Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization | Paper Citation Record

Observation 3a545538-4d88-497d-b91e-f748c3a82980 · outbound

This paper cites Machine learning in drug discovery: A review.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Machine learning in drug discovery: A review

Reference 1

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Observation 72a2ef9b-5790-4839-b59f-3e56599e284d · outbound

This paper cites Deep learning methods for small molecule drug discovery: A survey.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Deep learning methods for small molecule drug discovery: A survey

Reference 2

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This paper cites Neural message passing for quantum chemistry, In: Precup D, Teh YW , editors.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Neural message passing for quantum chemistry, In: Precup D, Teh YW , editors

Reference 3

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This paper cites The graph neural network model.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization The graph neural network model

Reference 4

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This paper cites Graph neural networks: A review of methods and applications.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Graph neural networks: A review of methods and applications

Reference 5

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Observation a308d804-c7fb-4475-8bd4-2d0b7fb40e8a · outbound

This paper cites Structure based protein and small molecule generation using EGNN and diffusion models: A comprehensive review.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Structure based protein and small molecule generation using EGNN and diffusion models: A comprehensive review

Reference 6

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This paper cites Explainable artificial intelligence for drug discovery and development: A comprehensive survey.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Explainable artificial intelligence for drug discovery and development: A comprehensive survey

Reference 7

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This paper cites DTI-Voodoo: Machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization DTI-Voodoo: Machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions

Reference 8

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This paper cites Enhancing preclinical drug discovery with artificial intelligence.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Enhancing preclinical drug discovery with artificial intelligence

Reference 9

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This paper cites Bench marking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Bench marking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence

Reference 10

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This paper cites Artificial intelligence in drug discovery and development.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Artificial intelligence in drug discovery and development

Reference 11

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This paper cites ChEMBL: A large-scale bioactivity database for drug discovery.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization ChEMBL: A large-scale bioactivity database for drug discovery

Reference 12

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This paper cites Quantitative evaluation of explainable graph neural networks for molecular property prediction.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Quantitative evaluation of explainable graph neural networks for molecular property prediction

Reference 13

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This paper cites GNNExplainer: Generating Explanations for Graph Neural Networks.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization GNNExplainer: Generating Explanations for Graph Neural Networks

Reference 14

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This paper cites Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism

Reference 15

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This paper cites Parameterized Explainer for Graph Neural Network.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Parameterized Explainer for Graph Neural Network

Reference 16

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This paper cites Structure-aware multimodal deep learning for drug–protein interaction prediction.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Structure-aware multimodal deep learning for drug–protein interaction prediction

Reference 17

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This paper cites MM-DRPNet: A multimodal dynamic radial partitioning network for enhanced protein–ligand binding affinity prediction.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization MM-DRPNet: A multimodal dynamic radial partitioning network for enhanced protein–ligand binding affinity prediction

Reference 18

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This paper cites DrugForm-DTA: Towards real-world drug- target binding affinity model.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization DrugForm-DTA: Towards real-world drug- target binding affinity model

Reference 19

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This paper cites G–PLIP: Knowledge graph neural network for structure-free protein– ligand bioactivity prediction.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization G–PLIP: Knowledge graph neural network for structure-free protein– ligand bioactivity prediction

Reference 20

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This paper cites Evolving concept of activity cliffs.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Evolving concept of activity cliffs

Reference 21

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This paper cites Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity

Reference 22

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This paper cites Benchmarking molecular feature attribution methods with activity cliffs.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Benchmarking molecular feature attribution methods with activity cliffs

Reference 23

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This paper cites DIG: A turnkey library for diving into graph deep learning research.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization DIG: A turnkey library for diving into graph deep learning research

Reference 24

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This paper cites Coloring molecules with explainable artificial intelligence for preclinical relevance assessment.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Coloring molecules with explainable artificial intelligence for preclinical relevance assessment

Reference 25

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This paper cites Using attribution to decode binding mechanism in neural network models for chemistry.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Using attribution to decode binding mechanism in neural network models for chemistry

Reference 26

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This paper cites Learning important features through propagating activation differences.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Learning important features through propagating activation differences

Reference 27

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Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Grad-CAM: Visual explanations from deep networks via gradient-based localization

