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zPoseScore model for accurate and robust protein–ligand docking pose scoring in CASP15

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/anie.201204077openalex-snapshot

Computational Enzyme Design

inferredcites →https://doi.org/10.1002/bit.27618openalex-snapshot

Recent advances in computational methods for biosensor design

inferredcites →https://doi.org/10.1002/jcc.21256openalex-snapshot

AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

inferredcites →https://doi.org/10.1002/jcc.21334openalex-snapshot

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

inferredcites →https://doi.org/10.1002/jcc.24667openalex-snapshot

Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest

inferredcites →https://doi.org/10.1002/pro.3784openalex-snapshot

Cross‐docking benchmark for automated pose and ranking prediction of ligand binding

inferredcites →https://doi.org/10.1002/prot.10465openalex-snapshot

Improved protein–ligand docking using GOLD

inferredcites →https://doi.org/10.1002/prot.26232openalex-snapshot

When homologous sequences meet structural decoys: Accurate contact prediction by tFold in CASP14 —( tFold for CASP14 contact prediction)

inferredcites →https://doi.org/10.1007/978-3-030-58548-8_7openalex-snapshot

Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation

inferredcites →https://doi.org/10.1007/s10822-016-9946-8openalex-snapshot

D3R grand challenge 2015: Evaluation of protein–ligand pose and affinity predictions

inferredcites →https://doi.org/10.1007/s12272-015-0640-5openalex-snapshot

Role of computer-aided drug design in modern drug discovery

inferredcites →https://doi.org/10.1016/j.cbi.2006.12.006openalex-snapshot

Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach

inferredcites →https://doi.org/10.1016/j.cbpa.2013.02.012openalex-snapshot

De novo enzymes by computational design

inferredcites →https://doi.org/10.1016/j.csbj.2020.02.008openalex-snapshot

Exploring the computational methods for protein-ligand binding site prediction

inferredcites →https://doi.org/10.1016/j.isci.2022.105920openalex-snapshot

How good are AlphaFold models for docking-based virtual screening?

inferredcites →https://doi.org/10.1017/cbo9780511816581openalex-snapshot

The Art of Molecular Dynamics Simulation

inferredcites →https://doi.org/10.1021/acs.chemrev.9b00055openalex-snapshot

End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

inferredcites →https://doi.org/10.1021/acs.jcim.0c00075openalex-snapshot

RosENet: Improving Binding Affinity Prediction by Leveraging Molecular Mechanics Energies with an Ensemble of 3D Convolutional Neural Networks

inferredcites →https://doi.org/10.1021/acs.jcim.1c00334openalex-snapshot

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

inferredcites →https://doi.org/10.1021/acs.jcim.1c01512openalex-snapshot

PointSite: A Point Cloud Segmentation Tool for Identification of Protein Ligand Binding Atoms

inferredcites →https://doi.org/10.1021/acs.jcim.2c00127openalex-snapshot

Predicting Protein–Ligand Docking Structure with Graph Neural Network

inferredcites →https://doi.org/10.1021/acs.jcim.2c01219openalex-snapshot

Benchmarking Refined and Unrefined AlphaFold2 Structures for Hit Discovery

inferredcites →https://doi.org/10.1021/acs.jcim.7b00049openalex-snapshot

Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein–Ligand Interactions

inferredcites →https://doi.org/10.1021/acs.jcim.7b00650openalex-snapshot

K DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

inferredcites →https://doi.org/10.1021/acs.jcim.8b00545openalex-snapshot

Comparative Assessment of Scoring Functions: The CASF-2016 Update

inferredcites →https://doi.org/10.1021/acs.jcim.9b00645openalex-snapshot

Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions

inferredcites →https://doi.org/10.1021/acs.jcim.9b00714openalex-snapshot

Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?

inferredcites →https://doi.org/10.1021/acs.jmedchem.1c01830openalex-snapshot

InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein–Ligand Interaction Predictions

inferredcites →https://doi.org/10.1021/acs.jmedchem.2c00991openalex-snapshot

Boosting Protein–Ligand Binding Pose Prediction and Virtual Screening Based on Residue–Atom Distance Likelihood Potential and Graph Transformer

inferredcites →https://doi.org/10.1021/acsomega.9b01997openalex-snapshot

OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction

inferredcites →https://doi.org/10.1021/ci300604zopenalex-snapshot

Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

inferredcites →https://doi.org/10.1021/jm030644sopenalex-snapshot

Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening

inferredcites →https://doi.org/10.1038/nsb0902-646openalex-snapshot

Molecular dynamics simulations of biomolecules

inferredcites →https://doi.org/10.1038/s41586-021-03819-2openalex-snapshot

Highly accurate protein structure prediction with AlphaFold

inferredcites →https://doi.org/10.1038/srep02619openalex-snapshot

A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction

inferredcites →https://doi.org/10.1038/srep03568openalex-snapshot

Hierarchical Nanocomposites of Polyaniline Nanowire Arrays on Reduced Graphene Oxide Sheets for Supercapacitors

