Paper Citation Record · LEDGER

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration

As of 7 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2508.21468.

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

pith.paper-citation-record.v1
2508.21468 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:25:31.339421Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T15:56:09.312666Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T21:56:16.227901Z

Reference resolution

57 of 57 outbound references displayed

verified exact2
verified fuzzy38
unresolved16
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a3d9517c-6948-408d-8383-7af5eb2e1754 · outbound

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

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Accurate structure prediction of biomolecular interactions with alphafold 3

Reference 1

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Source-reported events for the cited work

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Observation 4bacf021-a84d-40a9-9ea6-43e7e510f6ef · outbound

This paper cites Protein sequence modelling with bayesian flow networks.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Protein sequence modelling with bayesian flow networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.922485Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3c3e4d77-9c29-4f60-b393-0b2981d5b957 · outbound

This paper cites Geometric deep learning methods and applications in 3d structure-based drug design.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Geometric deep learning methods and applications in 3d structure-based drug design

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.910875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 318f2416-f1ab-4dc7-a27b-fe65ae3fcb43 · outbound

This paper cites Equivariant Energy-Guided SDE for Inverse Molecular Design.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Equivariant Energy-Guided SDE for Inverse Molecular Design

Reference 4

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Source-reported events for the cited work

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Observation b6f900e0-c1be-483d-9404-e370738b467c · outbound

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

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.899307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 98ef91fa-49af-4542-ba35-b0d28b6a0347 · outbound

This paper cites Pid- iff: Physics informed diffusion model for protein pocket-specific 3d molecular generation.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Pid- iff: Physics informed diffusion model for protein pocket-specific 3d molecular generation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.888082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 179abc6c-775e-479d-be29-aaa145361f06 · outbound

This paper cites Diffusion posterior sampling for general noisy inverse problems.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Diffusion posterior sampling for general noisy inverse problems

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.877398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation af2d385f-5358-4730-b16c-36437d9eadde · outbound

This paper cites Uniprot: a worldwide hub of protein knowledge.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Uniprot: a worldwide hub of protein knowledge

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.865764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c0fac689-af22-4d48-a0e8-9f515df84aca · outbound

This paper cites Multi-parameter optimization: identifying high quality compounds with a balance of properties.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Multi-parameter optimization: identifying high quality compounds with a balance of properties

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.855286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0e59f98b-a02c-401a-a85e-691f2e72b2bf · outbound

This paper cites Comprehensive analysis of kinase inhibitor selectivity.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Comprehensive analysis of kinase inhibitor selectivity

Reference 10

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Source-reported events for the cited work

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Observation c7ffd768-1954-40ef-875c-57ba0bdf8751 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Diffusion models beat gans on image synthesis

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T14:25:27.249667Z

Source-reported events for the cited work

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Observation 3ab3b6be-f575-469d-9570-4c93c09096df · outbound

This paper cites Autodock vina 1.2.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Autodock vina 1.2

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.830458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3de22a8b-2936-4993-9244-d8ede7f5e13b · outbound

This paper cites Tweedie’s formula and selection bias.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Tweedie’s formula and selection bias

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T14:25:27.398826Z

Source-reported events for the cited work

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Observation c622c464-ec52-495b-8f13-9ebf3428af75 · outbound

This paper cites Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.812408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bff8ed79-5779-4a2b-ae68-5aabff2e0276 · outbound

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

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Three-dimensional convolutional neural networks and a cross- docked data set for structure-based drug design

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.801989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4c77817e-4aab-46b8-a2e1-7ca8182d21d2 · outbound

This paper cites Reinforced genetic algorithm for structure-based drug design.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Reinforced genetic algorithm for structure-based drug design

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.791132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 46b43fad-9298-4ff8-82d9-b140a20dca0f · outbound

This paper cites Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion

Reference 17

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Source-reported events for the cited work

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Observation 5ca183c5-1d0b-49a6-9a86-257db7286feb · outbound

This paper cites Bayesian Flow Networks.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Bayesian Flow Networks

Reference 18

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Source-reported events for the cited work

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Observation 7e09d8f5-750d-4f95-8920-39a1015a8a24 · outbound

