Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities | Paper Citation Record

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This paper cites Drugclip: Contrasive protein-molecule representation learning for virtual screening,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Drugclip: Contrasive protein-molecule representation learning for virtual screening,

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This paper cites Uni-mol: A universal 3d molecular representation learning framework,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Uni-mol: A universal 3d molecular representation learning framework,

Reference 2

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This paper cites Adapting protein language models for rapid dti prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Adapting protein language models for rapid dti prediction,

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This paper cites Drug–target interaction predication via multi-channel graph neural networks,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Drug–target interaction predication via multi-channel graph neural networks,

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This paper cites Hyperattentiondti: improving drug–protein interaction prediction by sequence-based deep learning with attention mechanism,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Hyperattentiondti: improving drug–protein interaction prediction by sequence-based deep learning with attention mechanism,

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This paper cites Predicting drug–protein interaction using quasi-visual question answering system,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Predicting drug–protein interaction using quasi-visual question answering system,

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This paper cites Moltrans: molecular interaction transformer for drug–target interaction prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Moltrans: molecular interaction transformer for drug–target interaction prediction,

Reference 7

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This paper cites A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction

Reference 8

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This paper cites Dtiam: a unified framework for predicting drug- target interactions, binding affinities and drug mechanisms,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Dtiam: a unified framework for predicting drug- target interactions, binding affinities and drug mechanisms,

Reference 9

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This paper cites Interpretable bilinear atten- tion network with domain adaptation improves drug–target prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Interpretable bilinear atten- tion network with domain adaptation improves drug–target prediction,

Reference 10

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This paper cites Psc-cpi: Multi-scale protein sequence-structure contrasting for efficient and generalizable compound-protein interaction prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Psc-cpi: Multi-scale protein sequence-structure contrasting for efficient and generalizable compound-protein interaction prediction,

Reference 11

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This paper cites Cross-modality and self-supervised protein em- bedding for compound–protein affinity and contact prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Cross-modality and self-supervised protein em- bedding for compound–protein affinity and contact prediction,

Reference 12

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This paper cites Mgndti: A drug-target interaction prediction framework based on multimodal representation learning and the gating mechanism,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Mgndti: A drug-target interaction prediction framework based on multimodal representation learning and the gating mechanism,

Reference 13

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This paper cites Perceiver cpi: a nested cross-attention network for compound–protein interaction prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Perceiver cpi: a nested cross-attention network for compound–protein interaction prediction,

Reference 14

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This paper cites Deeptta: a transformer-based model for predicting cancer drug response,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Deeptta: a transformer-based model for predicting cancer drug response,

Reference 15

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This paper cites A subcomponent-guided deep learning method for interpretable cancer drug response prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities A subcomponent-guided deep learning method for interpretable cancer drug response prediction,

Reference 16

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This paper cites Tgsa: protein–protein association-based twin graph neural networks for drug response prediction with similarity augmentation,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Tgsa: protein–protein association-based twin graph neural networks for drug response prediction with similarity augmentation,

Reference 17

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This paper cites Improving drug response prediction via integrating gene relationships with deep learning,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Improving drug response prediction via integrating gene relationships with deep learning,

Reference 18

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This paper cites Graphcdr: a graph neural network method with contrastive learning for cancer drug response prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Graphcdr: a graph neural network method with contrastive learning for cancer drug response prediction,

Reference 19

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This paper cites Contrastive learning drug response models from natural language supervision,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Contrastive learning drug response models from natural language supervision,

Reference 20

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This paper cites Zero-shot learning for preclinical drug screening,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Zero-shot learning for preclinical drug screening,

Reference 21

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This paper cites A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening,

Reference 22

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This paper cites Deep transfer learning of cancer drug responses by integrating bulk and single-cell rna-seq data,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Deep transfer learning of cancer drug responses by integrating bulk and single-cell rna-seq data,

Reference 23

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This paper cites WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer

Reference 24

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This paper cites Geometry-enhanced molecular representation learning for property prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Geometry-enhanced molecular representation learning for property prediction,

Reference 25

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This paper cites Sliced denoising: A physics-informed molecular pre-training method,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Sliced denoising: A physics-informed molecular pre-training method,

Reference 26

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This paper cites Pre-training molecular graph representation with 3d geometry,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Pre-training molecular graph representation with 3d geometry,

