New machine learning and physics-based scoring functions for drug discovery
Published 2021 View Full Article
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Title
New machine learning and physics-based scoring functions for drug discovery
Authors
Keywords
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Journal
Scientific Reports
Volume 11, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-02-04
DOI
10.1038/s41598-021-82410-1
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Note: Only part of the references are listed.- Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein–Peptide Data Set
- (2020) Karina B. Santos 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
- Structure- and Ligand-Based Virtual Screening on DUD-E+: Performance Dependence on Approximations to the Binding Pocket
- (2020) Ann E. Cleves et al. Journal of Chemical Information and Modeling
- The impact of compound library size on the performance of scoring functions for structure-based virtual screening
- (2020) Louison Fresnais et al. BRIEFINGS IN BIOINFORMATICS
- Machine learning classification can reduce false positives in structure-based virtual screening
- (2020) Yusuf O. Adeshina et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking
- (2019) Jin Li et al. Interdisciplinary Sciences-Computational Life Sciences
- 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
- Predicting Complexation Performance between Cyclodextrins and Guest Molecules by Integrated Machine learning and Molecular Modeling Techniques
- (2019) Qianqian Zhao et al. Acta Pharmaceutica Sinica B
- Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods
- (2019) Dingyan Wang et al. Frontiers in Pharmacology
- Optimized Virtual Screening Workflow: Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease
- (2018) Val Oliveira Pintro et al. COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
- Evaluation of AutoDock and AutoDock Vina on the CASF-2013 benchmark
- (2018) Thomas Gaillard Journal of Chemical Information and Modeling
- KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
- (2018) José Jiménez et al. Journal of Chemical Information and Modeling
- Assessing protein–ligand interaction scoring functions with the CASF-2013 benchmark
- (2018) Yan Li et al. Nature Protocols
- Decoys Selection in Benchmarking Datasets: Overview and Perspectives
- (2018) Manon Réau et al. Frontiers in Pharmacology
- Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges
- (2018) Isabella A. Guedes et al. Frontiers in Pharmacology
- Analysis of solvent-exposed and buried co-crystallized ligands: a case study to support the design of novel protein–protein interaction inhibitors
- (2018) Daniela Trisciuzzi et al. DRUG DISCOVERY TODAY
- Comparative Assessment of Scoring Functions: The CASF-2016 Update
- (2018) Minyi Su et al. Journal of Chemical Information and Modeling
- RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy
- (2018) Stephen K Burley et al. NUCLEIC ACIDS RESEARCH
- Protein–Ligand Empirical Interaction Components for Virtual Screening
- (2017) Yuna Yan et al. Journal of Chemical Information and Modeling
- Task-Specific Scoring Functions for Predicting Ligand Binding Poses and Affinity and for Screening Enrichment
- (2017) Hossam M. Ashtawy et al. Journal of Chemical Information and Modeling
- Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening
- (2017) Spencer S. Ericksen et al. Journal of Chemical Information and Modeling
- AMMOS2: a web server for protein–ligand–water complexes refinement via molecular mechanics
- (2017) Céline M. Labbé et al. NUCLEIC ACIDS RESEARCH
- Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors
- (2017) David Lagorce et al. Scientific Reports
- Performance of machine-learning scoring functions in structure-based virtual screening
- (2017) Maciej Wójcikowski et al. Scientific Reports
- Docking and Scoring with Target-Specific Pose Classifier Succeeds in Native-Like Pose Identification But Not Binding Affinity Prediction in the CSAR 2014 Benchmark Exercise
- (2016) Regina Politi et al. Journal of Chemical Information and Modeling
- Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest
- (2016) Cheng Wang et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Empirical Scoring Functions for Affinity Prediction of Protein-ligand Complexes
- (2016) Lukas P. Pason et al. Molecular Informatics
- Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power
- (2016) Zhe Wang et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Rocker: Open source, easy-to-use tool for AUC and enrichment calculations and ROC visualization
- (2016) Sakari Lätti et al. