Unraveling the Potential of Diffusion Models in Small Molecule Generation | Paper Citation Record

Observation ff5e8d87-2f9b-4816-be72-f50fa830623a · outbound

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Unraveling the Potential of Diffusion Models in Small Molecule Generation Unresolved cited work

Reference 1

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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

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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

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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

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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

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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

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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

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This paper cites Shape- conditioned3dmoleculegenerationviaequivariantdiffusionmodels.

Unraveling the Potential of Diffusion Models in Small Molecule Generation Shape- conditioned3dmoleculegenerationviaequivariantdiffusionmodels

Reference 8

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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

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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

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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

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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

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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

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This paper cites Autodock vina 1.2.

Unraveling the Potential of Diffusion Models in Small Molecule Generation Autodock vina 1.2

Reference 14

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Unraveling the Potential of Diffusion Models in Small Molecule Generation Unresolved cited work

Reference 30

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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

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This paper cites an unresolved cited work.

Unraveling the Potential of Diffusion Models in Small Molecule Generation Unresolved cited work

Reference 32

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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)

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:12.144069Z

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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

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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

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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

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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

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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

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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

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

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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

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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

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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 2026-08-14T06:32:32.682623+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

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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

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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

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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

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Observation a8851c0c-f14f-49c5-9d23-9b11499befbe · outbound

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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

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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

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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

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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

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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

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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 2026-08-14T06:32:32.682623+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

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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

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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

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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

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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

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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

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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

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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 2026-08-14T06:32:32.682623+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

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

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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

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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

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

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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

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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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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

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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

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