Paper Citation Record · LEDGER

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion

As of 14 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2412.19589.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.19589 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:14:04.312457Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact0
verified fuzzy32
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bb1bdaf0-165f-4236-af42-856e16b1c414 · outbound

This paper cites Overview of the immune response[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Overview of the immune response[J]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:06.035449Z

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 729d5be9-1315-4908-8090-c53ab9a96250 · outbound

This paper cites Prediction of Drug-Target B inding Afﬁnity Based on Deep Learning Models[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Prediction of Drug-Target B inding Afﬁnity Based on Deep Learning Models[J]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:06.004501Z

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 09fe4050-d448-4098-b2f2-7fd08d10ca62 · outbound

This paper cites STA Ts and gene regulation[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion STA Ts and gene regulation[J]

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.942069Z

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 b24c40ca-d8e3-4986-a6b3-7c604fd71251 · outbound

This paper cites The nature of statistical learning theory[M].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion The nature of statistical learning theory[M]

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.895817Z

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 70d17aca-f335-44c5-b70b-810624f34256 · outbound

This paper cites The recent progress in proteoch emometric modelling: focusing on target descriptors, cross-term des criptors and application scope[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion The recent progress in proteoch emometric modelling: focusing on target descriptors, cross-term des criptors and application scope[J]

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.858622Z

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 bd59bed4-9b73-459a-b17a-f4b690986e94 · outbound

This paper cites MSGNN-DTA: Multi-Scale Top ological Feature Fusion Based on Graph Neural Networks for Drug–Targ et Bind- ing Afﬁnity Prediction[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion MSGNN-DTA: Multi-Scale Top ological Feature Fusion Based on Graph Neural Networks for Drug–Targ et Bind- ing Afﬁnity Prediction[J]

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.825334Z

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 70cea100-66c8-4839-af99-405bc6149af6 · outbound

This paper cites DataDTA: a multi-feature and du al- interaction aggregation framework for drug–target bindin g afﬁnity pre- diction[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion DataDTA: a multi-feature and du al- interaction aggregation framework for drug–target bindin g afﬁnity pre- diction[J]

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.769101Z

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 55a5a1fd-4c83-4c06-8111-743f8c53a43d · outbound

This paper cites Predicting drug–target bin ding afﬁnity with cross-scale graph contrastive learning[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Predicting drug–target bin ding afﬁnity with cross-scale graph contrastive learning[J]

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.694230Z

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 9f03e425-ec29-4ab8-a0fe-583c0c2f5cd3 · outbound

This paper cites DeepDTA: deep drug–target binding afﬁnity prediction[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion DeepDTA: deep drug–target binding afﬁnity prediction[J]

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.616482Z

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 4aa2a12d-674e-4a7e-85a3-f0ca2df53c94 · outbound

This paper cites AttentionDTA: Drug–target bind- ing afﬁnity prediction by sequence-based deep learning wit h attention mechanism[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion AttentionDTA: Drug–target bind- ing afﬁnity prediction by sequence-based deep learning wit h attention mechanism[J]

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.578938Z

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 ce0a8879-ba6d-4094-b073-bbd34610b12c · outbound

This paper cites TEFDTA: a transformer encoder a nd ﬁnger- print representation combined prediction method for bonde d and non- bonded drug–target afﬁnities[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion TEFDTA: a transformer encoder a nd ﬁnger- print representation combined prediction method for bonde d and non- bonded drug–target afﬁnities[J]

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.527693Z

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 81f7cb51-1cb1-4ab7-a034-9b301760de59 · outbound

This paper cites GraphDTA: predicting dru g–target binding afﬁnity with graph neural networks[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion GraphDTA: predicting dru g–target binding afﬁnity with graph neural networks[J]

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.476436Z

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 30edce62-075b-47fa-a1c9-7a00d4de7cf3 · outbound

This paper cites GSAML-DTA: an interpretable drug- target binding afﬁnity prediction model based on graph neur al networks with self-attention mechanism and mutual information[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion GSAML-DTA: an interpretable drug- target binding afﬁnity prediction model based on graph neur al networks with self-attention mechanism and mutual information[J]

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.416693Z

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 23cde4c8-3514-4e8a-b1c2-b7ddb47b2e48 · outbound

This paper cites Gefa: early fusion appr oach in drug-target afﬁnity prediction[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Gefa: early fusion appr oach in drug-target afﬁnity prediction[J]

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.363270Z

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 cfddfe42-5312-4db8-8d07-fcb9bca7350a · outbound

This paper cites MGraphDTA: deep multiscal e graph neural network for explainable drug–target binding afﬁnit y prediction[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion MGraphDTA: deep multiscal e graph neural network for explainable drug–target binding afﬁnit y prediction[J]

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.330106Z

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 84d3b838-58c0-465b-8a13-ae1cf1aae404 · outbound

This paper cites SGNet: Sequence-based Con volution and Ligand Graph Network for Protein Binding Afﬁnity Predic tion[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion SGNet: Sequence-based Con volution and Ligand Graph Network for Protein Binding Afﬁnity Predic tion[J]

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.294065Z

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 a1802a90-0e27-4730-be1f-db728b273a13 · outbound