Reference 28

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Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Src family tyrosine kinases

Reference 29

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Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Src protein-tyrosine kinase structure, mechanism, and small molecule inhibitors

Reference 30

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This paper cites c-Abl tyrosine kinase down regulation as target for memory improvement in Alzheimer’s disease Front Aging Neurosci.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization c-Abl tyrosine kinase down regulation as target for memory improvement in Alzheimer’s disease Front Aging Neurosci

Reference 31

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This paper cites The potential therapeutic role of Bruton tyrosine kinase inhibition in neurodegenerative diseases.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization The potential therapeutic role of Bruton tyrosine kinase inhibition in neurodegenerative diseases

Reference 32

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This paper cites Anaplastic lymphoma kinase (ALK) receptor tyrosine kinase: A catalytic receptor with many faces.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Anaplastic lymphoma kinase (ALK) receptor tyrosine kinase: A catalytic receptor with many faces

Reference 33

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This paper cites Src family kinases as therapeutic targets in advanced solid tumors: What we have learned so far.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Src family kinases as therapeutic targets in advanced solid tumors: What we have learned so far

Reference 34

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This paper cites Recent developments of protein kinase inhibitors as potential AD therapeutics.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Recent developments of protein kinase inhibitors as potential AD therapeutics

Reference 35

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This paper cites A sparse-group lasso.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization A sparse-group lasso

Reference 36

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This paper cites BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology

Reference 37

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This paper cites The Protein Data Bank.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization The Protein Data Bank

Reference 38

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This paper cites Neural Message Passing for Quantum Chemistry.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Neural Message Passing for Quantum Chemistry

Reference 39

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This paper cites Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs

Reference 40

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This paper cites How Powerful are Graph Neural Networks?.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization How Powerful are Graph Neural Networks?

Reference 41

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Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Graph Attention Networks

Reference 42

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Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Strategies for Pre-training Graph Neural Networks

Reference 43

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This paper cites Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau Vs OneCycleLR.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau Vs OneCycleLR

Reference 44

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This paper cites Explaining compound activity predictions with a substructure-aware loss for graph neural networks.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Explaining compound activity predictions with a substructure-aware loss for graph neural networks

Reference 45

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This paper cites Group lasso regularized deep learning for cancer prognosis from multi- omics and clinical features.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Group lasso regularized deep learning for cancer prognosis from multi- omics and clinical features

Reference 46

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This paper cites Evaluating attribution for graph neural networks.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Evaluating attribution for graph neural networks

Reference 47

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This paper cites Learning Deep Features for Discriminative Localization.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Learning Deep Features for Discriminative Localization

Reference 48

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This paper cites Axiomatic Attribution for Deep Networks.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Axiomatic Attribution for Deep Networks

Reference 49

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This paper cites A self-conformation-aware pre- training framework for molecular property prediction with substructure interpretability.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization A self-conformation-aware pre- training framework for molecular property prediction with substructure interpretability

Reference 50

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This paper cites ACES-GNN: Can graph neural network learn to explain activity cliffs? Digit Discov.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization ACES-GNN: Can graph neural network learn to explain activity cliffs? Digit Discov

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:09.493486Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 231541e7-2a49-4078-b7fc-0b89421e852e · outbound

This paper cites A semi-supervised molecular learning framework for activity cliff estimation.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization A semi-supervised molecular learning framework for activity cliff estimation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:09.316712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 80e390ff-f181-46fa-b7f5-39ddc85ea320 · outbound

This paper cites ACGCN: Graph convolutional networks for activity cliff prediction between matched molecular pairs.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization ACGCN: Graph convolutional networks for activity cliff prediction between matched molecular pairs

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:09.172814Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e030d55e-12c1-4982-9bcb-2e0d997101b5 · outbound

This paper cites ACtriplet: An improved deep learning model for activity cliffs prediction by integrating triplet loss and pre-training.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization ACtriplet: An improved deep learning model for activity cliffs prediction by integrating triplet loss and pre-training

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:08.972426Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 26fc733b-fca3-49fe-a46b-9f4f95ccac0e · outbound

This paper cites Practically significant method comparison protocols for machine learning in small molecule drug discovery.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Practically significant method comparison protocols for machine learning in small molecule drug discovery

