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Flexible protein–protein docking with a multitrack iterative transformer

https://doi.org/10.1002/pro.4862
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Flexible protein–protein docking with a multitrack iterative transformer
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Evaluation of AlphaFold antibody–antigen modeling with implications for improving predictive accuracy
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Protein–Protein Docking with Simultaneous Optimization of Rigid-body Displacement and Side-chain Conformations
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ColabFold: making protein folding accessible to all
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ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction
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Deep graph learning of inter-protein contacts
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A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers
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lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests
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Increasing the efficiency of SAGE adaptor ligation bydirected ligation chemistry
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Protein docking using continuum electrostatics and geometric fit
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Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro
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Protein complex prediction with AlphaFold-Multimer
inferredcites →https://doi.org/10.1101/2023.04.01.535079openalex-snapshot
Deep Learning for Flexible and Site-Specific Protein Docking and Design
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01502openalex-snapshot
Fast end-to-end learning on protein surfaces
inferredcites →https://doi.org/10.1110/ps.0239303openalex-snapshot
Protein–protein docking with a reduced protein model accounting for side‐chain flexibility
inferredcites →https://doi.org/10.1110/ps.26002openalex-snapshot
Electrostatics in protein–protein docking
inferredcites →https://doi.org/10.1126/science.abj8754openalex-snapshot
Accurate prediction of protein structures and interactions using a three-track neural network
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Evolutionary-scale prediction of atomic-level protein structure with a language model
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Deep Learning of High-Order Interactions for Protein Interface Prediction
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Regularized gene selection in cancer microarray meta-analysis
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Protein-protein docking using region-based 3D Zernike descriptors
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Induced fit with replica exchange improves protein complex structure prediction
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DockQ: A Quality Measure for Protein-Protein Docking Models
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Protein interface prediction using graph convolutional networks
inferredcites →https://doi.org/10.3390/ijms11103623openalex-snapshot
SwarmDock and the Use of Normal Modes in Protein-Protein Docking
inferredcites →https://doi.org/10.48550/arxiv.1807.01297openalex-snapshot
End-to-End Learning on 3D Protein Structure for Interface Prediction
inferredcites →https://doi.org/10.48550/arxiv.2111.07786openalex-snapshot
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
inferredcites →https://doi.org/10.48550/arxiv.2205.13594openalex-snapshot
DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning
inferredcites →https://doi.org/10.48550/arxiv.2207.05477openalex-snapshot
HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle
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DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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