-2e62-44cc-9ade-4605b08796d6 · outbound
This paper cites A small-molecule tnikinhibitortargetsfibrosisinpreclinicalandclinicalmodels.Nature Biotechnology 43, 63–75.
Unraveling the Potential of Diffusion Models in Small Molecule Generation A small-molecule tnikinhibitortargetsfibrosisinpreclinicalandclinicalmodels.Nature Biotechnology 43, 63–75
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.026004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5085ddf1-4601-4e12-b331-0493538020c2 · outbound
This paper cites RDKit: Open-source cheminformatics.http://www.
Unraveling the Potential of Diffusion Models in Small Molecule Generation RDKit: Open-source cheminformatics.http://www
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.034352Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a8851c0c-f14f-49c5-9d23-9b11499befbe · outbound
This paper cites an unresolved cited work.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Unresolved cited work
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:01:11.651168Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8e0b7c28-734b-4f87-ac05-8d2b8e3ea378 · outbound
This paper cites High-resolution image synthesis with latent diffusion models, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.
Unraveling the Potential of Diffusion Models in Small Molecule Generation High-resolution image synthesis with latent diffusion models, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.017668Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 7082fed7-db69-4062-8dd4-3b42ea768e83 · outbound
This paper cites E (n) equivari- ant graph neural networks, in: International conference on machine learning, PMLR.
Unraveling the Potential of Diffusion Models in Small Molecule Generation E (n) equivari- ant graph neural networks, in: International conference on machine learning, PMLR
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.995718Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 74aec772-39d8-43f1-92cc-f820b3fb32fb · outbound
This paper cites Silvr: Guided diffusion for molecule generation.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Silvr: Guided diffusion for molecule generation
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:12.004139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ab37d709-cf2a-4933-bd54-bbc3377298ea · outbound
This paper cites Structure- baseddrugdesignwithequivariantdiffusionmodels.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Structure- baseddrugdesignwithequivariantdiffusionmodels
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.979248Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 65e6246d-d205-435e-a957-9162d7140684 · outbound
This paper cites Fast high-resolution image synthesis with latent ad- versarialdiffusiondistillation,in:SIGGRAPHAsia2024Conference Papers, pp.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Fast high-resolution image synthesis with latent ad- versarialdiffusiondistillation,in:SIGGRAPHAsia2024Conference Papers, pp
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.987746Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8174d88b-bf66-4946-9a03-d25980bb1193 · outbound
This paper cites Consistency models, in: International Conference on Machine Learning, PMLR.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Consistency models, in: International Conference on Machine Learning, PMLR
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.963877Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c3bcfed0-52a4-4508-8793-164bcabdbb56 · outbound
This paper cites an unresolved cited work.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Unresolved cited work
Reference 70
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T23:01:11.971375Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9fad3b5a-cd00-4486-8644-f7373cff8732 · outbound
This paper cites Score-based generative modeling through stochas- tic differential equations.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Score-based generative modeling through stochas- tic differential equations
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.947353Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0dc9a009-27fa-4039-af97-3ce805313989 · outbound
This paper cites Generative modeling by estimating gradients of the data distribution.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Generative modeling by estimating gradients of the data distribution
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.955615Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c70aed6a-3e19-4d29-8e55-6e53f947eeca · outbound
This paper cites Digress: Discrete denoising diffusion for graph generation, in: ICLR.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Digress: Discrete denoising diffusion for graph generation, in: ICLR
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.931689Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3352a39c-df8b-498d-9041-537f7ad724bf · outbound
This paper cites A deep learning approach to antibiotic discovery.
