A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era | Paper Citation Record

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A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Unresolved cited work

Reference 1

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This paper cites Schoenholz, Patrick F.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Schoenholz, Patrick F

Reference 2

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This paper cites Anatole von Lilienfeld, Klaus-Robert Müller, and Alexandre Tkatchenko.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Anatole von Lilienfeld, Klaus-Robert Müller, and Alexandre Tkatchenko

Reference 3

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This paper cites Applications of machine learning in drug discovery and development.Nature Reviews Drug Discovery, 18(6):463–477.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Applications of machine learning in drug discovery and development.Nature Reviews Drug Discovery, 18(6):463–477

Reference 4

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This paper cites A compact review of molecular property prediction with graph neural networks.Drug Discovery Today: Technologies, 37:1–12.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era A compact review of molecular property prediction with graph neural networks.Drug Discovery Today: Technologies, 37:1–12

Reference 5

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This paper cites A perspective on foundation models in chemistry.JACS Au, 5(4):1499–1518.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era A perspective on foundation models in chemistry.JACS Au, 5(4):1499–1518

Reference 6

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This paper cites Advancements in molecular property prediction: A survey of single and multimodal approaches.Archives of Computational Methods in Engineering, pages 1–31.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Advancements in molecular property prediction: A survey of single and multimodal approaches.Archives of Computational Methods in Engineering, pages 1–31

Reference 7

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This paper cites Calculation of local excitations in large systems by embedding wave-function theory in density-functional theory.Physical Chemistry Chemical Physics, 10(35):5353–5362.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Calculation of local excitations in large systems by embedding wave-function theory in density-functional theory.Physical Chemistry Chemical Physics, 10(35):5353–5362

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This paper cites Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges.Journal of Chemical Theory and Computation, 15(6):3678–3693.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges.Journal of Chemical Theory and Computation, 15(6):3678–3693

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.858544Z

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This paper cites The performance of density functional and wavefunction- based methods for 2d and 3d structures of au10.Journal of Computational Chemistry, 34(23):1975–1981.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era The performance of density functional and wavefunction- based methods for 2d and 3d structures of au10.Journal of Computational Chemistry, 34(23):1975–1981

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This paper cites Linear-scaling quantum mechanical calculations of biological molecules: The divide-and-conquer approach.Computational Materials Science, 12(3):259–277.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Linear-scaling quantum mechanical calculations of biological molecules: The divide-and-conquer approach.Computational Materials Science, 12(3):259–277

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This paper cites Large-scale molecular simulations of hypervelocity impact of materials.Procedia Engineering, 58:167–176.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Large-scale molecular simulations of hypervelocity impact of materials.Procedia Engineering, 58:167–176

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This paper cites Extended-connectivity fingerprints.Journal of chemical information and modeling, 50(5):742–754.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Extended-connectivity fingerprints.Journal of chemical information and modeling, 50(5):742–754

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.604956Z

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A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Unresolved cited work

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Observation 7657466c-63f6-41b8-96a0-ec611f0a1d40 · outbound

This paper cites Reconstruction of lossless molecular representations from fingerprints.Journal of cheminformatics, 15(1):26.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Reconstruction of lossless molecular representations from fingerprints.Journal of cheminformatics, 15(1):26

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This paper cites Geometry-enhanced molecular representation learning for property prediction.Nature Machine Intelligence, 4(2):127–134.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Geometry-enhanced molecular representation learning for property prediction.Nature Machine Intelligence, 4(2):127–134

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This paper cites Se (3)-transformers: 3d roto-translation equivariant attention networks.Advances in neural information processing systems, 33:1970–1981.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Se (3)-transformers: 3d roto-translation equivariant attention networks.Advances in neural information processing systems, 33:1970–1981

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This paper cites Directed message passing based on attention for prediction of molecular properties.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Directed message passing based on attention for prediction of molecular properties

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Observation 6dcb1b91-b81b-492f-b041-730a38e6d75a · outbound

