Paper Citation Record · LEDGER

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning

As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2504.16261.

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

pith.paper-citation-record.v1
2504.16261 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation a15aa354-077f-4ba2-b048-1e416b26b969 · outbound

This paper cites Innovation in the pharmaceutical industry: new estimates of r&d costs,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Innovation in the pharmaceutical industry: new estimates of r&d costs,

Reference 1

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Observation 3ac5d82f-fe54-4801-a5f5-d2dc3dfb2dd4 · outbound

This paper cites Principles of early drug discovery,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Principles of early drug discovery,

Reference 2

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Observation 78586bd1-01e6-4398-94c9-5aa3e7ece5ec · outbound

This paper cites Empirical scoring func- tions for structure-based virtual screening: applications, critical aspects, and challenges,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Empirical scoring func- tions for structure-based virtual screening: applications, critical aspects, and challenges,

Reference 3

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Observation 3eec680c-5f15-404f-8577-df44db3c25a3 · outbound

This paper cites Insights into protein–ligand interactions: mechanisms, models, and methods,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Insights into protein–ligand interactions: mechanisms, models, and methods,

Reference 4

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Observation 4793e6cc-5373-498b-b4bf-94e4ba40dffa · outbound

This paper cites Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design,

Reference 5

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Observation e833f876-56a8-491d-9087-e52366a41599 · outbound

This paper cites Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:12:54.170185Z

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Observation 412e2c62-16b8-419d-a072-779efbd65916 · outbound

This paper cites Deepdta: deep drug–target binding affinity prediction,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Deepdta: deep drug–target binding affinity prediction,

Reference 7

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Observation aa6606a2-2fcf-420d-87e4-0b0f855c581c · outbound

This paper cites Graphdta: predicting drug–target binding affinity with graph neural networks,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Graphdta: predicting drug–target binding affinity with graph neural networks,

Reference 8

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This paper cites Deep- purpose: a deep learning library for drug–target interaction prediction,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Deep- purpose: a deep learning library for drug–target interaction prediction,

Reference 9

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Observation 0a18c909-7588-46dc-84c9-b56c6aa84f3d · outbound

This paper cites Multi-scale representation learning on proteins,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Multi-scale representation learning on proteins,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:12:54.125072Z

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Observation ad119d54-7516-413c-a955-367098b2d68a · outbound

This paper cites Development and evaluation of a deep learning model for protein–ligand binding affinity prediction,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Development and evaluation of a deep learning model for protein–ligand binding affinity prediction,

Reference 11

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Observation 636076f9-52bb-4926-8517-6db9c122cc27 · outbound

This paper cites K deep: protein–ligand absolute binding affinity prediction via 3d-convolutional neural networks,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning K deep: protein–ligand absolute binding affinity prediction via 3d-convolutional neural networks,

Reference 12

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Observation 767fe152-0fa8-4304-b810-0d54418ebe0a · outbound

This paper cites Ss- gnn: a simple-structured graph neural network for affinity prediction,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Ss- gnn: a simple-structured graph neural network for affinity prediction,

Reference 13

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Observation dd4e2de6-1284-4540-971f-6613a190379b · outbound

This paper cites Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction,

Reference 14

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Observation 191dff3b-884d-4d1e-8066-6ee78e0792d9 · outbound

This paper cites Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+

Reference 15

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This paper cites Physic- ochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Physic- ochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data,

Reference 16

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Observation 40bfbd2f-ec2a-4dd2-822d-16f2e73018fc · outbound

This paper cites The future of machine learning for small-molecule drug discovery will be driven by data,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning The future of machine learning for small-molecule drug discovery will be driven by data,

Reference 17

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This paper cites Artificial intelligence in drug development,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Artificial intelligence in drug development,

Reference 18

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Observation bdf4ccd8-16e6-473c-b1cb-8af225ac2645 · outbound

This paper cites Learning Hierarchical Protein Representations via Complete 3D Graph Networks.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Learning Hierarchical Protein Representations via Complete 3D Graph Networks

Reference 19

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Observation 135eadbd-1b48-477a-9d57-96717ce23320 · outbound

This paper cites ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment

Reference 20

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Observation fb343140-3882-404f-b276-bef223b94e14 · outbound

This paper cites Protein structure generation via folding diffusion,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Protein structure generation via folding diffusion,

Reference 21

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This paper cites Geometric interaction graph neural network for predicting protein–ligand binding affinities from 3d structures (gign),.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Geometric interaction graph neural network for predicting protein–ligand binding affinities from 3d structures (gign),

