A standard transformer and attention with linear biases for molecular conformer generation | Paper Citation Record

Observation 9e841379-31d9-40bf-a0c5-19cf01b4f682 · outbound

This paper cites GEOM, energy-annotated molecular conformations for property prediction and molecular generation.Scientific Data, 9(185), 2022.

A standard transformer and attention with linear biases for molecular conformer generation GEOM, energy-annotated molecular conformations for property prediction and molecular generation.Scientific Data, 9(185), 2022

Reference 1

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This paper cites Equivariant energy-guided SDE for inverse molecular design.

A standard transformer and attention with linear biases for molecular conformer generation Equivariant energy-guided SDE for inverse molecular design

Reference 2

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Observation caa8a51a-deb0-4b4f-ab42-d9e16d015c8f · outbound

This paper cites How Attentive are Graph Attention Networks?.

A standard transformer and attention with linear biases for molecular conformer generation How Attentive are Graph Attention Networks?

Reference 3

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This paper cites Memory Transformer.

A standard transformer and attention with linear biases for molecular conformer generation Memory Transformer

Reference 4

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This paper cites Morris, and Charlotte M.

A standard transformer and attention with linear biases for molecular conformer generation Morris, and Charlotte M

Reference 5

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This paper cites On the Importance of Noise Scheduling for Diffusion Models.

A standard transformer and attention with linear biases for molecular conformer generation On the Importance of Noise Scheduling for Diffusion Models

Reference 6

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This paper cites Principal neighbourhood aggregation for graph nets.Advances in Neural Information Processing Systems, 33:13260–13271, 2020.

A standard transformer and attention with linear biases for molecular conformer generation Principal neighbourhood aggregation for graph nets.Advances in Neural Information Processing Systems, 33:13260–13271, 2020

Reference 7

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Observation 6c1ec338-8777-4360-aac1-dbdb5ff689bb · outbound

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

A standard transformer and attention with linear biases for molecular conformer generation A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

Reference 8

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Observation a4c896c9-6305-42a9-adf4-6f229a1b971c · outbound

This paper cites A generalization of transformer networks to graphs.

A standard transformer and attention with linear biases for molecular conformer generation A generalization of transformer networks to graphs

Reference 9

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This paper cites Coley, Regina Barzilay, Klavs F.

A standard transformer and attention with linear biases for molecular conformer generation Coley, Regina Barzilay, Klavs F

Reference 10

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Observation f9c236fc-8e9c-4842-9ee4-30bfa1b4b72c · outbound

This paper cites Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules.

A standard transformer and attention with linear biases for molecular conformer generation Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules

Reference 11

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This paper cites On embeddings for numerical features in tabular deep learning.Advances in Neural Information Processing Systems, 35:24991–25004, 2022.

A standard transformer and attention with linear biases for molecular conformer generation On embeddings for numerical features in tabular deep learning.Advances in Neural Information Processing Systems, 35:24991–25004, 2022

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:59.213910Z

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This paper cites Scaffold splits overesti- mate virtual screening performance.

A standard transformer and attention with linear biases for molecular conformer generation Scaffold splits overesti- mate virtual screening performance

Reference 13

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This paper cites ET-Flow: Equivariant flow-matching for molecular conformer generation.

A standard transformer and attention with linear biases for molecular conformer generation ET-Flow: Equivariant flow-matching for molecular conformer generation

Reference 14

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This paper cites Havel, Irwin D.

A standard transformer and attention with linear biases for molecular conformer generation Havel, Irwin D

Reference 15

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This paper cites an unresolved cited work.

A standard transformer and attention with linear biases for molecular conformer generation Unresolved cited work

Reference 16

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A standard transformer and attention with linear biases for molecular conformer generation Unresolved cited work

Reference 17

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This paper cites Ballester.

A standard transformer and attention with linear biases for molecular conformer generation Ballester

Reference 18

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Observation 1662ab58-916f-43c8-9565-ee063ea3138e · outbound

This paper cites Denoising diffusion probabilistic models.