This paper cites Generalizing from a few examples: A survey on few-shot learning.ACM computing surveys (csur), 53(3):1–34.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Generalizing from a few examples: A survey on few-shot learning.ACM computing surveys (csur), 53(3):1–34

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This paper cites Fs-mol: A few-shot learning dataset of molecules.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Fs-mol: A few-shot learning dataset of molecules

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A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Unresolved cited work

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Observation bc2490e0-fe0a-4aa7-b2f1-39ad5d1b9486 · outbound

This paper cites Evaluating large language models on multimodal chemistry olympiad exams.Communications Chemistry, 8(1):402, Dec 2025.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Evaluating large language models on multimodal chemistry olympiad exams.Communications Chemistry, 8(1):402, Dec 2025

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This paper cites Ash, Cas Wognum, Raquel Rodríguez-Pérez, Matteo Aldeghi, Alan C.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Ash, Cas Wognum, Raquel Rodríguez-Pérez, Matteo Aldeghi, Alan C

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.747806Z

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This paper cites DeepTox: Toxicity Prediction using Deep Learning.Frontiers in Environmental Science, 3.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era DeepTox: Toxicity Prediction using Deep Learning.Frontiers in Environmental Science, 3

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This paper cites Fate-tox: fragment attention transformer for E(3)-equivariant multi-organ toxicity prediction.Journal of Cheminformatics, 17(1):74, May 2025.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Fate-tox: fragment attention transformer for E(3)-equivariant multi-organ toxicity prediction.Journal of Cheminformatics, 17(1):74, May 2025

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

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era DeepDTA: deep drug–target binding affinity prediction

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Observation 9c715081-ebd3-4134-90a3-4b03bf2897f3 · outbound

This paper cites ChemBERTa-2: Towards Chemical Foundation Models.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era ChemBERTa-2: Towards Chemical Foundation Models

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Observation 5008750e-0dea-480b-8c51-03989f6527cd · outbound

This paper cites Molecular representation learning with language models and domain-relevant auxiliary tasks.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Molecular representation learning with language models and domain-relevant auxiliary tasks

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A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Unresolved cited work

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Observation c179eec3-e5b5-4c45-9bb1-9c2a6a8323df · outbound

This paper cites Mole: a foundation model for molecular graphs using disentangled attention.Nature Communications, 15(1):9431.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Mole: a foundation model for molecular graphs using disentangled attention.Nature Communications, 15(1):9431

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Observation 3eba259b-5626-41f3-9d43-8bf012a6294f · outbound

This paper cites Uni-mol: A universal 3d molecular representation learning framework.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Uni-mol: A universal 3d molecular representation learning framework

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Observation 1a3f254d-7f9d-4246-acd8-6117e1d6362b · outbound

This paper cites Hoffman, C.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Hoffman, C

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This paper cites ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries

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Observation 2538680b-cae1-4113-8a4a-2a5c0e96ec79 · outbound

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

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S

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This paper cites Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk

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Observation de37e466-2f41-4307-9d28-861019f55003 · outbound

This paper cites A predictive machine learning force-field framework for liquid electrolyte development.Nature Machine Intelligence, 7(4):543–552, April 2025.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era A predictive machine learning force-field framework for liquid electrolyte development.Nature Machine Intelligence, 7(4):543–552, April 2025

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Observation 45581cbd-7534-4675-9ace-547309a7a197 · outbound

This paper cites Uni-electrolyte: An artificial intelligence platform for designing electrolyte molecules for rechargeable batteries.Angewandte Chemie International Edition, 64(30):e202503105.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Uni-electrolyte: An artificial intelligence platform for designing electrolyte molecules for rechargeable batteries.Angewandte Chemie International Edition, 64(30):e202503105

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Observation 7ac05cf1-d4c3-4d38-ac29-9f52b200c448 · outbound

This paper cites Do transformers really perform badly for graph representation? InThirty-Fifth Conference on Neural Information Processing Systems.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Do transformers really perform badly for graph representation? InThirty-Fifth Conference on Neural Information Processing Systems