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:08.834864Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2467aa00-06a1-42a2-9b81-68084bc20905 · outbound

This paper cites Individual comparisons by ranking methods.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Individual comparisons by ranking methods

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:08.690349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2bb3249b-dbe3-4ac6-b96b-cffe1325707f · outbound

This paper cites Correlation coefficients: Appropriate use and interpretation.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Correlation coefficients: Appropriate use and interpretation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:08.488670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1150c25d-7683-42cc-a38c-b9707d875ecb · outbound

This paper cites SDDSynergy: Learning important molecular substructures for explainable anticancer drug synergy prediction.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization SDDSynergy: Learning important molecular substructures for explainable anticancer drug synergy prediction

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:08.327451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9b8cb531-7860-4376-88de-9bc8a20d0d99 · outbound

This paper cites MGTNSyn: Molecular structure aware graph transformer network with relational attention for drug synergy prediction.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization MGTNSyn: Molecular structure aware graph transformer network with relational attention for drug synergy prediction

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:08.150218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0274c289-e72d-459d-8bbb-5f57bf65c46b · outbound

This paper cites Key substructure learning with chemical intuition for material property prediction.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Key substructure learning with chemical intuition for material property prediction

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:07.985600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dff8483d-6c90-4786-9edf-0cca69ab7d84 · outbound

This paper cites KSGTN-DDI: Key substructure- aware graph transformer network for drug-drug interaction prediction.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization KSGTN-DDI: Key substructure- aware graph transformer network for drug-drug interaction prediction

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:07.816387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e9757aa4-5ad4-4dc9-9e44-d7bfaba9ad8e · outbound

This paper cites Representational Alignment with Chemical Induced Fit for Molecular Relational Learning.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Representational Alignment with Chemical Induced Fit for Molecular Relational Learning

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:22:06.116886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 05cb7be2-6058-42e7-b109-c7cb7d4f62af · outbound

This paper cites Subgraph information bottleneck with causal dependency for stable molecular relational learning.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Subgraph information bottleneck with causal dependency for stable molecular relational learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:07.646431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2eb14027-7c73-42e2-8751-02e6b59fdef9 · outbound

This paper cites Matched molecular pair analysis in short: Algorithms, applications and limitations.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Matched molecular pair analysis in short: Algorithms, applications and limitations

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:07.504299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 71de7dac-2b1d-4ca5-874d-ca66ee3e3f50 · outbound

This paper cites A maximum common substructure- based algorithm for searching and predicting drug-like compounds.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization A maximum common substructure- based algorithm for searching and predicting drug-like compounds

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:07.350119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 167a2923-46cd-4095-b59c-e6552492a83c · outbound

This paper cites Macrocycles in drug discovery-learning from the past for the future.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Macrocycles in drug discovery-learning from the past for the future

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:07.164353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 03acb0b9-869e-4c9c-a1a8-4d3435a1403b · outbound

This paper cites Targeting Src family kinases in anti-cancer therapies: Turning promise into triumph.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Targeting Src family kinases in anti-cancer therapies: Turning promise into triumph

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:07.017306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 48523115-bd42-4d9a-ac15-737323369bcd · outbound

This paper cites c-Abl in neurodegenerative disease.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization c-Abl in neurodegenerative disease

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:06.827566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8a253cab-8dcf-4467-8f94-a4154bc3a6e3 · outbound

This paper cites TEC family kinases in health and disease—Loss- of-function of BTK and ITK and the gain-of-function fusions ITK–SYK and BTK–SYK.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization TEC family kinases in health and disease—Loss- of-function of BTK and ITK and the gain-of-function fusions ITK–SYK and BTK–SYK

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:22:06.675478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 77ca01e1-2103-4cfd-aa24-d58a6e6809ea · outbound

This paper cites Aberrant role of ALK in tau proteinopathy through autophagosomal dysregulation.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Aberrant role of ALK in tau proteinopathy through autophagosomal dysregulation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:05.917865Z

Source-reported events for the cited work

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Observation d0817d12-450e-4c55-8251-e4d2fc251488 · outbound

This paper cites Learning Important Features Through Propagating Activation Differences.

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization Learning Important Features Through Propagating Activation Differences

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:02.354648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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