This paper cites Aligning target-aware molecule diffusion models with exact energy optimization.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Aligning target-aware molecule diffusion models with exact energy optimization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.781390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a3d6ecd8-53b9-496b-b6e8-8a0fdb310be9 · outbound

This paper cites 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9feb90b4-d6d5-4e3b-bcb0-1fcb0e07f3b5 · outbound

This paper cites DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T14:25:28.012501Z

Source-reported events for the cited work

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Observation a5feea8d-7175-412c-8958-6aea03dbb2fa · outbound

This paper cites Gradient Guidance for Diffusion Models: An Optimization Perspective.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Gradient Guidance for Diffusion Models: An Optimization Perspective

Reference 22

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Source-reported events for the cited work

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Observation 2060369a-838e-4dec-addc-4ecf849b793b · outbound

This paper cites Training- free multi-objective diffusion model for 3d molecule generation.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Training- free multi-objective diffusion model for 3d molecule generation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.771796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a9931f27-b7e7-4b53-a45f-e4ee13bdd8e7 · outbound

This paper cites Posecheck: Generative models for 3d structure-based drug design produce unrealistic poses.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Posecheck: Generative models for 3d structure-based drug design produce unrealistic poses

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.760442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5057ed33-0ed7-42bc-8f74-c7608534345a · outbound

This paper cites Protein-ligand interaction prior for binding- aware 3d molecule diffusion models.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Protein-ligand interaction prior for binding- aware 3d molecule diffusion models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.749542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f95ea69a-eb56-4cd1-9aad-f8871d8cf413 · outbound

This paper cites Rational approaches to improving selectivity in drug design.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Rational approaches to improving selectivity in drug design

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.739050Z

Source-reported events for the cited work

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Observation dfb58398-b767-4cc3-b3ca-7dd20043e546 · outbound

This paper cites A quantitative analysis of kinase inhibitor selectivity.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration A quantitative analysis of kinase inhibitor selectivity

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.726398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 417424e6-728b-4418-a579-dab5d851a69b · outbound

This paper cites Noise2score: tweedie’s approach to self-supervised image denoising without clean images.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Noise2score: tweedie’s approach to self-supervised image denoising without clean images

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.714270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aedf26a6-bb0f-469d-9893-d5dd626fa00b · outbound

This paper cites Recent developments in structure-based drug design.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Recent developments in structure-based drug design

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.703934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 80d40693-b026-462e-84f1-9895cea71a9e · outbound

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

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.693739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 78adbec1-0a09-401d-832d-bd35e2329254 · outbound

This paper cites A 3d generative model for structure-based drug design.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration A 3d generative model for structure-based drug design

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.683320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 76804ed2-7673-4633-aa5f-1b54f711b2a4 · outbound

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

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Gnina 1.0: molecular docking with deep learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.673382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b03e1972-15a5-48d2-992e-ec8d8ccbcd94 · outbound

This paper cites 3d molecule generation by denoising voxel grids.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration 3d molecule generation by denoising voxel grids

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.661728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 303244e1-558e-445b-8f24-694736762545 · outbound

This paper cites Pocket2mol: Efficient molecular sampling based on 3d protein pockets.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Pocket2mol: Efficient molecular sampling based on 3d protein pockets

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.608080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a3b8f302-f1bd-46a0-82f8-b76b8c1bb878 · outbound

This paper cites MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T14:25:29.394587Z

Source-reported events for the cited work

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Observation 9bb84b3e-45aa-43dc-be7c-b462c49d6638 · outbound

This paper cites Geometric deep learning for structure-based ligand design.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Geometric deep learning for structure-based ligand design

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.419346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7229f47d-e83e-4b4f-9ddb-0d06fc7db79a · outbound

This paper cites Molcraft: Structure-based drug design in continuous parameter space.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Molcraft: Structure-based drug design in continuous parameter space

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.243750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 40e7c504-d5ca-44ff-bb22-caf082a2701b · outbound

This paper cites Generating 3d molecules con- ditional on receptor binding sites with deep generative models.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Generating 3d molecules con- ditional on receptor binding sites with deep generative models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:35.043630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 539b5194-2d31-4058-896d-677fa4aba21d · outbound

