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Papers › Molecular Fingerprints Are Strong Models for Peptide Function Prediction

Molecular Fingerprints Are Strong Models for Peptide Function Prediction

29 Jan 2025arXiv:2501.17901archive 2025-07-28

Jakub Adamczyk, Piotr Ludynia, Wojciech Czech

We study the effectiveness of molecular fingerprints for peptide property prediction and demonstrate that domain-specific feature extraction from molecular graphs can outperform complex and computationally expensive models such as GNNs, pretrained sequence-based transformers and multimodal ensembles, even without hyperparameter tuning. To this end, we perform a thorough evaluation on 126 datasets, achieving state-of-the-art results on LRGB and 5 other peptide function prediction benchmarks. We show that models based on count variants of ECFP, Topological Torsion, and RDKit molecular fingerprints and LightGBM as classification head are remarkably robust. The strong performance of molecular fingerprints, which are intrinsically very short-range feature encoders, challenges the presumed importance of long-range interactions in peptides. Our conclusion is that the use of molecular fingerprints for larger molecules, such as peptides, can be a computationally feasible, low-parameter, and versatile alternative to sophisticated deep learning models.

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Code

AGH-ML-and-Chemoinformatics-Group/peptides_molecular_fingerprints_classification officialmentioned on GitHub report
arch4ngel21/scikit-fingerprints mentioned on GitHub report
scikit-fingerprints/peptides_molecular_fingerprints_classification mentioned on GitHub report
scikit-fingerprints/scikit-fingerprints mentioned on GitHub report

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Tasks

Graph ClassificationGraph RegressionProperty Prediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Graph Classification Peptides-func ECFP + LightGBM AP 0.7460 #2 of 44 Archive leaderboard report
Graph Classification Peptides-func TT + LightGBM AP 0.7318 #4 of 44 Archive leaderboard report
Graph Classification Peptides-func RDKit + LightGBM AP 0.7311 #6 of 44 Archive leaderboard report
Graph Regression Peptides-struct ECFP + LightGBM MAE 0.2432 #4 of 39 Archive leaderboard report
Graph Regression Peptides-struct TT + LightGBM MAE 0.2438 #5 of 39 Archive leaderboard report
Graph Regression Peptides-struct RDKit + LightGBM MAE 0.2459 #9 of 39 Archive leaderboard report

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