Imagen de Portada

Systematic evaluation of computational tools to predict the effects of mutations on protein-ligand binding affinity in the absence of experimental structures

Fuente: Zenodo

Guardado en:

Detalles Bibliográficos
Autor principal:	Pan, Qisheng
Formato:	Recurso digital
Publicado:	Zenodo 2025
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Systematic evaluation of computational tools to predict the effects of mutations on protein-ligand binding affinity in the absence of experimental structures
por: Pan, Qisheng
Publicado: (2025)

Mayer-homology learning prediction of protein-ligand binding affinities
por: Feng, Hongsong, et al.
Publicado: (2024)

Improved prediction of ligand-protein binding affinities by meta-modeling
por: Lee, Ho-Joon, et al.
Publicado: (2023)

Persistent local Laplacian prediction of protein-ligand binding affinities
por: Liu, Jian, et al.
Publicado: (2026)

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning
por: Li, Krinos, et al.
Publicado: (2025)

Simultaneous Labeling and Binding affinity prediction for protein-ligand structures
por: Ranganath, Aditya, et al.
Publicado: (2025)

ERL‐ProLiGraph: Enhanced representation learning on protein‐ligand graph structured data for binding affinity prediction
por: Gloria Geine Paendong, et al.
Publicado: (2024)

SG‐ML‐PLAP: A structure‐guided machine learning–based scoring function for protein–ligand binding affinity prediction
por: Sapna Pal, et al.
Publicado: (2024)

CAML: Commutative algebra machine learning -- a case study on protein-ligand binding affinity prediction
por: Feng, Hongsong, et al.
Publicado: (2025)

Enhancing protein‐ligand binding affinity prediction through sequential fusion of graph and convolutional neural networks
por: Yimin Yang, et al.
Publicado: (2024)

A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery
por: Domingo, L., et al.
Publicado: (2023)

Two‐conformer equilibrium of maltose‐binding protein in the absence of ligand from residual dipolar coupling analysis
por: Yang Shen, et al.
Publicado: (2025)

Improving protein-ligand binding prediction for shallow binding sites by incorporating interfacial water molecules
por: Bayarsaikhan, Bayartsetseg, et al.
Publicado: (2026)

Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges
por: Tobias Harren, et al.
Publicado: (2024)

Predicting mutational effects on protein binding from folding energy
por: Deng, Arthur, et al.
Publicado: (2025)

Uncertainty quantification of receptor ligand binding sites prediction
por: Chen, Nanjie, et al.
Publicado: (2024)

Unlocking GPCR–ligand interactions: Measuring binding affinities with thermal shift assay
por: Dmitrii Dashevskii, et al.
Publicado: (2025)

VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data
por: Zhang, Liang, et al.
Publicado: (2025)

Can AI-predicted complexes teach machine learning to compute drug binding affinity?
por: Hsu, Wei-Tse, et al.
Publicado: (2025)

Functional thermodynamics govern the ligand binding to human cytosolic transport proteins
por: Sebastian Michler, et al.
Publicado: (2026)

GenShin:geometry-enhanced structural graph embodies binding pose can better predicting compound-protein interaction affinity
por: Zhu, Pingfei, et al.
Publicado: (2025)

Solvation free energy in governing equations for DNA hybridization, protein–ligand binding, and protein folding
por: Caroline Harmon, et al.
Publicado: (2024)

MAGPIE: An interactive tool for visualizing and analyzing protein–ligand interactions
por: Daniel C. Pineda Rodriguez, et al.
Publicado: (2024)

Assessing interaction recovery of predicted protein-ligand poses
por: Errington, David, et al.
Publicado: (2024)

An evaluation of machine learning/molecular mechanics end-state corrections with mechanical embedding to calculate relative protein-ligand binding free energies
por: Karwounopoulos, Johannes, et al.
Publicado: (2024)

Benchmarking AlphaFold3's protein-protein complex accuracy and machine learning prediction reliability for binding free energy changes upon mutation
por: Wee, JunJie, et al.
Publicado: (2024)

Development and large-scale benchmarks of a protein--ligand absolute binding free energy toolkit
por: Liu, Yu, et al.
Publicado: (2026)

Toward generalizable structure‐based deep learning models for protein–ligand interaction prediction: Challenges and strategies
por: Seokhyun Moon, et al.
Publicado: (2024)

Docking guidance with experimental ligand structural density improves docking pose prediction and virtual screening performance
por: Althea T. Hansel‐Harris, et al.
Publicado: (2025)

On fine-tuning Boltz-2 for protein-protein affinity prediction
por: King, James, et al.
Publicado: (2025)

Competitive ligand binding kinetics to linear polymers
por: Villaluenga, Juan P. G., et al.
Publicado: (2024)

Exploring data‐driven chemical SMILES tokenization approaches to identify key protein–ligand binding moieties
por: Asu Busra Temizer, et al.
Publicado: (2024)

Age‐ and sex‐effects on tau PET binding in the absence of beta‐amyloid pathology
por: Diana A Hobbs, et al.
Publicado: (2025)

Synergy of machine learning and density functional theory calculations for predicting experimental Lewis base affinity and Lewis polybase binding atoms
por: Hieu Huynh, et al.
Publicado: (2024)

A perspective on protein structure prediction using quantum computers
por: Doga, Hakan, et al.
Publicado: (2023)

A time‐efficient computational binding affinity estimation protocol with utilization of limited experimental data: A case study for adenosine receptor
por: Ilkwon Cho, et al.
Publicado: (2024)

Revealing protein structures: crystallization of protein‐ligand complexes – co‐crystallization and crystal soaking
por: Barbora Kaščáková, et al.
Publicado: (2024)

Quantifying arsenic-binding affinities of ArsR proteins via biomimetic self-assembly.
por: Cui, Liang, et al.
Publicado: (2026)

Decoding functional impact of epigenetic regulator mutations on ligand–receptor interaction perturbations for evaluation of cancer immunotherapy
por: Aiai Shi, et al.
Publicado: (2024)

A comment on the interpretation of ligand binding experiments for agonists
por: James P. Higham
Publicado: (2025)