A standard transformer and attention with linear biases for molecular conformer generation Denoising diffusion probabilistic models

Reference 19

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Observation 2a91beb4-445f-4723-a8e5-ed89729027ea · outbound

This paper cites Cascaded diffusion models for high fidelity image generation.Journal of Machine Learning Research, 23(47):1–33, 2022.

A standard transformer and attention with linear biases for molecular conformer generation Cascaded diffusion models for high fidelity image generation.Journal of Machine Learning Research, 23(47):1–33, 2022

Reference 20

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Observation c82ec683-4951-4404-8cb3-9c3707285462 · outbound

This paper cites Perceiver IO: A general architecture for structured inputs & outputs.

A standard transformer and attention with linear biases for molecular conformer generation Perceiver IO: A general architecture for structured inputs & outputs

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:59.128230Z

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This paper cites Torsional diffusion for molecular conformer generation.Advances in Neural Information Processing Systems, 35:24240–24253, 2022.

A standard transformer and attention with linear biases for molecular conformer generation Torsional diffusion for molecular conformer generation.Advances in Neural Information Processing Systems, 35:24240–24253, 2022

Reference 22

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This paper cites Eigenfold: Generative protein structure prediction with diffusion models.

A standard transformer and attention with linear biases for molecular conformer generation Eigenfold: Generative protein structure prediction with diffusion models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:59.111898Z

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This paper cites Rethinking graph transformers with spectral attention.Advances in Neural Information Pro- cessing Systems, 34:21618–21629, 2021.

A standard transformer and attention with linear biases for molecular conformer generation Rethinking graph transformers with spectral attention.Advances in Neural Information Pro- cessing Systems, 34:21618–21629, 2021

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:59.099978Z

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Observation e34a702f-0f51-49c5-b321-a57a4d525e5c · outbound

This paper cites Graph inductive biases in transformers without message passing.

A standard transformer and attention with linear biases for molecular conformer generation Graph inductive biases in transformers without message passing

Reference 25

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Observation dc8bb7b2-1ccb-4f5b-8acd-128b4f6c5fe6 · outbound

This paper cites Molecular geometry prediction using a deep generative graph neural network.Scientific reports, 9(1):20381, 2019.

A standard transformer and attention with linear biases for molecular conformer generation Molecular geometry prediction using a deep generative graph neural network.Scientific reports, 9(1):20381, 2019

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:59.081770Z

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Observation 16e2f765-7b30-4d60-8d25-88bbb900c678 · outbound

This paper cites Transformer for Graphs: An Overview from Architecture Perspective.

A standard transformer and attention with linear biases for molecular conformer generation Transformer for Graphs: An Overview from Architecture Perspective

Reference 27

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This paper cites Miteva, Frederic Guyon, and Pierre Tufféry.

A standard transformer and attention with linear biases for molecular conformer generation Miteva, Frederic Guyon, and Pierre Tufféry

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:59.070667Z

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This paper cites Automated exploration of the low-energy chemical space with fast quantum chemical methods.Physical Chemistry Chemical Physics, 22:7169–7192, 2020.

A standard transformer and attention with linear biases for molecular conformer generation Automated exploration of the low-energy chemical space with fast quantum chemical methods.Physical Chemistry Chemical Physics, 22:7169–7192, 2020

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:59.059974Z

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This paper cites Wesołowski, and Felix Zeller.

A standard transformer and attention with linear biases for molecular conformer generation Wesołowski, and Felix Zeller

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:59.048714Z

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This paper cites Train short, test long: Attention with linear biases enables input length extrapolation.

A standard transformer and attention with linear biases for molecular conformer generation Train short, test long: Attention with linear biases enables input length extrapolation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:59.038980Z

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This paper cites Recipe for a general, powerful, scalable graph transformer.Advances in Neural Information Processing Systems, 35:14501–14515, 2022.

