Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses
出版年份 2021 全文链接
标题
Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses
作者
关键词
-
出版物
Journal of Chemical Information and Modeling
Volume -, Issue -, Pages -
出版商
American Chemical Society (ACS)
发表日期
2021-09-02
DOI
10.1021/acs.jcim.1c00096
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors
- (2021) Surendra Kumar et al. Journal of Cheminformatics
- GNINA 1.0: molecular docking with deep learning
- (2021) Andrew T. McNutt et al. Journal of Cheminformatics
- Current Trends, Overlooked Issues, and Unmet Challenges in Virtual Screening
- (2020) Dagmar Stumpfe et al. Journal of Chemical Information and Modeling
- Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?
- (2020) Minyi Su et al. Journal of Chemical Information and Modeling
- Combining Docking Pose Rank and Structure with Deep Learning Improves Protein–Ligand Binding Mode Prediction over a Baseline Docking Approach
- (2020) Joseph A. Morrone et al. Journal of Chemical Information and Modeling
- Machine‐learning scoring functions for structure‐based drug lead optimization
- (2020) Hongjian Li et al. Wiley Interdisciplinary Reviews-Computational Molecular Science
- An open-source drug discovery platform enables ultra-large virtual screens
- (2020) Christoph Gorgulla et al. NATURE
- Extended connectivity interaction features: improving binding affinity prediction through chemical description
- (2020) Norberto Sánchez-Cruz et al. BIOINFORMATICS
- In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening
- (2019) Jochen Sieg et al. Journal of Chemical Information and Modeling
- Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data
- (2019) Hongjian Li et al. BIOINFORMATICS
- Learning from the ligand: using ligand-based features to improve binding affinity prediction
- (2019) Fergus Boyles et al. BIOINFORMATICS
- KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
- (2018) José Jiménez et al. Journal of Chemical Information and Modeling
- Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data?
- (2018) David Ramírez et al. MOLECULES
- Development of a Protein-Ligand Extended Connectivity (PLEC) fingerprint and its application for binding affinity predictions
- (2018) Maciej Wójcikowski et al. BIOINFORMATICS
- Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges
- (2018) Duc Duy Nguyen et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Comparative Assessment of Scoring Functions: The CASF-2016 Update
- (2018) Minyi Su et al. Journal of Chemical Information and Modeling
- Correcting the impact of docking pose generation error on binding affinity prediction
- (2016) Hongjian Li et al. BMC BIOINFORMATICS
- Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest
- (2016) Cheng Wang et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- The ChEMBL database in 2017
- (2016) Anna Gaulton et al. NUCLEIC ACIDS RESEARCH
- Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation
- (2015) Sereina Riniker et al. Journal of Chemical Information and Modeling
- Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets
- (2015) Hongjian Li et al. Molecular Informatics
- Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest
- (2015) Hongjian Li et al. MOLECULES
- Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field
- (2015) Maciej Wójcikowski et al. Journal of Cheminformatics
- Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?
- (2014) Pedro J. Ballester et al. Journal of Chemical Information and Modeling
- Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results
- (2014) Yan Li et al. Journal of Chemical Information and Modeling
- CSAR Benchmark Exercise 2011–2012: Evaluation of Results from Docking and Relative Ranking of Blinded Congeneric Series
- (2013) Kelly L. Damm-Ganamet et al. Journal of Chemical Information and Modeling
- SFCscoreRF: A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein–Ligand Complexes
- (2013) David Zilian et al. Journal of Chemical Information and Modeling
- Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise
- (2013) David Ryan Koes et al. Journal of Chemical Information and Modeling
- Analysis of structure-based virtual screening studies and characterization of identified active compounds
- (2012) Peter Ripphausen et al. Future Medicinal Chemistry
- Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking
- (2012) Michael M. Mysinger et al. JOURNAL OF MEDICINAL CHEMISTRY
- CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes
- (2011) James B. Dunbar et al. Journal of Chemical Information and Modeling
- NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function
- (2011) Jacob D. Durrant et al. Journal of Chemical Information and Modeling
- Open Babel: An open chemical toolbox
- (2011) Noel M O'Boyle et al. Journal of Cheminformatics
- 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
- Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets
- (2010) Christian Kramer et al. Journal of Chemical Information and Modeling
- Extended-Connectivity Fingerprints
- (2010) David Rogers et al. Journal of Chemical Information and Modeling
- Comparative Assessment of Scoring Functions on a Diverse Test Set
- (2009) Tiejun Cheng 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
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