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.620599Z

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Observation 2ab45dff-876b-46e7-83bc-3001477291fa · outbound

This paper cites Graphmae: Self- supervised masked graph autoencoders.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Graphmae: Self- supervised masked graph autoencoders

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Observation 954c6a91-53d0-4bd9-97d3-2f555dea36e0 · outbound

This paper cites Chemberta: Large-scale self-supervised pretrain- ing for molecular property prediction.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Chemberta: Large-scale self-supervised pretrain- ing for molecular property prediction

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.661986Z

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Observation cf63b856-db4d-4791-97f6-be913f44d9a3 · outbound

This paper cites Molecular contrastive learning of representations via graph neural networks.Nature Machine Intelligence, 4(3):279–287.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Molecular contrastive learning of representations via graph neural networks.Nature Machine Intelligence, 4(3):279–287

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.593044Z

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Observation 183dc81b-8c93-49db-95d7-4217bd77d3fa · outbound

This paper cites Nadkarni, Benjamin S.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Nadkarni, Benjamin S

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.721649Z

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Observation cd637838-f089-4d75-924a-a260d93e76ae · outbound

This paper cites Allegro-fm: Toward an equivariant foundation model for exascale molecular dynamics simulations.The Journal of Physical Chemistry Letters, 16:6637–6644.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Allegro-fm: Toward an equivariant foundation model for exascale molecular dynamics simulations.The Journal of Physical Chemistry Letters, 16:6637–6644

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.646143Z

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Observation 6b264e54-dd9c-4bff-99de-2d7f5f566758 · outbound

This paper cites Smiles-bert: Large scale unsupervised pre-training for molecular property prediction.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Smiles-bert: Large scale unsupervised pre-training for molecular property prediction

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Observation ac61d0ea-a0d5-44e8-a8a8-6b20845638a4 · outbound

This paper cites Transformers for molecular property prediction: Domain adaptation efficiently improves performance.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Transformers for molecular property prediction: Domain adaptation efficiently improves performance

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.618604Z

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Observation b2515246-d558-4a9c-b59d-18f41ae1df21 · outbound

This paper cites Mol2vec: Unsupervised machine learning approach with chemical intuition.Journal of Chemical Information and Modeling, 58(1):27–35.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Mol2vec: Unsupervised machine learning approach with chemical intuition.Journal of Chemical Information and Modeling, 58(1):27–35

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.627325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c5e7e70e-f97d-479e-bfec-e94535b765b1 · outbound

This paper cites Bemis and Mark A.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Bemis and Mark A

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.622344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 49869b2c-6d4f-4873-aa60-96ee72604477 · outbound

This paper cites Unsupervised data base clustering based on Daylight’s fingerprint and Tanimoto similarity: A fast and automated way to cluster small and large data sets.J.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Unsupervised data base clustering based on Daylight’s fingerprint and Tanimoto similarity: A fast and automated way to cluster small and large data sets.J

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.597417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4fec4151-1025-4d6a-b028-b79a7900bc6b · outbound

This paper cites Ballester.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Ballester

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.606796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation db1f80ac-35b0-406d-9db1-8d676dfc66a6 · outbound

This paper cites Ballester.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Ballester

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.625025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 80f7b35c-77d3-4f31-a414-e9dc2cb987ce · outbound

This paper cites Sheridan.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Sheridan

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.679916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cfd8297c-6170-4b93-9cc7-90877367150a · outbound

This paper cites Patrick Walters.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Patrick Walters

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.871840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 74011e64-4aaa-45d3-b390-fc9ee8797157 · outbound

This paper cites Uncertainty quantification and propagation in atomistic machine learning.Reviews in Chemical Engineering, 41(4):333–357.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Uncertainty quantification and propagation in atomistic machine learning.Reviews in Chemical Engineering, 41(4):333–357

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.671485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ae2b70ed-2e76-4942-be62-45f7714149b6 · outbound

