Paper Citation Record · LEDGER

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models

As of 20 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2607.19237.

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

pith.paper-citation-record.v1
2607.19237 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T13:06:16.527258Z

measured 45 of 45 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 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

45 of 45 outbound references displayed

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unresolved45
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 01f98349-c5f1-41a0-9abe-ac222fac8a10 · outbound

This paper cites Pharmacogenomics of gpcr drug targets.Cell, 172(1):41–54, 2018.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Pharmacogenomics of gpcr drug targets.Cell, 172(1):41–54, 2018

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:12.794244Z

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Observation 0ddab567-97e8-4fa3-afb7-2a39c1dde4d7 · outbound

This paper cites Structure-based drug design with equiv- ariant diffusion models.Nature Computational Science, 4(12):899–909, 2024.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Structure-based drug design with equiv- ariant diffusion models.Nature Computational Science, 4(12):899–909, 2024

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:12.860073Z

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Observation 7ce0d682-c55d-492f-ae9f-f118c3bf5c07 · outbound

This paper cites FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:12.935245Z

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Observation 059e37c5-8e7f-458c-b1f6-6ce37be02168 · outbound

This paper cites Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.023521Z

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Observation e9184c3a-d816-401c-a123-f2bafad2b2bd · outbound

This paper cites an unresolved cited work.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.082319Z

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Observation 705d422c-9090-4f6f-a32d-502d45107bd8 · outbound

This paper cites an unresolved cited work.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.165616Z

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Observation 41b4acce-77c1-4a24-8ee8-eb4108363e01 · outbound

This paper cites Generative Artificial Intelligence for Navigating Synthesizable Chemical Space.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Generative Artificial Intelligence for Navigating Synthesizable Chemical Space

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.242909Z

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Observation 1c54b5e1-3788-4b84-b0bc-b7478df9e047 · outbound

This paper cites Compositional flows for 3d molecule and synthesis pathway co-design.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Compositional flows for 3d molecule and synthesis pathway co-design

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.332399Z

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Observation 0a74c4d0-b646-4590-b95b-d06dadd26cc3 · outbound

This paper cites Syncogen: Synthe- sizable 3d molecule generation via joint reaction and coordinate modeling.arXiv preprint arXiv:2507.11818, 2025.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Syncogen: Synthe- sizable 3d molecule generation via joint reaction and coordinate modeling.arXiv preprint arXiv:2507.11818, 2025

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.420051Z

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Observation 56abd5f5-4d55-4a2c-91ba-ade67ec9eace · outbound

This paper cites Accurate structure prediction of biomolecular interactions with alphafold 3.Nature, pages 1–3, 2024.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Accurate structure prediction of biomolecular interactions with alphafold 3.Nature, pages 1–3, 2024

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.482638Z

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Observation 0029c3c0-af9a-474a-9b9c-42a40b444e81 · outbound

This paper cites Boltz-2: Towards accurate and efficient binding affinity prediction.BioRxiv, pages 2025–06, 2025.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Boltz-2: Towards accurate and efficient binding affinity prediction.BioRxiv, pages 2025–06, 2025

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.563791Z

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Observation ef402664-ae8e-4732-bfdc-431d599143f2 · outbound

This paper cites Defog: Discrete flow matching for graph generation.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Defog: Discrete flow matching for graph generation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.623645Z

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Observation 2e843bfd-365c-4c7f-aecc-1f3bd1eef4eb · outbound

This paper cites Lit-pcba: An unbiased data set for machine learning and virtual screening.Journal of Chemical Information and Modeling, 60(9):4263–4273, 2020.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Lit-pcba: An unbiased data set for machine learning and virtual screening.Journal of Chemical Information and Modeling, 60(9):4263–4273, 2020

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.662848Z

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Observation db2e7dca-2ffa-4fff-9fa8-1ec6441370f8 · outbound

This paper cites Hirzel, Ryan P.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Hirzel, Ryan P

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.720640Z

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Observation d385c996-1641-4eb2-b88c-b29a2669b5e3 · outbound

This paper cites an unresolved cited work.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.804975Z

