Performance of machine-learning scoring functions in structure-based virtual screening
出版年份 2017 全文链接
标题
Performance of machine-learning scoring functions in structure-based virtual screening
作者
关键词
-
出版物
Scientific Reports
Volume 7, Issue 1, Pages -
出版商
Springer Nature
发表日期
2017-04-25
DOI
10.1038/srep46710
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。- Dissecting protein architecture with communication blocks and communicating segment pairs
- (2016) Yasaman Karami et al. BMC BIOINFORMATICS
- Docking Screens for Novel Ligands Conferring New Biology
- (2016) John J. Irwin et al. JOURNAL OF MEDICINAL CHEMISTRY
- A novel features ranking metric with application to scalable visual and bioinformatics data classification
- (2016) Quan Zou et al. NEUROCOMPUTING
- CSM-lig: a web server for assessing and comparing protein–small molecule affinities
- (2016) Douglas E.V. Pires et al. NUCLEIC ACIDS RESEARCH
- Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery
- (2016) Huiyong Sun et al. Scientific Reports
- A Comparative Assessment of Predictive Accuracies of Conventional and Machine Learning Scoring Functions for Protein-Ligand Binding Affinity Prediction
- (2015) Hossam M. Ashtawy et al. IEEE-ACM Transactions on Computational Biology and Bioinformatics
- 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
- Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study
- (2014) Hongjian Li et al. BMC BIOINFORMATICS
- 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
- Drug repositioning by structure-based virtual screening
- (2013) Dik-Lung Ma et al. CHEMICAL SOCIETY REVIEWS
- Discovery of novel potent ΔF508-CFTR correctors that target the nucleotide binding domain
- (2013) Norbert Odolczyk et al. EMBO Molecular Medicine
- How far can virtual screening take us in drug discovery?
- (2013) Supratik Kar et al. Expert Opinion on Drug Discovery
- 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
- Evaluation and Optimization of Virtual Screening Workflows with DEKOIS 2.0 – A Public Library of Challenging Docking Benchmark Sets
- (2013) Matthias R. Bauer 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
- LibD3C: Ensemble classifiers with a clustering and dynamic selection strategy
- (2013) Chen Lin et al. NEUROCOMPUTING
- Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review
- (2012) Tiejun Cheng et al. AAPS Journal
- Recognizing Pitfalls in Virtual Screening: A Critical Review
- (2012) Thomas Scior et al. Journal of Chemical Information and Modeling
- Characterization of Small Molecule Binding. I. Accurate Identification of Strong Inhibitors in Virtual Screening
- (2012) Bo Ding et al. Journal of Chemical Information and Modeling
- Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking
- (2012) Michael M. Mysinger et al. JOURNAL OF MEDICINAL CHEMISTRY
- Support Vector Regression Scoring of Receptor–Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries
- (2011) Liwei Li et al. Journal of Chemical Information and Modeling
- Comments on “Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets”: Significance for the Validation of Scoring Functions
- (2011) Pedro J. Ballester 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
- Rapid Context-Dependent Ligand Desolvation in Molecular Docking
- (2010) Michael M. Mysinger et al. Journal of Chemical Information and Modeling
- Virtual screening: an endless staircase?
- (2010) Gisbert Schneider NATURE REVIEWS DRUG DISCOVERY
- Scoring functions and their evaluation methods for protein–ligand docking: recent advances and future directions
- (2010) Sheng-You Huang et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- DOCK 6: Combining techniques to model RNA-small molecule complexes
- (2009) P. T. Lang et al. RNA
- Community benchmarks for virtual screening
- (2008) John J. Irwin JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Discovery of Novel Human Histamine H4 Receptor Ligands by Large-Scale Structure-Based Virtual Screening
- (2008) Róbert Kiss et al. JOURNAL OF MEDICINAL CHEMISTRY
- SFCscore: Scoring functions for affinity prediction of protein-ligand complexes
- (2008) Christoph A. Sotriffer et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
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