This paper cites an unresolved cited work.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-05-20T21:14:02.757467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 94f01cec-1d03-4864-bca3-a79b8649823e · outbound

This paper cites an unresolved cited work.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-05-20T21:14:02.717201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fff78111-0056-4290-8205-3633dd4403c1 · outbound

This paper cites Grambow, Barbara Pernici, Yi-Pei Li, and William H.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Grambow, Barbara Pernici, Yi-Pei Li, and William H

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.590790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1a417d23-21c0-45ba-9257-fc82f3537b4b · outbound

This paper cites Ltau-ff: Loss trajectory analysis for uncertainty in atomistic force fields.Machine Learning: Science and Technology, 6(1):015048, feb 2025.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Ltau-ff: Loss trajectory analysis for uncertainty in atomistic force fields.Machine Learning: Science and Technology, 6(1):015048, feb 2025

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.704126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c44065a8-fd89-4324-94c8-99061826ada3 · outbound

This paper cites Fast uncertainty estimates in deep learning interatomic potentials.The Journal of Chemical Physics, 158(16).

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Fast uncertainty estimates in deep learning interatomic potentials.The Journal of Chemical Physics, 158(16)

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.836866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e6136117-f456-48c2-9be7-91075cc1cd03 · outbound

This paper cites Deep evidential regression.Advances in neural information processing systems, 33:14927–14937.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Deep evidential regression.Advances in neural information processing systems, 33:14927–14937

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.666403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1efcb7d6-5b5e-4bec-9239-e06749b81eeb · outbound

This paper cites Soleimany, Alexander Amini, Samuel Goldman, Daniela Rus, Sangeeta N.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Soleimany, Alexander Amini, Samuel Goldman, Daniela Rus, Sangeeta N

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.650697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cfc9f80b-ec9b-4597-a9ae-a18635f8bcb4 · outbound

This paper cites Dropout as a Bayesian approximation: Representing model uncertainty in deep learning.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Dropout as a Bayesian approximation: Representing model uncertainty in deep learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.726390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ff2c1e42-4cc0-4393-a984-84de77433ceb · outbound

This paper cites When gaussian process meets big data: A review of scalable gps.IEEE transactions on neural networks and learning systems, 31(11):4405–4423.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era When gaussian process meets big data: A review of scalable gps.IEEE transactions on neural networks and learning systems, 31(11):4405–4423

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.640800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d9b91ad9-ff80-4e8b-9cb3-ca66da56e1c1 · outbound

This paper cites SimSon: Simple contrastive learning of SMILES for molecular property prediction.Bioinformatics, 41(5).

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era SimSon: Simple contrastive learning of SMILES for molecular property prediction.Bioinformatics, 41(5)

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.643489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9dd10db8-9cd3-421e-bccf-dd060e875de6 · outbound

This paper cites Convolutional neural network based on SMILES representation of compounds for detecting chemical motif.BMC Bioinformatics, 19(Suppl 19):526.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Convolutional neural network based on SMILES representation of compounds for detecting chemical motif.BMC Bioinformatics, 19(Suppl 19):526

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.648354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0d5f6537-bf8c-4ffd-9e27-180c4c18c97e · outbound

This paper cites Deepsmiles: An adaptation of smiles for use in machine-learning of chemical structures.ChemRxiv, 2018(0919).

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Deepsmiles: An adaptation of smiles for use in machine-learning of chemical structures.ChemRxiv, 2018(0919)

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.817614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fc1ae5c7-9102-475c-9854-a64978d4388e · outbound

This paper cites Smiles pair encoding: A data-driven substructure tokenization algorithm for deep learning.Journal of Chemical Information and Modeling, 61(4):1560–1569.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Smiles pair encoding: A data-driven substructure tokenization algorithm for deep learning.Journal of Chemical Information and Modeling, 61(4):1560–1569

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.638302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation aa809822-f21b-4ae5-b859-cd30676fead1 · outbound

