Paper Citation Record · LEDGER

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows

As of 21 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2412.02889.

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

pith.paper-citation-record.v1
2412.02889 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:04:07.509997Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T16:06:25.893394Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:20:59.682652Z

Reference resolution

26 of 26 outbound references displayed

verified exact1
verified fuzzy16
unresolved8
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ba4d207e-cd2d-4c45-8413-135af4134673 · outbound

This paper cites DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T23:04:07.367820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f204bf1d-4732-44c8-b28e-f6df97eac6eb · outbound

This paper cites PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:04:07.591094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 35010f1f-f7cf-4352-baf9-29a6b7f2bd0f · outbound

This paper cites Knowledge-guided docking: Accurate prospective prediction of bound config- urations of novel ligands using Surflex-Dock.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Knowledge-guided docking: Accurate prospective prediction of bound config- urations of novel ligands using Surflex-Dock

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:08.119042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c69ff8f8-feba-4f33-9a52-10ee80ffa21a · outbound

This paper cites Surflex-Dock: Docking benchmarks and real-world application.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Surflex-Dock: Docking benchmarks and real-world application

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:08.105991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9f94abb8-da24-47bc-83c8-09d486b2452b · outbound

This paper cites an unresolved cited work.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:04:08.090553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 58885db7-1d6e-460e-8f1b-7a470b06606c · outbound

This paper cites an unresolved cited work.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:04:08.073325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fe701bb3-9eca-4ddf-a05f-676e94b4357c · outbound

This paper cites Effects of protein conformation in docking: Improved pose prediction through protein pocket adaptation.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Effects of protein conformation in docking: Improved pose prediction through protein pocket adaptation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:08.049497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d35c3fc0-6223-4cc2-af07-e50b31fa0454 · outbound

This paper cites an unresolved cited work.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:04:08.029431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 756a85b4-90ba-4196-b302-cc349d87e33a · outbound

This paper cites Glide: A new approach for rapid, accurate docking and scoring.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Glide: A new approach for rapid, accurate docking and scoring

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:08.008546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2a85311b-79c4-433b-8866-5adbea96e899 · outbound

This paper cites Autodock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Autodock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:07.993688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f9e67998-cf3f-44d0-9932-07c91e834829 · outbound

This paper cites Autodock vina 1.2.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Autodock vina 1.2

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:07.971829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 36685741-f989-489a-9e0b-2a8517d48d44 · outbound

This paper cites Gnina 1.0: Molecular docking with deep learning.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Gnina 1.0: Molecular docking with deep learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:07.957805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 848defb0-5181-4c94-9fa2-0dc40ab4b254 · outbound

This paper cites Cleves, Rocco Varela, and Ajay N.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Cleves, Rocco Varela, and Ajay N

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:07.943429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 46750aa4-8d5a-4f2f-b7e7-3f1889526173 · outbound

This paper cites Cleves, Rocco Varela, and Ajay N.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Cleves, Rocco Varela, and Ajay N

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:07.926913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 28f9adf0-f0d0-42ca-9076-a9471df08898 · outbound

This paper cites Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:07.908028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation faee8611-7b48-41fb-a7b7-1a911ccaf035 · outbound

This paper cites ForceGen 3D structure and conformer generation: From small lead-like molecules to macrocyclic drugs.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows ForceGen 3D structure and conformer generation: From small lead-like molecules to macrocyclic drugs

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:07.886352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 159db641-7fdd-4a10-bf7a-6a11ebe55e00 · outbound

This paper cites Complex macrocycle exploration: Parallel, heuristic, and constraint-based conformer generation using forcegen.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Complex macrocycle exploration: Parallel, heuristic, and constraint-based conformer generation using forcegen

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:07.853702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c4537245-33c1-4348-a165-2be53cfd5f76 · outbound

This paper cites Structure-and ligand-based virtual screening on DUD-E+: Performance dependence on approximations to the binding pocket.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Structure-and ligand-based virtual screening on DUD-E+: Performance dependence on approximations to the binding pocket

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:07.833862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fce67975-d253-4cda-bf93-30a2cc516e96 · outbound

This paper cites Electrostatic-field and surface-shape similarity for virtual screening and pose prediction.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Electrostatic-field and surface-shape similarity for virtual screening and pose prediction

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:07.796622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5c97e479-63c2-454f-8c33-5c24f0fbd452 · outbound

This paper cites ANI-1: An extensible neural network potential with dft accuracy at force field computational cost.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows ANI-1: An extensible neural network potential with dft accuracy at force field computational cost

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:07.774746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 346be09c-de58-48bc-bd27-72d609dcf263 · outbound

This paper cites Deep Confident Steps to New Pockets: Strategies for Docking Generalization.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Deep Confident Steps to New Pockets: Strategies for Docking Generalization

Reference 21

Resolution: malformed identifier; no resolver link, observed 2026-08-11T23:04:07.485899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fb33261-ee04-47f5-bf6a-251e12e22822 · outbound

This paper cites an unresolved cited work.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:04:07.757474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 84b337f6-ccad-42c3-9efc-1a4835576061 · outbound

This paper cites an unresolved cited work.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:04:07.730581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ec8032f2-831e-4a74-86ab-73d215f66d2a · outbound

This paper cites an unresolved cited work.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:04:07.711646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation adedbaf2-3133-48c8-9f43-1643918448a8 · outbound

This paper cites an unresolved cited work.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:04:07.691699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e52672a8-935f-4050-bb2c-2010d89cc004 · outbound

This paper cites $ S C H R O D I N G E R / run / opt / schrodinger2022 -3/ mmshare - v5 .9/ python / scripts / p r e p w i z a r d 2 _ d r i v e r . py.

Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows $ S C H R O D I N G E R / run / opt / schrodinger2022 -3/ mmshare - v5 .9/ python / scripts / p r e p w i z a r d 2 _ d r i v e r . py

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:04:07.635064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation d0807539-bac6-43a5-b01b-2d32b3e1732f · inbound

Benchmarking Single-Pose Docking, Consensus Rescoring, and Supervised ML on the LIT-PCBA Library: A Critical Evaluation of DiffDock, AutoDock-GPU, GNINA, and DiffDock-NMDN cites this paper.

Benchmarking Single-Pose Docking, Consensus Rescoring, and Supervised ML on the LIT-PCBA Library: A Critical Evaluation of DiffDock, AutoDock-GPU, GNINA, and DiffDock-NMDN Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:20:59.684847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.