Graph Classification on Peptides-func

Metric: AP (higher is better)

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#	Model↕	AP▼	Extra Data	Paper	Date↕	Code
1	ESA + RWSE (Edge set attention, Random Walk Structural Encoding, + validation set)	0.7479	Yes	An end-to-end attention-based approach for learn...	2024-02-16	Code
2	ECFP + LightGBM	0.746	No	Molecular Fingerprints Are Strong Models for Pep...	2025-01-29	Code
3	TT + LightGBM	0.7318	No	Molecular Fingerprints Are Strong Models for Pep...	2025-01-29	Code
4	RDKit + LightGBM	0.7311	No	Molecular Fingerprints Are Strong Models for Pep...	2025-01-29	Code
5	CKGCN	0.6952	No	CKGConv: General Graph Convolution with Continuo...	2024-04-21	Code
6	TIGT	0.6679	No	Topology-Informed Graph Transformer	2024-02-03	Code
7	ViT-PS	0.6575	Yes	Learning Probabilistic Symmetrization for Archit...	2023-06-05	Code
8	EIGENFORMER	0.6414	No	Graph Transformers without Positional Encodings	2024-01-31	-

#1ESA + RWSE (Edge set attention, Random Walk Structural Encoding, + validation set)SOTA
0.7479
AP· Extra Data· 2024-02-16
An end-to-end attention-based approach for learning on graphs Code
#2ECFP + LightGBM
0.746
AP· 2025-01-29
Molecular Fingerprints Are Strong Models for Peptide Function Prediction Code
#3TT + LightGBM
0.7318
AP· 2025-01-29
Molecular Fingerprints Are Strong Models for Peptide Function Prediction Code
#4RDKit + LightGBM
0.7311
AP· 2025-01-29
Molecular Fingerprints Are Strong Models for Peptide Function Prediction Code
#5CKGCN
0.6952
AP· 2024-04-21
CKGConv: General Graph Convolution with Continuous Kernels Code
#6TIGTSOTA
0.6679
AP· 2024-02-03
Topology-Informed Graph Transformer Code
#7ViT-PSSOTA
0.6575
AP· Extra Data· 2023-06-05
Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance Code
#8EIGENFORMER
0.6414
AP· 2024-01-31
Graph Transformers without Positional Encodings