inferredcites →https://doi.org/10.1039/b608269fopenalex-snapshot

Molecular mechanics methods for predicting protein–ligand binding

inferredcites →https://doi.org/10.1073/pnas.0707684105openalex-snapshot

A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation

inferredcites →https://doi.org/10.1073/pnas.1114017109openalex-snapshot

On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities

inferredcites →https://doi.org/10.1088/1755-1315/218/1/012143openalex-snapshot

Using LeDock as a docking tool for computational drug design

inferredcites →https://doi.org/10.1093/bib/bbab529openalex-snapshot

OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors

inferredcites →https://doi.org/10.1093/bib/bbac051openalex-snapshot

Improving protein–ligand docking and screening accuracies by incorporating a scoring function correction term

inferredcites →https://doi.org/10.1093/bib/bbac520openalex-snapshot

A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function

inferredcites →https://doi.org/10.1093/bioinformatics/btq112openalex-snapshot

A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

inferredcites →https://doi.org/10.1093/bioinformatics/btt473openalex-snapshot

lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests

inferredcites →https://doi.org/10.1093/bioinformatics/btu626openalex-snapshot

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

inferredcites →https://doi.org/10.1093/bioinformatics/btv082openalex-snapshot

Fast, accurate, and reliable molecular docking with QuickVina 2

inferredcites →https://doi.org/10.1093/bioinformatics/bty374openalex-snapshot

Development and evaluation of a deep learning model for protein–ligand binding affinity prediction

inferredcites →https://doi.org/10.1093/nar/gkab1061openalex-snapshot

AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

inferredcites →https://doi.org/10.1093/nar/gky1049openalex-snapshot

UniProt: a worldwide hub of protein knowledge

inferredcites →https://doi.org/10.1101/2022.03.11.483812openalex-snapshot

An end-to-end deep learning method for rotamer-free protein side-chain packing

inferredcites →https://doi.org/10.1109/cibcb.2012.6217214openalex-snapshot

idock: A multithreaded virtual screening tool for flexible ligand docking

inferredcites →https://doi.org/10.1109/cibcb31374.2013openalex-snapshot

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

inferredcites →https://doi.org/10.1126/sciadv.abc5329openalex-snapshot

Persistent spectral–based machine learning (PerSpect ML) for protein-ligand binding affinity prediction

inferredcites →https://doi.org/10.1186/1471-2105-10-168openalex-snapshot

Fpocket: An open source platform for ligand pocket detection

inferredcites →https://doi.org/10.1186/s13321-018-0285-8openalex-snapshot

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

inferredcites →https://doi.org/10.1186/s13321-019-0362-7openalex-snapshot

DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism

inferredcites →https://doi.org/10.1186/s13321-021-00522-2openalex-snapshot

GNINA 1.0: molecular docking with deep learning

inferredcites →https://doi.org/10.1517/17460441.2015.1032936openalex-snapshot

The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities

inferredcites →https://doi.org/10.15212/amm-2022-0017openalex-snapshot

Structure prediction of the entire proteome of monkeypox variants

inferredcites →https://doi.org/10.3389/fchem.2021.753002openalex-snapshot

OnionNet-2: A Convolutional Neural Network Model for Predicting Protein-Ligand Binding Affinity Based on Residue-Atom Contacting Shells

inferredcites →https://doi.org/10.3390/electronics9081295openalex-snapshot

The k-means Algorithm: A Comprehensive Survey and Performance Evaluation

inferredcites →https://doi.org/10.52202/068431-0525openalex-snapshot

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

inferredcites →https://doi.org/10.7717/peerj.7362openalex-snapshot

DeepBindRG: a deep learning based method for estimating effective protein–ligand affinity

What points at this work

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

inferred← citeshttps://doi.org/10.1002/pro.5097openalex-snapshot

T hermo L ink: Bridging disulfide bonds and enzyme thermostability through database construction and machine learning prediction

inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00669openalex-snapshot

Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling

inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00741openalex-snapshot

Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction

inferred← citeshttps://doi.org/10.1038/s41401-025-01513-xopenalex-snapshot

An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor

inferred← citeshttps://doi.org/10.1093/bib/bbae145openalex-snapshot

A new paradigm for applying deep learning to protein–ligand interaction prediction

inferred← citeshttps://doi.org/10.1093/bib/bbag088openalex-snapshot

Could statistical potential models achieve comparable or better performance than deep learning models?

inferred← citeshttps://doi.org/10.1093/bib/bbag194openalex-snapshot

Discussions on the generalization of HybridSP on more equivalent benchmarks

inferred← citeshttps://doi.org/10.1101/2023.12.17.572051openalex-snapshot

Fully flexible molecular alignment enables accurate ligand structure modelling

inferred← citeshttps://doi.org/10.26599/tst.2025.9010068openalex-snapshot

Evaluation Metrics and Quality Assessment Methods for Predicted Protein Complex Structures

inferred← citeshttps://doi.org/10.3390/molecules29040832openalex-snapshot

Recent Progress of Protein Tertiary Structure Prediction

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