PSLL: Persistent Sheaf Laplacian Learning for Protein-Ligand Binding Affinity Prediction

Abstract

Accurate prediction of protein-ligand binding affinity remains a central challenge in computational drug discovery due to the complex interplay among molecular geometry, physicochemical interactions, and atom-specific charge information. In this work, we introduce a Persistent Sheaf Laplacian learning (PSLL) framework for protein-ligand binding affinity prediction. The proposed approach constructs multiscale topological representations from three-dimensional protein-ligand complexes by incorporating atomic partial charges into sheaf restriction maps over Vietoris-Rips and alpha complex filtrations. To capture chemically diverse protein-ligand interactions, we introduce element-specific and category-specific atom-pair representations within the PSLL framework. Harmonic and non-harmonic spectra extracted from the resulting persistent sheaf Laplacians are used as molecular descriptors. To complement the PSLL-derived molecular representation, we incorporate transformer-based protein embeddings and SMILES-derived ligand descriptors for binding affinity prediction. The scoring power of the proposed multiscale PSLL model is validated against existing state-of-the-art methods on three widely used PDBbind benchmark datasets, including PDBbind-v2007, PDBbind-v2013, and PDBbind-v2016. The computational results indicate that the proposed PSLL model achieves strong predictive performance across benchmark datasets, highlighting its potential as an interpretable and mathematically grounded framework with promising generalizability for molecular machine learning and drug discovery.

PHL: Persistent Hyperdigraph Learning for Protein-Protein Binding Affinity Prediction
Xingjian Xu, Chunmei Wang, Jiahui Chen
arXiv27 Sep 2026PreprintCC BY 4.0Full textBiophysicsarXiv
RA-PLA: Retrieval-augmented graph convolutional networks for protein-ligand binding affinity prediction
Karim Abbasi, Hossein Banadkuki, Reza Sepahvand +1
PLOS One22 Sep 2026Peer-reviewedCC BY 4.0Full textEnzymology & metabolism Proteomics & metabolomicsPLOS
AbLWR: a context-aware listwise ranking framework for antibody-antigen binding affinity prediction via positive-unlabeled learning
Fan Xu, Zhi-An Huang, Haohuai He +6
Bioinformatics23 Oct 2026Peer-reviewedCC BY 4.0Full text1 citedMolecular biology & gene regulationPMC
Multiscale and physically consistent multitask learning for protein melting temperature prediction
Rui Min, Chen Lin, Xiaonuo Teng +4
3 Biotech4 Nov 2026Peer-reviewedLicence not statedFull textMolecular biology & gene regulationPMC
Representation learning for low-data protein fitness prediction using sequence–structure embeddings and graph neural networks
Chanoknun Phunnasorn, Narupat Hongdilokkul, Wasakorn Laesanklang +2
Scientific Reports16 Oct 2026Peer-reviewedCC BY-NC-ND 4.0Full textMolecular biology & gene regulationCrossref

Cite

BibTeX RIS

BibTeX

@misc{zia2026psll,
  title = {{PSLL: Persistent Sheaf Laplacian Learning for Protein-Ligand Binding Affinity Prediction}},
  author = {Mushal Zia and Benjamin Jones and Guo-Wei Wei},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.05475},
  eprint = {2609.05475},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.05475},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - PSLL: Persistent Sheaf Laplacian Learning for Protein-Ligand Binding Affinity Prediction
AU  - Mushal Zia
AU  - Benjamin Jones
AU  - Guo-Wei Wei
PY  - 2026
DA  - 2026/09/01
PB  - arXiv
DO  - 10.48550/arxiv.2609.05475
UR  - https://arxiv.org/abs/2609.05475
AB  - Accurate prediction of protein-ligand binding affinity remains a central challenge in computational drug discovery due to the complex interplay among molecular geometry, physicochemical interactions, and atom-specific charge information. In this work, we introduce a Persistent Sheaf Laplacian learning (PSLL) framework for protein-ligand binding affinity prediction. The proposed approach constructs multiscale topological representations from three-dimensional protein-ligand complexes by incorporating atomic partial charges into sheaf restriction maps over Vietoris-Rips and alpha complex filtrations. To capture chemically diverse protein-ligand interactions, we introduce element-specific and category-specific atom-pair representations within the PSLL framework. Harmonic and non-harmonic spectra extracted from the resulting persistent sheaf Laplacians are used as molecular descriptors. To complement the PSLL-derived molecular representation, we incorporate transformer-based protein embeddings and SMILES-derived ligand descriptors for binding affinity prediction. The scoring power of the proposed multiscale PSLL model is validated against existing state-of-the-art methods on three widely used PDBbind benchmark datasets, including PDBbind-v2007, PDBbind-v2013, and PDBbind-v2016. The computational results indicate that the proposed PSLL model achieves strong predictive performance across benchmark datasets, highlighting its potential as an interpretable and mathematically grounded framework with promising generalizability for molecular machine learning and drug discovery.
ER  -

Abstract

Related

Cite