This paper cites Structure-based drug design with equivariant diffusion models, 2023.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Structure-based drug design with equivariant diffusion models, 2023

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:34.748192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 916f60f3-f3d0-4a6a-9a44-818136a284f7 · outbound

This paper cites Structure-based drug design with equivariant diffusion models.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Structure-based drug design with equivariant diffusion models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:34.474999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 80e0e365-2cc7-4269-af1e-7514071941a0 · outbound

This paper cites On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T14:25:29.931810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b4e9a2e-f68c-40de-93cf-2055dd84b713 · outbound

This paper cites Tacogfn: Target-conditioned gflownet for structure-based drug design.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Tacogfn: Target-conditioned gflownet for structure-based drug design

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:34.226009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0a13d31b-cfbb-42b0-a166-11c282b495d3 · outbound

This paper cites TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:25:31.729773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 95c506fb-3ed8-416f-80e4-08e8f1cb836f · outbound

This paper cites From target to drug: generative modeling for the multimodal structure-based ligand design.Molecular pharmaceutics, 16(10):4282–4291, 2019.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration From target to drug: generative modeling for the multimodal structure-based ligand design.Molecular pharmaceutics, 16(10):4282–4291, 2019

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:34.031008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 496a8d85-c63e-467f-a53e-0a3d90e1669c · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Score-Based Generative Modeling through Stochastic Differential Equations

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T14:25:30.313220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e59c6ef-d98c-4cb6-a9ba-0f55e3d0338d · outbound

This paper cites Unified generative modeling of 3d molecules with bayesian flow networks.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Unified generative modeling of 3d molecules with bayesian flow networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:33.873781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d1a15dbc-e954-4b4f-b6c3-59c202c6162e · outbound

This paper cites Selective optimization of side activities: another way for drug discovery.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Selective optimization of side activities: another way for drug discovery

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:33.689922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 61daa666-f283-4118-8a0d-e766d873e290 · outbound

This paper cites Learning subpocket prototypes for generalizable structure-based drug design.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Learning subpocket prototypes for generalizable structure-based drug design

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:33.508353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e3e189fc-c547-47ab-b1ad-4794fd956ab3 · outbound

This paper cites Molecule generation for target pro- tein binding with structural motifs.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Molecule generation for target pro- tein binding with structural motifs

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:33.348023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6fe4ccaf-4330-442b-ad39-5d0e1bbf8f94 · outbound

This paper cites Geometric Deep Learning for Structure-Based Drug Design: A Survey.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Geometric Deep Learning for Structure-Based Drug Design: A Survey

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:25:31.511955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 473074fa-b053-4931-abec-30281d4c7a75 · outbound

This paper cites known unknown.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration known unknown

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:33.150214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 637c19c6-7dac-4ef8-8266-19a9b8d8e8d3 · outbound

This paper cites an unresolved cited work.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:25:32.974261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b0d98ba8-6690-423c-a925-0a37e10522d4 · outbound

This paper cites an unresolved cited work.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:25:32.799964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fdf75671-3e8e-4701-8255-57d0eea07636 · outbound

This paper cites Here h(θi−1, yi, αi) computes the posterior parameter after observing yi with precision αi, given the prior θi−1.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Here h(θi−1, yi, αi) computes the posterior parameter after observing yi with precision αi, given the prior θi−1

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:32.571158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c10dfd4-1644-4947-af80-d0435e69b56d · outbound

This paper cites In contrast, diffusion models explicitly add random noise to samples at each step to maintain stochasticity.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration In contrast, diffusion models explicitly add random noise to samples at each step to maintain stochasticity

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:25:32.356065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 66be65b3-41ec-4830-943a-e9d2bffc65f8 · outbound

This paper cites an unresolved cited work.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:25:32.178924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 36b354fe-8d4c-4f37-8624-acd9b4bddcb7 · outbound

This paper cites Only Generation Type.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Only Generation Type

Reference 57

Resolution: malformed identifier; raw_fallback, observed 2026-08-05T14:25:31.976708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation d5d78610-e890-4564-870e-e67b5c4030ad · inbound

Uncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation cites this paper.

Uncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:56:16.229264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.