Reference 27

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This paper cites One transformer can understand both 2d & 3d molecular data,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities One transformer can understand both 2d & 3d molecular data,

Reference 28

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Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Mole: a founda- tion model for molecular graphs using disentangled attention,

Reference 29

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Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Molspectra: Pre- training 3d molecular representation with multi-modal energy spectra,

Reference 30

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This paper cites Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation,

Reference 31

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This paper cites Data-driven quantum chemical property prediction leveraging 3d conformations with uni- mol+,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Data-driven quantum chemical property prediction leveraging 3d conformations with uni- mol+,

Reference 32

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This paper cites Molecular con- trastive learning of representations via graph neural networks,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Molecular con- trastive learning of representations via graph neural networks,

Reference 33

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This paper cites Mul- timodal molecular pretraining via modality blending,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Mul- timodal molecular pretraining via modality blending,

Reference 34

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This paper cites Learning topology-specific experts for molecular property prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Learning topology-specific experts for molecular property prediction,

Reference 35

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This paper cites Exploring molecular pretraining model at scale,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Exploring molecular pretraining model at scale,

Reference 36

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This paper cites Graphmae: Self-supervised masked graph autoencoders,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Graphmae: Self-supervised masked graph autoencoders,

Reference 37

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This paper cites Triplet interaction improves graph transformers: accurate molecular graph learning with triplet graph transformers,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Triplet interaction improves graph transformers: accurate molecular graph learning with triplet graph transformers,

Reference 38

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This paper cites Instructor-inspired machine learning for robust molecular property prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Instructor-inspired machine learning for robust molecular property prediction,

Reference 39

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This paper cites Spherical message passing for 3d molecular graphs,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Spherical message passing for 3d molecular graphs,

Reference 40

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This paper cites Molecular set representation learning,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Molecular set representation learning,

Reference 41

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This paper cites Comenet: Towards complete and efficient message passing for 3d molecular graphs,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Comenet: Towards complete and efficient message passing for 3d molecular graphs,

Reference 42

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This paper cites Expressivity and generalization: Fragment-biases for molecular gnns,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Expressivity and generalization: Fragment-biases for molecular gnns,

Reference 43

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This paper cites Equivariant transformers for neural network based molecular potentials,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Equivariant transformers for neural network based molecular potentials,

Reference 44

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This paper cites Representing molecules as random walks over interpretable grammars,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Representing molecules as random walks over interpretable grammars,

Reference 45

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Observation bb7bd400-8751-4769-a3ce-b5c95e7e986a · outbound

This paper cites A theoretically-principled sparse, connected, and rigid graph representation of molecules,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities A theoretically-principled sparse, connected, and rigid graph representation of molecules,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.286544Z

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Observation 68a006b9-b392-4d09-9bf2-9bc6e60ec026 · outbound

This paper cites Hierarchical grammar-induced geometry for data-efficient molecular property prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Hierarchical grammar-induced geometry for data-efficient molecular property prediction,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.275724Z

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Observation 7d18f0f1-b3c6-45ba-8efb-fe565b8e69bb · outbound

This paper cites Equiformer: Equivariant graph attention transformer for 3d atomistic graphs,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Equiformer: Equivariant graph attention transformer for 3d atomistic graphs,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.264719Z

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Observation 4f11f5c9-ad40-4b79-8de1-47ab49d4f0c3 · outbound

This paper cites Graph sampling-based meta-learning for molecular property prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Graph sampling-based meta-learning for molecular property prediction,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.254845Z

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Observation 77cfbe21-4036-447e-b3ff-12977e72cf0a · outbound

This paper cites Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.245326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ed298818-20a2-41e1-af6b-07c20e6c19e1 · outbound

This paper cites Efficient sharpness- aware minimization for molecular graph transformer models,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Efficient sharpness- aware minimization for molecular graph transformer models,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.234556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6e83f19f-2926-4b1e-b6b6-1d26d522f25d · outbound

This paper cites ZeroDDI: A Zero-Shot Drug-Drug Interaction Event Prediction Method with Semantic Enhanced Learning and Dual-Modal Uniform Alignment.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities ZeroDDI: A Zero-Shot Drug-Drug Interaction Event Prediction Method with Semantic Enhanced Learning and Dual-Modal Uniform Alignment