This paper cites SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction.Frontiers in Chemistry, 7.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction.Frontiers in Chemistry, 7

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.826097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 19c8119e-edb7-47b3-a78a-042c28e0fd00 · outbound

This paper cites AlHammadi.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era AlHammadi

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.877912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation af61d449-7412-4554-b46e-0e5384402a7b · outbound

This paper cites Mol-bert: An effective molecular representation with bert for molecular property prediction.Wireless Communications and Mobile Computing, 2021(1):7181815.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Mol-bert: An effective molecular representation with bert for molecular property prediction.Wireless Communications and Mobile Computing, 2021(1):7181815

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.819684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0fdec97d-9fad-4590-a7e9-5c84d4f0f563 · outbound

This paper cites Chemformer: a pre-trained transformer for computational chemistry.Machine Learning: Science and Technology, 3(1):015022, jan 2022.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Chemformer: a pre-trained transformer for computational chemistry.Machine Learning: Science and Technology, 3(1):015022, jan 2022

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.850857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8ed701ef-3275-4111-9bb3-1ffe0d49f0fc · outbound

This paper cites an unresolved cited work.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-05-20T21:14:02.773848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 317fb060-9911-48fc-9ff4-de260efcd2d0 · outbound

This paper cites Goh, Nathan O.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Goh, Nathan O

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.780090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ebda7935-ccdc-4a3c-848a-e6805e93d79e · outbound

This paper cites ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data.Journal of Cheminformatics, 17(1):126, August 2025.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data.Journal of Cheminformatics, 17(1):126, August 2025

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.788678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e6f51cbd-5cad-4177-bb84-500c07e57798 · outbound

This paper cites Chemical representation learning for toxicity prediction.Digital Discovery, 2(3):674–691.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Chemical representation learning for toxicity prediction.Digital Discovery, 2(3):674–691

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.634266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6d512e72-bc47-4ccf-9357-2ecaf50684cb · outbound

This paper cites MolTrans: Molecular Interaction Transformer for drug–target interaction prediction.Bioinformatics, 37(6):830–836, March 2021.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era MolTrans: Molecular Interaction Transformer for drug–target interaction prediction.Bioinformatics, 37(6):830–836, March 2021

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.811385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 44d21416-bc44-4159-8a65-40a55ef6d57f · outbound

This paper cites Group selfies: a robust fragment-based molecular string representation.Digital Discovery, 2, 03 2023.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Group selfies: a robust fragment-based molecular string representation.Digital Discovery, 2, 03 2023

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.840428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 763eb398-64e4-4a4a-858f-258b6e43462e · outbound

This paper cites SELFormer: Molecular representation learning via SELFIES language models.Machine Learning: Science and Technology, 4(2):025035.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era SELFormer: Molecular representation learning via SELFIES language models.Machine Learning: Science and Technology, 4(2):025035

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.588986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f27b9c77-10ca-4729-821d-f527722b38a5 · outbound

This paper cites Self-referencing embedded strings (selfies): A 100% robust molecular string representation.Machine Learning: Science and Technology, 1(4):045024.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Self-referencing embedded strings (selfies): A 100% robust molecular string representation.Machine Learning: Science and Technology, 1(4):045024

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.584549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8e5d20be-d3fe-4a12-96e4-14577c538045 · outbound

This paper cites Distributed representations of words and phrases and their compositionality.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Distributed representations of words and phrases and their compositionality

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.629410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1f4650b4-d75e-43d0-b361-015d586e3949 · outbound

This paper cites Heller, Alan McNaught, Igor Pletnev, Stephen Stein, and Dmitrii Tchekhovskoi.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Heller, Alan McNaught, Igor Pletnev, Stephen Stein, and Dmitrii Tchekhovskoi

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.610472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 826ed62d-8aa6-46ed-84d1-97d54890ebab · outbound

This paper cites Graph attention networks.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Graph attention networks

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.784123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 68f6dc90-4a83-484d-92f3-ba1bc38c603d · outbound

