ArticleNature communications2021
DeepRank: a deep learning framework for data mining 3D protein-protein interfaces.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- Article
- Computational design and translational development of TCR-mimic scFv antibodies for peptide-HLA neoantigen targeting.npj drug discovery · 2026Review
- Evaluating AlphaFold Tools and Related Scoring Functions for Protein-peptide Complex Prediction.Genomics, proteomics & bioinformatics · 2026Article
- TINA: a geometry-aware post-hoc scoring framework for protein-protein interaction.Acta pharmacologica Sinica · 2026Article
- ESpma: A method for assessing biological/non-biological interfaces using point-clouds-based structural features and protein language models.Protein science : a publication of the Protein Society · 2026Article
- DeepRank-Ab: a scoring function for antibody-antigen complexes based on geometric deep learning.Communications biology · 2026Article
- Spectral Graph Features for Reference-free RNA 3D Quality Assessment.bioRxiv : the preprint server for biology · 2026Article
- Beyond contacts: The important role of the support region in protein complex assembly.Protein science : a publication of the Protein Society · 2026Article
- Structure-informed machine learning for drug discovery: a task-centric perspective.Briefings in bioinformatics · 2026Review
- NanoBind: Mechanism-Driven Deep Learning of Nanobody-Antigen Molecular Recognition.Research (Washington, D.C.) · 2026Article
- Learning Universal Representations of Intermolecular Interactions with ATOMICA.bioRxiv : the preprint server for biology · 2025Article
- HADDOCK3: A Modular and Versatile Platform for Integrative Modeling of Biomolecular Complexes.Journal of chemical information and modeling · 2025Article
- Nearl: extracting dynamic features from molecular dynamics trajectories for machine learning tasks.Bioinformatics (Oxford, England) · 2025Article
- Recent advances in deep learning for protein-protein interaction: a review.BioData mining · 2025Review
- A new age in structural S-layer biology: Experimental and in silico milestones.The Journal of biological chemistry · 2025Review
- Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues.Journal of chemical information and modeling · 2025Article
- A comprehensive survey of scoring functions for protein docking models.BMC bioinformatics · 2025Article
- Benchmarking the robustness of the correct identification of flexible 3D objects using common machine learning models.Patterns (New York, N.Y.) · 2025Article
- Artificial intelligence in drug development.Nature medicine · 2025Review
- A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language Models (LLM).Methods in molecular biology (Clifton, N.J.) · 2025Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.