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Observation bd2d9a16-b3db-41f2-b6d6-22bf9e69b537 · outbound

This paper cites Kusner, Brooks Paige, and Jos ´e Miguel Hern ´andez-Lobato.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Kusner, Brooks Paige, and Jos ´e Miguel Hern ´andez-Lobato

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.875377Z

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Observation a1b1e4eb-4c79-4b92-9c67-444f809f693d · outbound

This paper cites Constrained graph variational autoencoders for molecule design.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Constrained graph variational autoencoders for molecule design

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.911567Z

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Observation f90b5c95-d3e4-42b7-b997-3a8a93565023 · outbound

This paper cites Junction tree variational autoencoder for molecular graph generation, 2019.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Junction tree variational autoencoder for molecular graph generation, 2019

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:13.961706Z

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Observation e0b1df86-ebf7-4ddf-a397-d65794676e0c · outbound

This paper cites Moflow: an invertible flow model for generating molecular graphs.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Moflow: an invertible flow model for generating molecular graphs

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:14.001681Z

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Observation 09850d67-5489-4b80-a809-8907357f3ceb · outbound

This paper cites Digress: Discrete denoising diffusion for graph generation.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Digress: Discrete denoising diffusion for graph generation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:14.053738Z

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Observation 949b70de-3666-4d89-b52b-2f403b6f42a9 · outbound

This paper cites Equivariant diffusion for molecule generation in 3d.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Equivariant diffusion for molecule generation in 3d

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:14.115717Z

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Observation d48ea637-b6ed-4b02-a942-3a68b556ab31 · outbound

This paper cites Midi: Mixed graph and 3d denoising diffusion for molecule generation.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Midi: Mixed graph and 3d denoising diffusion for molecule generation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:14.243879Z

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Observation 2452f4c6-20ee-4725-b301-23ba3b822e53 · outbound

This paper cites Semlaflow–efficient 3d molecular generation with latent attention and equivariant flow matching.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Semlaflow–efficient 3d molecular generation with latent attention and equivariant flow matching

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:14.352028Z

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Observation 792ccd71-9a64-4b17-b1ff-d7bef36d8a6e · outbound

This paper cites Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, and Zachary W.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, and Zachary W

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:14.423951Z

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Observation 4d305959-2a25-4637-9cc3-3597c91a9369 · outbound

This paper cites Tabasco: A fast, simplified model for molecular generation with improved physical quality, 2025.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Tabasco: A fast, simplified model for molecular generation with improved physical quality, 2025

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:14.513816Z

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Observation ca2b81e2-b85f-42e1-bc3a-0c64cb944899 · outbound

This paper cites Synflownet: Design of diverse and novel molecules with synthesis constraints.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Synflownet: Design of diverse and novel molecules with synthesis constraints

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:14.614683Z

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Observation 8c1b9ac2-101b-43f3-8a09-5ad9caea67e0 · outbound

This paper cites RGFN: Synthesizable molec- ular generation using GFlowNets.Advances in Neural Information Processing Systems, 37:46908–46955, 2024.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models RGFN: Synthesizable molec- ular generation using GFlowNets.Advances in Neural Information Processing Systems, 37:46908–46955, 2024

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:14.664889Z

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Observation a070ed44-43a3-4def-93da-ffdf4c7f6981 · outbound

This paper cites All-atom inverse protein folding through discrete flow matching.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models All-atom inverse protein folding through discrete flow matching

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:14.791140Z

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Observation 87925d08-4142-466a-b51c-cf47b7deb5aa · outbound

This paper cites Protein hunter: ex- ploiting structure hallucination within diffusion for protein design.bioRxiv, pages 2025–10, 2025.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Protein hunter: ex- ploiting structure hallucination within diffusion for protein design.bioRxiv, pages 2025–10, 2025

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:14.888171Z

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Observation 3c2ad94b-5bd2-4988-8aa1-84cc790ed0ee · outbound

This paper cites One-shot design of functional protein binders with bindcraft.Nature, 646(8084):483–492, 2025.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models One-shot design of functional protein binders with bindcraft.Nature, 646(8084):483–492, 2025

