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EquiCPI: SE(3)-Equivariant Geometric Deep Learning for Structure-Aware Prediction of Compound-Protein Interactions
Open accessAbstract
Accurate prediction of compound-protein interactions (CPI) remains a cornerstone challenge in computational drug discovery. While existing sequence-based approaches leverage molecular fingerprints or graph representations, they critically overlook the three-dimensional (3D) structural determinants of binding affinity. To bridge this gap, we present EquiCPI, an end-to-end geometric deep learning framework that synergizes first-principles structural modeling with SE(3)-equivariant neural networks. Our pipeline transforms raw sequences into 3D atomic coordinates via ESMFold for proteins and DiffDock-L for ligands, followed by physics-guided conformer reranking and equivariant feature learning. At its core, EquiCPI employs SE(3)-equivariant message passing over atomic point clouds, preserving symmetry under rotations, translations, and reflections, while hierarchically encoding local interaction patterns through tensor products of spherical harmonics. The proposed model is evaluated on BindingDB (affinity prediction) and DUD-E (virtual screening). EquiCPI achieves performance on par with or exceeding the state-of-the-art deep learning competitors.
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Cites
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Metadata
- Authors
- Nguyen, N.-Q., & Kang, J.
- Affiliations
- Korea University (KR)
- Corresponding author
- Kang, Jaewoo
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2025-07-02
- Volume
- 65
- Issue
- 13
- Pages
- 7252-7262
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W4411963055
- Cited by
- 7
- References
- 20
- Citations by year
- 2026: 6 · 2025: 1
- FWCI
- 3.64
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Korea Health Industry Development Institute (HR22C1302)
- Ministry of Health and Welfare (HR20C0021(3))
- Ministry of Science and ICT, South Korea (RS-2023-00262002)
- National Research Foundation of Korea (NRF-2023R1A2C3004176)
- Institute for Information and Communications Technology Promotion (IITP-2024- 2020-0-01819)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- compound-protein interactions, geometric deep learning, binding affinity prediction, virtual screening, protein structure, ligand structure, spherical harmonics, point clouds, ESMFold
- MeSH terms
- Deep Learning, Ligands, Models, Molecular, Protein Binding, Protein Conformation, Proteins, Drug Discovery
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