Unraveling the Potential of Diffusion Models in Small Molecule Generation A deep learning approach to antibiotic discovery
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.939405Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 52ff2076-2cf4-40ea-8e55-3b1b712a3ec8 · outbound
This paper cites Boosting performance of generative diffusion model for molecular docking by training on artificial binding pockets.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Boosting performance of generative diffusion model for molecular docking by training on artificial binding pockets
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.915569Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation f7c40332-03fb-4367-a030-86936e0fd594 · outbound
This paper cites Midi: Mixed graph and 3d denoising diffusion for molecule generation, in: Joint European Conference on Machine Learning and Knowledge Discov- ery in Databases, Springer.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Midi: Mixed graph and 3d denoising diffusion for molecule generation, in: Joint European Conference on Machine Learning and Knowledge Discov- ery in Databases, Springer
Reference 76
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.923895Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b6e30e98-047d-4f7e-a71c-6aef9984cd10 · outbound
This paper cites Swallowing the bitter pill: Simplified scalable conformer generation, in: ICML 2024 AI for Science Workshop.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Swallowing the bitter pill: Simplified scalable conformer generation, in: ICML 2024 AI for Science Workshop
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.898631Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e748b798-d904-47cf-bd4f-ae6822078916 · outbound
This paper cites Gldm: hit molecule generation with constrained graph latent diffusion model.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Gldm: hit molecule generation with constrained graph latent diffusion model
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.907275Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5c228854-ae66-4e0e-a6c2-7244b26f1031 · outbound
This paper cites Guided diffusionformoleculargenerationwithinteractionprompt.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Guided diffusionformoleculargenerationwithinteractionprompt
Reference 79
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.883230Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 4109d169-2166-453c-beb6-2109037dd46f · outbound
This paper cites Smiles, a chemical language and information system.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Smiles, a chemical language and information system
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.890684Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation cc2e529d-52d3-4984-b7eb-e6900c7df484 · outbound
This paper cites Geometric- facilitated denoising diffusion model for 3d molecule generation, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Geometric- facilitated denoising diffusion model for 3d molecule generation, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp
Reference 81
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.867024Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 15feba49-542f-44c1-82f9-0ddc4f4a9dcb · outbound
This paper cites Diffdec: Structure-aware scaffold decoration with an end-to-end diffusion model.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Diffdec: Structure-aware scaffold decoration with an end-to-end diffusion model
Reference 82
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.874811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5fb75079-58d2-497d-a593-efc0b0b6565b · outbound
This paper cites Geodiff: A geometric diffusion model for molecular conformation generation, in: International Conference on Learning Representations.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Geodiff: A geometric diffusion model for molecular conformation generation, in: International Conference on Learning Representations
Reference 83
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.850027Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b8010cff-45a2-4483-b479-026ce4e3c9c9 · outbound
This paper cites Geometric latent diffusion models for 3d molecule generation, in: International Conference on Machine Learning, PMLR.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Geometric latent diffusion models for 3d molecule generation, in: International Conference on Machine Learning, PMLR
Reference 84
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.858876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c2282eb7-31f5-44f5-b1ae-dc6058db7dc0 · outbound
This paper cites an unresolved cited work.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Unresolved cited work
Reference 85
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T23:01:11.833037Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9bbbd4d2-e950-4a61-b97d-9b9dffbfa734 · outbound
This paper cites Prompt-based 3d molecular diffusion models for structure-based drug design.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Prompt-based 3d molecular diffusion models for structure-based drug design
Reference 86
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.841798Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ea4fdb69-4098-4594-a033-c0730adc8573 · outbound
This paper cites Graph neural networks: A review of methods and applications.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Graph neural networks: A review of methods and applications
Reference 87
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.807485Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c49852c6-ab95-43cb-9b82-2ff77a737cad · outbound
This paper cites WileyInter- disciplinary Reviews: Computational Molecular Science 14, e1711.
Unraveling the Potential of Diffusion Models in Small Molecule Generation WileyInter- disciplinary Reviews: Computational Molecular Science 14, e1711
Reference 88
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.824729Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9606ad56-3ead-4499-aab6-2bdbbb96c94e · outbound
This paper cites Stride: Structure-guided generation for inverse design of molecules, in: NeurIPS 2023 AI for Science Workshop.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Stride: Structure-guided generation for inverse design of molecules, in: NeurIPS 2023 AI for Science Workshop
Reference 89
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.816507Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 36fb193b-e785-4393-b9a3-b29c47232cb0 · outbound
This paper cites Decompopt: Controllable and decomposed diffusion models for structure-basedmolecularoptimization,in:TheTwelfthInternational Conference on Learning Representations.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Decompopt: Controllable and decomposed diffusion models for structure-basedmolecularoptimization,in:TheTwelfthInternational Conference on Learning Representations
Reference 91
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.798896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d0b9cc5d-4147-466e-aeb3-a17575404358 · outbound
This paper cites Molsnapper: Conditioning diffusion for structure based drug design.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Molsnapper: Conditioning diffusion for structure based drug design
Reference 92
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:01:11.790331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation f0c4b7b0-f1cc-415e-8730-c41c9c094cc1 · outbound
This paper cites Nature , 1–3.
Unraveling the Potential of Diffusion Models in Small Molecule Generation Nature , 1–3
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:01:11.459028Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.