Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints | Paper Citation Record

Observation 7abb1672-b8e1-4fec-af7c-944e21215fe8 · outbound

This paper cites Chemical representation learning for toxicity prediction[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Chemical representation learning for toxicity prediction[J]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:37.084535Z

Source-reported events for the cited work

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Observation 794e029f-bd56-4e33-94be-a8885f7b1b65 · outbound

This paper cites Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles[J]

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:37.026386Z

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Observation 2c80250b-e2c1-4603-ae2d-21596a60cc01 · outbound

This paper cites Machine learning toxicity prediction: latest advances by toxicity end point[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Machine learning toxicity prediction: latest advances by toxicity end point[J]

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:37.012312Z

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Observation be58c5b9-7897-4819-965c-20571e42719b · outbound

This paper cites DeepTox: toxicity prediction using deep learning[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints DeepTox: toxicity prediction using deep learning[J]

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.898833Z

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Observation cb60f5ef-4cf2-4883-ba77-46f2b9dd7901 · outbound

This paper cites Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations[J]

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.852813Z

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Observation 8e467b1e-6bb0-454e-a6fb-d9ca75c3f658 · outbound

This paper cites Machine learning -quantitative structure property relationship (ML-QSPR) method for fuel physicochemical properties prediction of multiple fuel types[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Machine learning -quantitative structure property relationship (ML-QSPR) method for fuel physicochemical properties prediction of multiple fuel types[J]

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.839431Z

Source-reported events for the cited work

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Observation f5986ab8-61f2-46ea-affc-d14ce47be835 · outbound

This paper cites QSPR for predicting the hydrophile-lipophile balance (HLB) of non- ionic surfactants[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints QSPR for predicting the hydrophile-lipophile balance (HLB) of non- ionic surfactants[J]

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.825851Z

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Observation 4044c1f6-076d-463a-9698-7a0c4726c15f · outbound

This paper cites Topological indices and QSPR modeling of some novel drugs used in the cancer treatment.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Topological indices and QSPR modeling of some novel drugs used in the cancer treatment

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.813409Z

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Observation 2a212624-c5d7-4419-951e-aee546715ed9 · outbound

This paper cites Molecular descriptors[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Molecular descriptors[J]

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.692014Z

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Observation adb199fd-ced0-48a2-94fb-e7d5182b424e · outbound

This paper cites One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome[J]

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.663934Z

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Observation 327bb1b5-c9ad-41ea-ad8b-67582df6be95 · outbound

This paper cites Nevae: A deep generative model for molecular graphs[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Nevae: A deep generative model for molecular graphs[J]

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.646392Z

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Observation def1e947-77ff-4351-a9f8-3203af1f79a6 · outbound

This paper cites Evolution of support vector machine and regression modeling in chemoinformatics and drug discovery[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Evolution of support vector machine and regression modeling in chemoinformatics and drug discovery[J]

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.631485Z

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Observation ff39495e-288e-4097-8f3d-a85dbbf6f9b4 · outbound

This paper cites Support vector machine[M]//Machine learning.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Support vector machine[M]//Machine learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.617514Z

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Observation 20e7447d-258b-4a4e-a82b-85966b570341 · outbound

This paper cites Random forests[M].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Random forests[M]

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.605792Z

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Observation bb566d51-9f6e-43f7-9ad2-b5048e6a49d9 · outbound

This paper cites Kernel ridge regression[M]//Empirical inference: Festschrift in honor of vladimir n.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Kernel ridge regression[M]//Empirical inference: Festschrift in honor of vladimir n

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.593164Z

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Observation 32769758-1e01-40e1-89f4-5b902e0d9e72 · outbound

This paper cites Machine learning methods for small data challenges in molecular science[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Machine learning methods for small data challenges in molecular science[J]

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.581455Z

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Observation 73636884-25ad-4e7e-9a53-3e551c943fc3 · outbound

This paper cites MLP-based regression prediction model for compound bioactivity[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints MLP-based regression prediction model for compound bioactivity[J]

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.430161Z

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Observation 95c13677-0f29-465e-a228-74c5e7ce4dc6 · outbound

This paper cites an unresolved cited work.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:38:36.360175Z

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Observation 24d854a4-dd81-4f7d-a8cc-eea686e5dcc4 · outbound

This paper cites Molecular Generation with Recurrent Neural Networks (RNNs).