This paper cites ColdDTA: utilizing data augm entation and attention-based feature fusion for drug-target bindin g afﬁnity pre- diction[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion ColdDTA: utilizing data augm entation and attention-based feature fusion for drug-target bindin g afﬁnity pre- diction[J]

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.254267Z

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 1780c034-9d2b-4634-bbda-77017df8eeb3 · outbound

This paper cites Comprehensive ana lysis of kinase inhibitor selectivity[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Comprehensive ana lysis of kinase inhibitor selectivity[J]

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.186063Z

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 7137115d-cb77-4150-a5c0-680e9cecd3ef · outbound

This paper cites Navigating the kinome [J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Navigating the kinome [J]

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.122120Z

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 e8b0bf94-3691-42ae-b8e8-91bb68da82bf · outbound

This paper cites Making sense of lar ge-scale kinase inhibitor bioactivity data sets: A comparative and i ntegrative analysis[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Making sense of lar ge-scale kinase inhibitor bioactivity data sets: A comparative and i ntegrative analysis[J]

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:05.049522Z

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 cd214dbe-5949-4eff-966c-fa1e78f370b8 · outbound

This paper cites AttentionMGT-DTA: A multi-mo dal drug-target afﬁnity prediction using graph transformer an d attention mechanism[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion AttentionMGT-DTA: A multi-mo dal drug-target afﬁnity prediction using graph transformer an d attention mechanism[J]

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:04.991409Z

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 558eb3e4-239c-4d73-b425-97bc62268ba1 · outbound

This paper cites A Generalization of Transformer Networks to Graphs.

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion A Generalization of Transformer Networks to Graphs

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T00:14:04.136636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b878354-7c81-4ed0-80ac-d9d3ef58cc37 · outbound

This paper cites Neural message passing for Quantum chemistry[C].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Neural message passing for Quantum chemistry[C]

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:04.954444Z

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 c59e50ed-366e-450b-bbac-7be41cbf3ba0 · outbound

This paper cites Compound–protein interacti on prediction with end-to-end learning of neural networks for graphs and s equences[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Compound–protein interacti on prediction with end-to-end learning of neural networks for graphs and s equences[J]

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:04.916625Z

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 53d50506-3922-493e-b0cd-582a225a13eb · outbound

This paper cites Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network.

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T00:14:04.184954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d471502-b5a0-49ac-b5f6-fee6b446fe44 · outbound

This paper cites Multivariable prognosti c models: issues in developing models, evaluating assumptions and ad equacy, and measuring and reducing errors[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Multivariable prognosti c models: issues in developing models, evaluating assumptions and ad equacy, and measuring and reducing errors[J]

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:04.881946Z

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 a2a25c65-554d-4b26-9837-08662ace205b · outbound

This paper cites Some case studies on application of “rm2” metrics for judging quality of quantitative struct ure–activity relationship predictions: emphasis on scaling of response data[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Some case studies on application of “rm2” metrics for judging quality of quantitative struct ure–activity relationship predictions: emphasis on scaling of response data[J]

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:04.844413Z

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 cfa2747f-ed64-4328-b731-20b0e91d8346 · outbound

This paper cites Kullback, R.A.

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Kullback, R.A

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:04.806271Z

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 9fa5bf6d-f74c-4b33-80f2-63d28363cc36 · outbound

This paper cites Gene Ontology aided compound p rotein binding afﬁnity prediction using BERT encoding[C].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion Gene Ontology aided compound p rotein binding afﬁnity prediction using BERT encoding[C]

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:04.771529Z

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 0a7de587-3b40-4cbe-9ce7-653a1a778ebe · outbound

This paper cites rzMLP-DTA: gMLP network with ReZero for sequence-based drug-target afﬁnity prediction.

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion rzMLP-DTA: gMLP network with ReZero for sequence-based drug-target afﬁnity prediction

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:04.711994Z

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 6dea0727-a8f5-4c57-83de-510fa128adbd · outbound

This paper cites TF-DTA: A Deep Learning Approach Usi ng Transformer Encoder to Predict Drug-Target Binding Afﬁnit y[C].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion TF-DTA: A Deep Learning Approach Usi ng Transformer Encoder to Predict Drug-Target Binding Afﬁnit y[C]

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:04.674114Z

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 c575e525-2533-4e01-be00-c7bd0f04a12f · outbound

This paper cites DGDTA: dynamic graph attentio n network for predicting drug–target binding afﬁnity[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion DGDTA: dynamic graph attentio n network for predicting drug–target binding afﬁnity[J]

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:04.622503Z

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 e7dbe4df-404d-4ce3-8586-fb95a5104540 · outbound

This paper cites TransV AE-DTA: Transformer an d varia- tional autoencoder network for drug-target binding afﬁnit y prediction[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion TransV AE-DTA: Transformer an d varia- tional autoencoder network for drug-target binding afﬁnit y prediction[J]

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:04.570094Z

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 79596c9f-d821-4506-be0b-9ede04d31006 · outbound

This paper cites ArkDTA: attention regulariz ation guided by non-covalent interactions for explainable drug–target binding afﬁnity prediction[J].

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion ArkDTA: attention regulariz ation guided by non-covalent interactions for explainable drug–target binding afﬁnity prediction[J]

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:14:04.526470Z

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.

Pith citing papers

No inbound Pith citation observations are available.