This paper cites Gnn-skan: Advancing molecular representation learning with swallowkan.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Gnn-skan: Advancing molecular representation learning with swallowkan

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.599304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7025a341-222d-4d1d-a237-74da5903807d · outbound

This paper cites Semi-supervised classification with graph convolutional networks.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Semi-supervised classification with graph convolutional networks

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.601487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bfe6a330-2570-4051-b2d7-68afa55eafb4 · outbound

This paper cites Chawla, and Nuno Moniz.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Chawla, and Nuno Moniz

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.608606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3aaf540e-b472-4220-97fb-786c0da10454 · outbound

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

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Recipe for a general, powerful, scalable graph transformer.Advances in Neural Information Processing Systems, 35:14501–14515

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.724330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9f83a25b-bbdd-4fa3-9a66-b5ba928f7368 · outbound

This paper cites N-gram graph: Simple unsupervised representation for graphs, with applications to molecules.Advances in neural information processing systems, 32.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era N-gram graph: Simple unsupervised representation for graphs, with applications to molecules.Advances in neural information processing systems, 32

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.730974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 77072a20-c841-4ddd-9d05-17a28eab3bca · outbound

This paper cites Chemical graph-based transformer models for yield prediction of high-throughput cross-coupling reaction datasets.ACS Omega, 9(39):40907–40919.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Chemical graph-based transformer models for yield prediction of high-throughput cross-coupling reaction datasets.ACS Omega, 9(39):40907–40919

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.688091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cc605463-b72e-4c9b-bcae-dc9e73f61a37 · outbound

This paper cites Self- supervised graph transformer on large-scale molecular data.Advances in neural information processing systems, 33:12559–12571.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Self- supervised graph transformer on large-scale molecular data.Advances in neural information processing systems, 33:12559–12571

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.652848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6c98da7a-a9ba-449f-ae57-a7cad3ea90f1 · outbound

This paper cites How powerful are graph neural networks? In International Conference on Learning Representations.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era How powerful are graph neural networks? In International Conference on Learning Representations

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.697183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d598b27f-80a4-487d-896c-c944b7c7b494 · outbound

This paper cites KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning

Reference 90

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:43:01.487003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1e07c9fc-1a24-49b2-b333-7f22b309d70c · outbound

This paper cites Graphkan: Graph kolmogorov arnold network for small molecule- protein interaction predictions.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Graphkan: Graph kolmogorov arnold network for small molecule- protein interaction predictions

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.615729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 53c25f08-7264-4a13-98a0-375181faa4aa · outbound

This paper cites Directional message passing for molecular graphs.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Directional message passing for molecular graphs

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.856642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b5848d51-e5d4-4914-94d1-9dcab8abda2d · outbound

This paper cites TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:43:01.482839Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fa39fe7a-454e-4407-bedc-b655a86499f6 · outbound

This paper cites Spherical message passing for 3d molecular graphs.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Spherical message passing for 3d molecular graphs

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.830214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c1efedfe-08b8-437b-8a79-368f2abbf7bf · outbound

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

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+

Reference 95

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:43:01.478865Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f57b7722-619f-4879-a12c-ca6965ca2920 · outbound

This paper cites Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules

Reference 96

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:43:01.495515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e8180b33-502e-42a6-986f-c6da7e2da608 · outbound

This paper cites Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Reference 97

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:43:01.475043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cbf0a763-1039-4c3c-992f-2e122a3c4efd · outbound

This paper cites Jaakkola.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Jaakkola

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.838644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b4e50095-5f71-46e5-ab3e-514792a379d9 · outbound

This paper cites Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets

Reference 99

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:43:01.491145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 78317eb0-bec5-4f9b-98e9-ed39b9a82489 · outbound

This paper cites Gemnet: Universal directional graph neural networks for molecules.Advances in Neural Information Processing Systems, 34:6790–6802.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era Gemnet: Universal directional graph neural networks for molecules.Advances in Neural Information Processing Systems, 34:6790–6802

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T21:14:02.815559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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