A standard transformer and attention with linear biases for molecular conformer generation Recipe for a general, powerful, scalable graph transformer.Advances in Neural Information Processing Systems, 35:14501–14515, 2022

Reference 32

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A standard transformer and attention with linear biases for molecular conformer generation Unresolved cited work

Reference 33

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This paper cites E (n) equivariant graph neural networks.

A standard transformer and attention with linear biases for molecular conformer generation E (n) equivariant graph neural networks

Reference 34

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This paper cites Learning gradient fields for molecular conformation generation.

A standard transformer and attention with linear biases for molecular conformer generation Learning gradient fields for molecular conformation generation

Reference 35

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This paper cites A generative model for molecular distance geometry.

A standard transformer and attention with linear biases for molecular conformer generation A generative model for molecular distance geometry

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:58.997152Z

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Observation f6e75bea-4128-46fe-b8f1-a10a016e4562 · outbound

This paper cites Denoising diffusion implicit models.

A standard transformer and attention with linear biases for molecular conformer generation Denoising diffusion implicit models

Reference 37

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Observation e0d856ec-beb0-4980-a2ef-ec8dc46e0d1d · outbound

This paper cites Score-based generative modeling through stochastic differential equations.

A standard transformer and attention with linear biases for molecular conformer generation Score-based generative modeling through stochastic differential equations

Reference 38

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Observation 68cff1dc-1c1d-462f-906b-9d148b017538 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024.

A standard transformer and attention with linear biases for molecular conformer generation Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024

Reference 39

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This paper cites MLP- mixer: An all-MLP architecture for vision.Advances in neural information processing systems, 34:24261–24272, 2021.

A standard transformer and attention with linear biases for molecular conformer generation MLP- mixer: An all-MLP architecture for vision.Advances in neural information processing systems, 34:24261–24272, 2021

Reference 40

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

A standard transformer and attention with linear biases for molecular conformer generation LLaMA: Open and Efficient Foundation Language Models

Reference 41

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This paper cites Vainio and Mark S.

A standard transformer and attention with linear biases for molecular conformer generation Vainio and Mark S

Reference 42

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Observation ee93620d-1aea-4a96-a57f-6354cf926ae4 · outbound

This paper cites Swallowing the bitter pill: Simplified scalable conformer generation.

A standard transformer and attention with linear biases for molecular conformer generation Swallowing the bitter pill: Simplified scalable conformer generation

Reference 43

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This paper cites Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S.

A standard transformer and attention with linear biases for molecular conformer generation Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S

Reference 44

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Observation 91c9496e-b85a-4dd1-b172-03443ef7f3ea · outbound

This paper cites Learning neural generative dynamics for molecular conformation generation.

A standard transformer and attention with linear biases for molecular conformer generation Learning neural generative dynamics for molecular conformation generation

Reference 45

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This paper cites GeoDiff: A geometric diffusion model for molecular conformation generation.

A standard transformer and attention with linear biases for molecular conformer generation GeoDiff: A geometric diffusion model for molecular conformation generation

Reference 46

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This paper cites Do transformers really perform badly for graph representation?Advances in neural information processing systems, 34:28877–28888, 2021.

A standard transformer and attention with linear biases for molecular conformer generation Do transformers really perform badly for graph representation?Advances in neural information processing systems, 34:28877–28888, 2021

Reference 47

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This paper cites Do deep learning methods really perform better in molecular conformation generation? InICLR 2023-Machine Learning for Drug Discovery workshop, 2023.

A standard transformer and attention with linear biases for molecular conformer generation Do deep learning methods really perform better in molecular conformation generation? InICLR 2023-Machine Learning for Drug Discovery workshop, 2023

Reference 48

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Observation e976132f-d115-4a2b-bb2b-8bc0d4a515ea · outbound

This paper cites Direct molecular conformation generation.

A standard transformer and attention with linear biases for molecular conformer generation Direct molecular conformation generation

Reference 49

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This paper cites intramolecular validity.

A standard transformer and attention with linear biases for molecular conformer generation intramolecular validity

Reference 50

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