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:14.966393Z

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Observation 44be912f-44a2-4699-a55c-2b7e246dd4b8 · outbound

This paper cites Pxdesign: Fast, modular, and accurate de novo design of protein binders.bioRxiv, pages 2025–08, 2025.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Pxdesign: Fast, modular, and accurate de novo design of protein binders.bioRxiv, pages 2025–08, 2025

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:15.080152Z

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Observation eec7bffb-9c5a-4b78-b95a-9dc9acde18d5 · outbound

This paper cites Boltzdesign1: Inverting all-atom structure prediction model for generalized biomolecular binder design.bioRxiv, pages 2025–04, 2025.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Boltzdesign1: Inverting all-atom structure prediction model for generalized biomolecular binder design.bioRxiv, pages 2025–04, 2025

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:15.210172Z

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Observation bf75357b-cc90-4794-a665-48d3a35db3fe · outbound

This paper cites Aizynthfinder: a fast, robust and flexible open-source software for ret- rosynthetic planning.Journal of Cheminformatics, 2020.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Aizynthfinder: a fast, robust and flexible open-source software for ret- rosynthetic planning.Journal of Cheminformatics, 2020

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:15.332116Z

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Observation 2cb2ad65-8d97-4307-b068-521464bd080e · outbound

This paper cites an unresolved cited work.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Unresolved cited work

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:15.441134Z

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Observation 7788088a-f51f-4494-8a02-4b4f0dd7eda9 · outbound

This paper cites Cd47 as a promising therapeutic target in oncology.Frontiers in Immunol- ogy, 2022.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Cd47 as a promising therapeutic target in oncology.Frontiers in Immunol- ogy, 2022

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:15.566312Z

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Observation 17d957b2-150a-4ba8-bc74-b10a32120fe1 · outbound

This paper cites Have protein-ligand co-folding methods moved beyond memorisation?BioRxiv, pages 2025–02, 2025.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Have protein-ligand co-folding methods moved beyond memorisation?BioRxiv, pages 2025–02, 2025

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:15.684162Z

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Observation cbccf327-8c96-4827-b31c-fab53ccba96a · outbound

This paper cites Openbind: Open protein–ligand binding data.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Openbind: Open protein–ligand binding data

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:15.840514Z

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Observation e11883e5-5131-4041-9909-ab713a5fab5a · outbound

This paper cites Gen- erative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Gen- erative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:15.946963Z

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Observation e694e623-5bd8-427d-8b84-3d34398005aa · outbound

This paper cites Sparse training of discrete diffusion models for graph generation.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Sparse training of discrete diffusion models for graph generation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:16.015336Z

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Observation d8de9485-2ffb-47ab-97f3-b100d25baa77 · outbound

This paper cites How powerful are graph neural networks? InInternational Conference on Learning Representations (ICLR), 2019.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models How powerful are graph neural networks? InInternational Conference on Learning Representations (ICLR), 2019

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:16.073934Z

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Observation a2602dfb-07b8-4811-8c48-39fd074de480 · outbound

This paper cites On structural expressive power of graph transformers.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models On structural expressive power of graph transformers

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:16.135838Z

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Observation 53987853-16aa-487f-9634-9b3d36109240 · outbound

This paper cites Graph inductive biases in transformers without message pass- ing.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Graph inductive biases in transformers without message pass- ing

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:16.180272Z

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Observation a06772a0-0dbc-4570-b663-fa41b542f8cf · outbound

This paper cites Cometh: A continuous- time discrete-state graph diffusion model.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Cometh: A continuous- time discrete-state graph diffusion model

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:16.312613Z

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Observation d54cdf5f-acc2-4e8f-a462-f16660119887 · outbound

This paper cites Zinc 15–ligand discovery for everyone.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Zinc 15–ligand discovery for everyone

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:16.423925Z

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Observation e3d7a532-7819-4f5d-82db-0a4298ec0833 · outbound

This paper cites ref element.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models ref element

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-01T13:06:16.527258Z

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Pith citing papers

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