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Molecular Generation with Recurrent Neural Networks (RNNs)

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T15:38:34.112566Z

Source-reported events for the cited work

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Observation 0a34149e-9d42-4fdf-b23c-3bf68b677d31 · outbound

This paper cites Message passing neural networks[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Message passing neural networks[J]

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.349006Z

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Observation 6f50a3f8-a2ef-4af6-892c-d14246439390 · outbound

This paper cites Motif -based graph self -supervised learning for molecular property prediction[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Motif -based graph self -supervised learning for molecular property prediction[J]

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:37.037998Z

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Observation 366e1b21-7160-41ff-8f72-c0419f3c70bb · outbound

This paper cites Explaining the explainer: A first theoretical analysis of LIME[C]//International conference on artificial intelligence and statistics.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Explaining the explainer: A first theoretical analysis of LIME[C]//International conference on artificial intelligence and statistics

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.336639Z

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Observation 7892cf4a-8d7f-4268-a601-b365ae9ea10c · outbound

This paper cites From explanations to feature selection: assessing SHAP values as feature selection mechanism[C]//2020 33rd SIBGRAPI conference on Graphics, Patterns and Images (SIBGRAPI).

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints From explanations to feature selection: assessing SHAP values as feature selection mechanism[C]//2020 33rd SIBGRAPI conference on Graphics, Patterns and Images (SIBGRAPI)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.324997Z

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Observation 8a0d745f-d80a-4c6c-b385-d15dd36de849 · outbound

This paper cites A unified approach to interpreting model predictions[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints A unified approach to interpreting model predictions[J]

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.312932Z

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Observation 37790380-50e8-4052-b122-24e62da72cd4 · outbound

This paper cites Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T15:38:34.136854Z

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Observation 61c66bd5-e95d-4fa4-95a3-3ad8851d9281 · outbound

This paper cites Profile scaling increases the similarity search performance of molecular fingerprints containing numerical descriptors and structural keys.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Profile scaling increases the similarity search performance of molecular fingerprints containing numerical descriptors and structural keys

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.155189Z

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Observation 8805e1ae-5b42-477a-b4f2-6215f7e84983 · outbound

This paper cites Descriptor generation from Morgan fingerprint using persistent homology.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Descriptor generation from Morgan fingerprint using persistent homology

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.144370Z

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Observation da201bdb-668c-4ed4-ad28-ff50d7a53a75 · outbound

This paper cites P value interpretations and considerations[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints P value interpretations and considerations[J]

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.132364Z

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Observation 4458cbfa-c7e2-4f3c-b763-183d59572a37 · outbound

This paper cites False Discovery Rate[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints False Discovery Rate[J]

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.121351Z

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Observation cba22c04-c42d-48c1-9dfe-cbf1f64b87f9 · outbound

This paper cites The role of family-wise error rate in determining statistical significance[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints The role of family-wise error rate in determining statistical significance[J]

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.034912Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8c8980dd-5e80-42f2-b7f8-95130f87707b · outbound

This paper cites Permutation importance: a corrected feature importance measure[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Permutation importance: a corrected feature importance measure[J]

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.024033Z

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Observation 6bdad8eb-ba90-4cdc-8c03-30d59a963a7b · outbound

This paper cites Extended-connectivity fingerprints.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Extended-connectivity fingerprints

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.012911Z

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Observation 2a172b1d-04c0-4f27-a836-fecceeefb373 · outbound

This paper cites AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.001949Z

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Observation ee3cbb40-368c-4217-a217-3dbbc387943b · outbound

This paper cites Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor -based and fingerprints-based models.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor -based and fingerprints-based models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.991245Z

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Observation d23a03a0-37f1-4e18-bb6a-2cabe9f8ea6b · outbound

This paper cites ESOL: estimating aqueous solubility directly from molecular structure.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints ESOL: estimating aqueous solubility directly from molecular structure

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.919419Z

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Observation 1ff0fb87-d0c0-4777-b054-77e3bf4fe9eb · outbound

This paper cites FreeSolv: a database of experimental and calculated hydration free energies, with input files.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints FreeSolv: a database of experimental and calculated hydration free energies, with input files

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.855989Z

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Observation 7a52ad8b-2baf-4b6e-9142-05c380a667dc · outbound

This paper cites Machine learning with physicochemical relationships: solubility prediction in organic solvents and water.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Machine learning with physicochemical relationships: solubility prediction in organic solvents and water

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.844412Z

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Observation e4fa0e72-4b6f-4b37-a507-625a11d27374 · outbound

