4.5 Article

ViTScore: A Novel Three-Dimensional Vision Transformer Method for Accurate Prediction of ProteinLigand Docking Poses

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Biochemical Research Methods

aPRBind: protein-RNA interface prediction by combining sequence and I-TASSER model-based structural features learned with convolutional neural networks

Yang Liu et al.

Summary: In this study, a novel convolutional neural network-based RNA-binding residue prediction method, aPRBind, was proposed, which outperformed some state-of-the-art ab-initio methods in terms of accuracy. Additionally, aPRBind can provide better predictions for modeled structures with TM-score >= 0.5, and it has a low dependence on the accuracy of the structure model.

BIOINFORMATICS (2021)

Article Biochemical Research Methods

Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes

Yumeng Yan et al.

Summary: DeepHomo, a deep learning model for predicting inter-protein residue-residue contacts, integrates various information sources and achieves high precision, outperforming existing methods on both experimentally determined structures and simulated targets.

BRIEFINGS IN BIOINFORMATICS (2021)

Article Chemistry, Multidisciplinary

Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map

Jianwen Chen et al.

Summary: This study developed a new structure-aware method GraphSol for predicting protein solubility using attentive graph convolutional network, constructing a protein topology attribute graph from the sequence. The model showed superior performance and stability, being the first to utilize GCN for sequence-based protein solubility predictions.

JOURNAL OF CHEMINFORMATICS (2021)

Article Biochemical Research Methods

Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction

Beihong Ji et al.

Summary: Structure-based virtual screenings play an important role in drug discovery projects, but accurately predicting binding affinity and prioritizing ligands remains a challenge. A novel method using ligand-residue interaction profiles to construct machine learning-based prediction models significantly improved screening performance in SBVS. The IP-SFs utilizing a gradient boosting decision tree algorithm with the MIN + GB simulation protocol showed the best overall performance, with significant improvements in various metrics compared to Glide.

BRIEFINGS IN BIOINFORMATICS (2021)

Article Chemistry, Medicinal

DeepBSP-a Machine Learning Method for Accurate Prediction of Protein-Ligand Docking Structures

Jingxiao Bao et al.

Summary: The machine-learning model DeepBSP is capable of predicting the root mean square deviation (rmsd) of a ligand docking pose relative to its native binding pose. Trained on a large dataset, this model demonstrates excellent docking power and is useful in accurately selecting poses closest to their native structures.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)

Article Multidisciplinary Sciences

Highly accurate protein structure prediction with AlphaFold

John Jumper et al.

Summary: Proteins are essential for life, and accurate prediction of their structures is a crucial research problem. Current experimental methods are time-consuming, highlighting the need for accurate computational approaches to address the gap in structural coverage. Despite recent progress, existing methods fall short of atomic accuracy in protein structure prediction.

NATURE (2021)

Article Biochemical Research Methods

RNA inter-nucleotide 3D closeness prediction by deep residual neural networks

Saisai Sun et al.

Summary: Recent studies have shown that deep neural networks can accurately predict inter-residue contact/distance in protein structures, while fewer studies have focused on predicting RNA inter-nucleotide 3D closeness. A new algorithm named RNAcontact, based on deep residual neural networks and utilizing covariance information and predicted secondary structure, was proposed and demonstrated to achieve high precision in predicting RNA inter-nucleotide 3D closeness. These predicted 3D closenesses can provide critical information for RNA structure determination and folding, leading to more accurate models compared to approaches that do not consider 3D closeness.

BIOINFORMATICS (2021)

Article Biochemical Research Methods

Protein contact prediction using metagenome sequence data and residual neural networks

Qi Wu et al.

BIOINFORMATICS (2020)

Article Multidisciplinary Sciences

Improved protein structure prediction using potentials from deep learning

Andrew W. Senior et al.

NATURE (2020)

Article Multidisciplinary Sciences

An open-source drug discovery platform enables ultra-large virtual screens

Christoph Gorgulla et al.

NATURE (2020)

Article Biochemistry & Molecular Biology

AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks

Yongbeom Kwon et al.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2020)

Article Multidisciplinary Sciences

Ultra-large library docking for discovering new chemotypes

Jiankun Lyu et al.

NATURE (2019)

Article Chemistry, Medicinal

AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening

Duc Duy Nguyen et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

Review Biochemistry & Molecular Biology

Molecular Docking: Shifting Paradigms in Drug Discovery

Luca Pinzi et al.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2019)

Article Chemistry, Medicinal

Comparative Assessment of Scoring Functions: The CASF-2016 Update

Minyi Su et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

Article Chemistry, Medicinal

KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

Jose Jimenez et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2018)

Article Biochemical Research Methods

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening

Zixuan Cang et al.

PLOS COMPUTATIONAL BIOLOGY (2018)

Article Biochemical Research Methods

Comprehensive assessment of flexible-ligand docking algorithms: current effectiveness and challenges

Sheng-You Huang

BRIEFINGS IN BIOINFORMATICS (2018)

Article Chemistry, Medicinal

A Hybrid Knowledge-Based and Empirical Scoring Function for Protein-Ligand Interaction: SMoG2016

Theau Debroise et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)

Article Chemistry, Medicinal

Protein-Ligand Scoring with Convolutional Neural Networks

Matthew Ragoza et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)

Article Biochemical Research Methods

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

Zixuan Cang et al.

PLOS COMPUTATIONAL BIOLOGY (2017)

Article Biochemical Research Methods

DeepSite: protein-binding site predictor using 3D-convolutional neural networks

J. Jimenez et al.

BIOINFORMATICS (2017)

Article Biochemical Research Methods

3D deep convolutional neural networks for amino acid environment similarity analysis

Wen Torng et al.

BMC BIOINFORMATICS (2017)

Review Chemistry, Multidisciplinary

Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions

Zhihai Liu et al.

ACCOUNTS OF CHEMICAL RESEARCH (2017)

Article Chemistry, Multidisciplinary

DOCK 6: Impact of New Features and Current Docking Performance

William J. Allen et al.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2015)

Review Biochemistry & Molecular Biology

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Elizabeth Yuriev et al.

JOURNAL OF MOLECULAR RECOGNITION (2015)

Article Biochemical Research Methods

PDB-wide collection of binding data: current status of the PDBbind database

Zhihai Liu et al.

BIOINFORMATICS (2015)

Article Chemistry, Medicinal

Development of the Knowledge-Based and Empirical Combined Scoring Algorithm (KECSA) To Score Protein-Ligand Interactions

Zheng Zheng et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2013)

Article Biochemistry & Molecular Biology

Variability in docking success rates due to dataset preparation

Christopher R. Corbeil et al.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2012)

Article Chemistry, Physical

Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions

Sheng-You Huang et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2010)

Article Chemistry, Multidisciplinary

AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility

Garrett M. Morris et al.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2009)

Article Chemistry, Multidisciplinary

An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function

Sheng-You Huang et al.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2006)

Article Chemistry, Medicinal

Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy

RA Friesner et al.

JOURNAL OF MEDICINAL CHEMISTRY (2004)

Review Biotechnology & Applied Microbiology

Docking and scoring in virtual screening for drug discovery: Methods and applications

DB Kitchen et al.

NATURE REVIEWS DRUG DISCOVERY (2004)

Article Biochemical Research Methods

LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites

CM Venkatachalam et al.

JOURNAL OF MOLECULAR GRAPHICS & MODELLING (2003)