E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking

@article{Zhang2022E3BindAE,
  title={E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking},
  author={Yang Zhang and Huiyu Cai and Chence Shi and Bozitao Zhong and Jian Tang},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.06069}
}
In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery. This work focuses on blind flexible self-docking, where we aim to predict the positions, orientations and conformations of docked molecules. Traditional physics-based methods usually suffer from inac-curate scoring functions and high inference cost. Recently, data-driven methods based on deep learning techniques are attracting growing interest thanks to their efficiency… 

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References

SHOWING 1-10 OF 49 REFERENCES

TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction

This paper proposes Trigonometry-Aware Neural networKs for binding structure prediction, TANKBind, that builds trigonometry constraint as a vigorous inductive bias into the model and explicitly attends to all possible binding sites for each protein by segmenting the whole protein into functional blocks.

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

A novel and fast fine-tuning model that adjusts torsion angles of a ligand’s rotatable bonds based on closed-form global minima of the von Mises an-gular distance to a given input atomic point cloud, avoiding previous expensive differential evolution strategies for energy minimization.

Highly accurate protein structure prediction with AlphaFold

This work validated an entirely redesigned version of the neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods.

Learning from Protein Structure with Geometric Vector Perceptrons

This work introduces geometric vector perceptrons, which extend standard dense layers to operate on collections of Euclidean vectors and improves over existing classes of architectures, including state-of-the-art graph-based and voxel-based methods.

P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure

P2Rank is a new open source software package for ligand binding site prediction from protein structure that belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation.

The Protein Data Bank

The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.

A geometric deep learning approach to predict binding conformations of bioactive molecules

A method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets and represents an example of how artificial intelligence can be used to improve structure-based drug design.

GNINA 1.0: molecular docking with deep learning

Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined and produces scores that correlate well with the root mean square deviation to the known binding pose.

Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration

This research studied the relation between the search progression and Average Sum of Proximity relative Frequencies (ASoF) of searching threads, which is closely related to the search speed and accuracy.

Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening.

Comparisons to results for the thymidine kinase and estrogen receptors published by Rognan and co-workers show that Glide 2.5 performs better than GOLD 1.1, FlexX 1.8, or DOCK 4.01.