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T23:05:48.598205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93e7dac2-800c-4588-bcb3-f1f0bd5cd39f · outbound

This paper cites Mkg-fenn: A multimodal knowledge graph fused end-to-end neural network for accurate drug– drug interaction prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Mkg-fenn: A multimodal knowledge graph fused end-to-end neural network for accurate drug– drug interaction prediction,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.224948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2bececef-ba4d-4a4e-8e6d-9a904b132b90 · outbound

This paper cites Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T23:05:48.605345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05febffd-f2d4-4738-8af2-a1ae42504485 · outbound

This paper cites Conditional graph information bottleneck for molecular relational learning,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Conditional graph information bottleneck for molecular relational learning,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.214843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ed9cba7-5357-4682-bf66-a61df36a6423 · outbound

This paper cites Phgl-ddi: A pre-training based hierarchical graph learning framework for drug-drug interaction prediction,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Phgl-ddi: A pre-training based hierarchical graph learning framework for drug-drug interaction prediction,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.204152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4a9a7432-c176-4216-a151-2250e8ea949b · outbound

This paper cites Enhancing drug-drug interaction prediction using deep attention neu- ral networks,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Enhancing drug-drug interaction prediction using deep attention neu- ral networks,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.193191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 061fa1f6-a4ef-4c17-817d-a5dfa8349705 · outbound

This paper cites Dual-channel learning framework for drug-drug interaction prediction via relation- aware heterogeneous graph transformer,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Dual-channel learning framework for drug-drug interaction prediction via relation- aware heterogeneous graph transformer,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.181914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3739ab57-e42a-4cfb-812e-1113ccc89aee · outbound

This paper cites GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T23:05:48.623222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbdad3fc-1dc8-46e4-a8cf-008a6c753531 · outbound

This paper cites Csgnn: Contrastive self-supervised graph neural network for molecular interaction predic- tion.,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Csgnn: Contrastive self-supervised graph neural network for molecular interaction predic- tion.,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.170355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 10f022fd-5b5e-4a15-b0d7-1f7aae1e798c · outbound

This paper cites Deep learning improves prediction of drug–drug and drug–food interactions,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Deep learning improves prediction of drug–drug and drug–food interactions,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.159751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6edd5dfd-16a9-46ff-8b74-ca4b312d9f1a · outbound

This paper cites Enhancing drug-drug interaction prediction using deep attention neural networks,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Enhancing drug-drug interaction prediction using deep attention neural networks,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.148806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 39c7e01f-4846-491e-b6ad-ade27d6d8e93 · outbound

This paper cites 3dlinker: An e (3) equivariant variational autoencoder for molecular linker design,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities 3dlinker: An e (3) equivariant variational autoencoder for molecular linker design,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.137038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 762f52cf-4f37-4aa6-be1f-6197720ef4ca · outbound

This paper cites Gf-vae: a flow-based variational autoencoder for molecule generation,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Gf-vae: a flow-based variational autoencoder for molecule generation,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.127748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7dcb8504-1155-4036-8fcc-3ab177aa97cb · outbound

This paper cites Molhf: a hierarchical normalizing flow for molecular graph generation,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Molhf: a hierarchical normalizing flow for molecular graph generation,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.118569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 731d0a73-62c3-4151-a224-3aceb4b736c2 · outbound

This paper cites Learning Neural Generative Dynamics for Molecular Conformation Generation.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Learning Neural Generative Dynamics for Molecular Conformation Generation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T23:05:48.648924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6da19bed-64ad-41d0-bc38-ada264c76832 · outbound

This paper cites A de novo molecular generation method using latent vector based generative adversarial network,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities A de novo molecular generation method using latent vector based generative adversarial network,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.108147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f7cd69c-a337-455e-8b47-eac3315469df · outbound

This paper cites Transformer- based objective-reinforced generative adversarial network to generate desired molecules,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Transformer- based objective-reinforced generative adversarial network to generate desired molecules,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.097347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 06c322b5-59af-47d4-96fd-1313cd12242f · outbound

This paper cites Score-based generative modeling of graphs via the system of stochastic differential equations,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Score-based generative modeling of graphs via the system of stochastic differential equations,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.085543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4899f347-154a-43a4-964d-0d35814cb969 · outbound