Reference 22

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Observation f826ac5a-c4c0-4d0e-8c88-1ceb094f7b8d · outbound

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Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Schnet–a deep learning architecture for molecules and materials,

Reference 23

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Observation b9772888-1a49-4a4c-a5fd-24218a1e044c · outbound

This paper cites Extending machine learning beyond interatomic potentials for predicting molecular properties,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Extending machine learning beyond interatomic potentials for predicting molecular properties,

Reference 24

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Observation 4c41f2b1-f7a6-4860-8578-125cd45e62d3 · outbound

This paper cites Mace: Higher order equivariant message passing neural networks for fast and accurate force fields,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Mace: Higher order equivariant message passing neural networks for fast and accurate force fields,

Reference 25

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This paper cites E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials,

Reference 26

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Observation af9670f6-eb80-4fc7-9d6c-0cc4d8e8239e · outbound

This paper cites Frame Averaging for Invariant and Equivariant Network Design.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Frame Averaging for Invariant and Equivariant Network Design

Reference 27

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This paper cites A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

Reference 28

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Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Faenet: Frame averaging equivariant gnn for materials modeling,

Reference 29

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Observation af4ec7f5-f7a6-4ced-891d-8d97da22aa24 · outbound

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Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Balanced mse for imbalanced visual regression,

Reference 30

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Observation dd1abb00-bd16-4cf9-b641-f93af8907e41 · outbound

This paper cites Learning to rank by optimizing ndcg measure,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Learning to rank by optimizing ndcg measure,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:12:53.945291Z

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Observation c5ad3e7b-d445-467a-aaa9-d55001749ab9 · outbound

This paper cites Onionnet-2: a convolutional neural network model for predicting protein-ligand binding affinity based on residue-atom contacting shells,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Onionnet-2: a convolutional neural network model for predicting protein-ligand binding affinity based on residue-atom contacting shells,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:12:53.933181Z

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Observation 8fe033ac-1386-4f4f-b0b6-a3354194d81b · outbound

This paper cites Interaction-based inductive bias in graph neural networks: enhancing protein-ligand binding affinity predictions from 3d structures,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Interaction-based inductive bias in graph neural networks: enhancing protein-ligand binding affinity predictions from 3d structures,

Reference 33

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Observation 6bcce742-01ff-4c1d-ae77-a2c017b88943 · outbound

This paper cites Comparative assessment of scoring functions: the casf-2016 update,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Comparative assessment of scoring functions: the casf-2016 update,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:12:53.911258Z

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Observation 1f957ca7-70e2-41cd-8d9c-86ea08287ac1 · outbound

This paper cites Latent biases in machine learning models for predicting binding affinities using popular data sets,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Latent biases in machine learning models for predicting binding affinities using popular data sets,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:12:53.901380Z

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Observation b4853bfb-a92a-410d-974f-9955cd2bf0cd · outbound

This paper cites ATOM3D: Tasks On Molecules in Three Dimensions.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning ATOM3D: Tasks On Molecules in Three Dimensions

Reference 36

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Observation 8ea2e27d-c713-42aa-93e8-3cde6a01cf78 · outbound

This paper cites Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:12:53.890225Z

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Observation b4fbdd8b-525d-4575-9b32-019dbdd18cf1 · outbound

This paper cites Molecular recognition: lock-and-key, induced fit, and conformational selection,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Molecular recognition: lock-and-key, induced fit, and conformational selection,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:12:53.876628Z

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Observation fba247b4-9167-4e5b-bb75-22c6bdae5e92 · outbound

This paper cites Autodock vina 1.2. 0: New docking methods, expanded force field, and python bindings,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Autodock vina 1.2. 0: New docking methods, expanded force field, and python bindings,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:12:53.865557Z

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Observation c0e042dd-88f1-44ec-a9b1-cd52fe566f34 · outbound

This paper cites Chai-1: Decoding the molecular interactions of life,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Chai-1: Decoding the molecular interactions of life,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:12:53.854429Z

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Observation 381269ec-4cb5-4b61-8920-9ca515f5eef3 · outbound

This paper cites Accurate structure prediction of biomolecular interactions with alphafold 3,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Accurate structure prediction of biomolecular interactions with alphafold 3,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:12:53.843466Z

Source-reported events for the cited work

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Observation 8768b89c-beb7-42da-9abf-c5945d36defc · outbound

This paper cites Plip: fully automated protein–ligand interaction profiler,.

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning Plip: fully automated protein–ligand interaction profiler,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:12:53.831761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

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