A Multi-perspective Model for Protein–Ligand-Binding Affinity Prediction
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A Multi-perspective Model for Protein–Ligand-Binding Affinity Prediction
Authors
Keywords
-
Journal
Interdisciplinary Sciences-Computational Life Sciences
Volume 15, Issue 4, Pages 696-709
Publisher
Springer Science and Business Media LLC
Online
2023-10-10
DOI
10.1007/s12539-023-00582-y
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Compound–protein interaction prediction by deep learning: Databases, descriptors and models
- (2022) Bing-Xue Du et al. DRUG DISCOVERY TODAY
- DeepDTAF: a deep learning method to predict protein–ligand binding affinity
- (2021) Kaili Wang et al. BRIEFINGS IN BIOINFORMATICS
- Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction
- (2021) JunJie Wee et al. Journal of Chemical Information and Modeling
- InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein–Ligand Interaction Predictions
- (2021) Dejun Jiang et al. JOURNAL OF MEDICINAL CHEMISTRY
- Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction
- (2020) Mohammad A. Rezaei et al. IEEE-ACM Transactions on Computational Biology and Bioinformatics
- An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking
- (2019) Jin Li et al. Interdisciplinary Sciences-Computational Life Sciences
- Molecular Docking: Shifting Paradigms in Drug Discovery
- (2019) Luca Pinzi et al. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
- Predicting Drug–Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation
- (2019) Jaechang Lim et al. Journal of Chemical Information and Modeling
- Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism
- (2019) Zhaoping Xiong et al. JOURNAL OF MEDICINAL CHEMISTRY
- OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction
- (2019) Liangzhen Zheng et al. ACS Omega
- Development and evaluation of a deep learning model for protein–ligand binding affinity prediction
- (2018) Marta M Stepniewska-Dziubinska et al. BIOINFORMATICS
- OUP accepted manuscript
- (2018) BIOINFORMATICS
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
- (2018) Tian Xie et al. PHYSICAL REVIEW LETTERS
- Comparative Assessment of Scoring Functions: The CASF-2016 Update
- (2018) Minyi Su et al. Journal of Chemical Information and Modeling
- Protein–Ligand Scoring with Convolutional Neural Networks
- (2017) Matthew Ragoza et al. Journal of Chemical Information and Modeling
- Machine learning in computational docking
- (2015) Mohamed A. Khamis et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- Molecular Docking and Structure-Based Drug Design Strategies
- (2015) Leonardo Ferreira et al. MOLECULES
- A Machine Learning-Based Method To Improve Docking Scoring Functions and Its Application to Drug Repurposing
- (2011) Sarah L. Kinnings et al. Journal of Chemical Information and Modeling
- A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
- (2010) Pedro J. Ballester et al. BIOINFORMATICS
- NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes
- (2010) Jacob D. Durrant et al. Journal of Chemical Information and Modeling
- AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
- (2009) Oleg Trott et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now