mlsanityData to 7 Oct 2026

Effective Protein-Protein Interaction Exploration with PPIretrieval

arXiv 2402.03675 ↗First posted 6 Feb 2024q-bio.BM cs.AI cs.CEcited by 5

Not on the map. Why?

Abstract (arXiv)

Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the challenge has shifted towards effectively navigating the vast complex universe to identify potential PPIs. Herein, we propose PPIretrieval, the first deep learning-based model for protein-protein interaction exploration, which leverages existing PPI data to effectively search for potential PPIs in an embedding space, capturing rich geometric and chemical information of protein surfaces. When provided with an unseen query protein with its associated binding site, PPIretrieval effectively identifies a potential binding partner along with its corresponding binding site in an embedding space, facilitating the formation of protein-protein complexes.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 47 references that are in the map. First posted on arXiv 6 Feb 2024.

Builds on

16 of 47 references.

References · 16

  1. 2018How Powerful are Graph Neural Networks?cited by 4,355
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  3. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,277
  4. 2021E(n) Equivariant Graph Neural Networkscited by 951
  5. 2020PointContrast: Unsupervised Pre-training for 3D Point Cloud Understandingcited by 502
  6. 2022Revisiting Heterophily For Graph Neural Networkscited by 169
  7. 2022Protein Representation Learning by Geometric Structure Pretrainingcited by 161
  8. 2021Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?cited by 82
  9. 2023MUDiff: Unified Diffusion for Complete Molecule Generationcited by 41
  10. 2022Structure-based drug design with geometric deep learningcited by 40
  11. 2023Jigsaw: Learning to Assemble Multiple Fractured Objectscited by 26
  12. 2022High-Order Pooling for Graph Neural Networks with Tensor Decompositioncited by 24
4 more
  1. 2022Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networkscited by 19
  2. 2023Learning to design protein-protein interactions with enhanced generalizationcited by 15
  3. 2021DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Predictioncited by 10
  4. 2023Learning Harmonic Molecular Representations on Riemannian Manifoldcited by 8

Built on by

Cited by year

Citing papers per year: 2024: 5.

5 papers.