This paper cites Feature learning as alignment: a structural property of gradient descent in non-linear neural networks.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Feature learning as alignment: a structural property of gradient descent in non-linear neural networks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T15:38:34.441081Z

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Observation c61f24e6-84dc-4c60-9535-046b1a0073be · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Neural tangent kernel: Convergence and generalization in neural networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.833017Z

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Observation 9233ed98-0f9d-4d13-bb52-0ba8a8f32579 · outbound

This paper cites Generalization in Kernel Regression Under Realistic Assumptions.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Generalization in Kernel Regression Under Realistic Assumptions

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T15:38:34.534114Z

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Observation 84f136ab-d804-419d-8711-b1df59461116 · outbound

This paper cites Neural Network Layer Matrix Decomposition reveals Latent Manifold Encoding and Memory Capacity.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Neural Network Layer Matrix Decomposition reveals Latent Manifold Encoding and Memory Capacity

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:38:35.219894Z

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Observation f8b69849-423b-4c9d-ac62-a2cb9a5c9460 · outbound

This paper cites Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:38:35.136353Z

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Observation 28f80eeb-92af-4e3a-bb43-81655936b6bf · outbound

This paper cites Mechanism for feature learning in neural networks and backpropagation -free machine learning models[J].

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Mechanism for feature learning in neural networks and backpropagation -free machine learning models[J]

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:36.186989Z

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Observation 85eae852-b3fa-44bb-9dd8-d69b73896d26 · outbound

This paper cites Lightgbm: A highly efficient gradient boosting decision tree.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Lightgbm: A highly efficient gradient boosting decision tree

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.819447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 08a1826e-ec3b-4539-b478-b9cb7e2f18f9 · outbound

This paper cites Xgboost: A scalable tree boosting system.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Xgboost: A scalable tree boosting system

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.787635Z

Source-reported events for the cited work

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Observation 4a9c6e3f-3191-423d-af1e-6738546f992d · outbound

This paper cites Random forests.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Random forests

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.755337Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f6228932-8d06-43a5-9256-a15b3ea32cad · outbound

This paper cites Deep residual learning for image recognition.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Deep residual learning for image recognition

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T15:38:34.778377Z

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Observation 8fc7705c-2514-4e2f-802c-e92f9bb2e590 · outbound

This paper cites Revisiting deep learning models for tabular data.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Revisiting deep learning models for tabular data

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.735045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 934cad13-e80d-443a-9ba5-7ece8ca15e79 · outbound

This paper cites Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.699043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ce92a434-e388-4ca2-8fc4-527a45b925e8 · outbound

This paper cites Comparing support vector machines with Gaussian kernels to radial basis function classifiers.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Comparing support vector machines with Gaussian kernels to radial basis function classifiers

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.619758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 29489f4d-495c-42f2-8ad7-45b397c16748 · outbound

This paper cites Improved msvr-based range-free localization using a rational quadratic kernel function.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Improved msvr-based range-free localization using a rational quadratic kernel function

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.609171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6b67e287-34da-4c68-bd37-077bf33a99a3 · outbound

This paper cites Molecular set representation learning.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Molecular set representation learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.596699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4d1a67f1-c676-4740-9ccb-f5332bd9ffa7 · outbound

This paper cites A Bayesian Flow Network Framework for Chemistry Tasks.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints A Bayesian Flow Network Framework for Chemistry Tasks

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:38:35.096536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e0a616c0-ee03-4042-8365-a1b2f3221ba9 · outbound

This paper cites Bidirectional generation of structure and properties through a single molecular foundation model.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Bidirectional generation of structure and properties through a single molecular foundation model

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.467199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 103cf652-e0dc-4d5e-92e0-66760e9663e3 · outbound

This paper cites Geometry -enhanced molecular representation learning for property prediction.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Geometry -enhanced molecular representation learning for property prediction

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.455665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3ee6f2c9-7258-48ae-8049-364adea88d0f · outbound

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

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Uni-mol: A universal 3d molecular representation learning framework

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.441896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 448c2f78-c9bc-413e-bbcc-df33fa0ed45f · outbound

This paper cites Direct fit to nature: an evolutionary perspective on biological and artificial neural networks.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Direct fit to nature: an evolutionary perspective on biological and artificial neural networks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.430042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6007409a-5ae8-4a6d-a871-f64fe6c8d21f · outbound

This paper cites Bidirectional generation of structure and properties through a single molecular foundation model.

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints Bidirectional generation of structure and properties through a single molecular foundation model

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:38:35.360107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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