This paper cites Geometric latent diffusion models for 3d molecule generation,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Geometric latent diffusion models for 3d molecule generation,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.075277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation be4447a1-6b6a-4706-8703-2668fc80f327 · outbound

This paper cites Exploring chemical space with score- based out-of-distribution generation,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Exploring chemical space with score- based out-of-distribution generation,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.063830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d70d18d8-ecfb-4d09-892e-60df92d6c391 · outbound

This paper cites Decompdiff: Diffusion models with decomposed priors for structure-based drug design,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Decompdiff: Diffusion models with decomposed priors for structure-based drug design,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.053409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f3f38f9e-5c79-425b-bbdd-2d1601d26ee6 · outbound

This paper cites Graph diffusion transformers for multi-conditional molecular generation,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Graph diffusion transformers for multi-conditional molecular generation,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.043147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f9f1aeda-7159-4c63-ae9f-6d66327edd4b · outbound

This paper cites Junction tree variational autoen- coder for molecular graph generation,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Junction tree variational autoen- coder for molecular graph generation,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.032485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c2d4d64b-1141-4f6a-b8dc-597bb8c1454f · outbound

This paper cites Fflom: A flow-based autoregressive model for fragment- to-lead optimization,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Fflom: A flow-based autoregressive model for fragment- to-lead optimization,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.021260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f520db5f-9614-41b4-a5ad-9a0f79c6c937 · outbound

This paper cites Leveraging language model for advanced multiproperty molecular optimization via prompt engineering,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Leveraging language model for advanced multiproperty molecular optimization via prompt engineering,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:49.009235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8b91e7cb-7af5-4fe0-8223-67e7e2e10df8 · outbound

This paper cites Mol-cyclegan: a generative model for molecular optimization,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Mol-cyclegan: a generative model for molecular optimization,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:48.997115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation df100145-77ad-4309-a2d9-f61f3e4d751c · outbound

This paper cites Decom- popt: Controllable and decomposed diffusion models for structure-based molecular optimization,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Decom- popt: Controllable and decomposed diffusion models for structure-based molecular optimization,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:48.985602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 27b9786c-bac9-4f4a-bcb6-7f4014522335 · outbound

This paper cites A dual diffusion model enables 3d molecule generation and lead optimization based on target pockets,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities A dual diffusion model enables 3d molecule generation and lead optimization based on target pockets,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:48.975514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b1babd4b-6031-4e36-a087-631888004fd6 · outbound

This paper cites Fragment-Masked Diffusion for Molecular Optimization.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Fragment-Masked Diffusion for Molecular Optimization

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-08-07T23:05:48.825632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5ffe5ce8-7581-4fcb-a730-9030f87a07ca · outbound

This paper cites Mars: Markov molecular sampling for multi-objective drug discovery,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Mars: Markov molecular sampling for multi-objective drug discovery,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:48.964381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c3a72df6-b42e-40f3-aed4-0e9e0cd6ebf1 · outbound

This paper cites Molecule optimization by explainable evolution,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Molecule optimization by explainable evolution,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:48.952801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation efa62799-f55a-4660-920e-02729fe7e8da · outbound

This paper cites Molsearch: search-based multi-objective molecular generation and property opti- mization,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Molsearch: search-based multi-objective molecular generation and property opti- mization,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:48.941322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f26963e0-0bad-427b-8ea4-7bae86e97b6d · outbound

This paper cites Dif- ferentiable scaffolding tree for molecule optimization,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Dif- ferentiable scaffolding tree for molecule optimization,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:48.929823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ab33cf2-cfe9-4518-81c2-650f3348f3c7 · outbound

This paper cites Sample-efficient multi-objective molecular optimization with gflownets,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Sample-efficient multi-objective molecular optimization with gflownets,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:48.918057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1e7aead0-d072-4a07-919e-d31983423d5e · outbound

This paper cites Dynamic many-objective molecular optimization: Unfolding complexity with ob- jective decomposition and progressive optimization,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Dynamic many-objective molecular optimization: Unfolding complexity with ob- jective decomposition and progressive optimization,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:05:48.905940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9e9840de-5537-483f-8d5f-1edf629f10ec · outbound

This paper cites Text-guided multi-property molecular optimization with a diffusion language model,.

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities Text-guided multi-property molecular optimization with a diffusion language model,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T23:05:48.716998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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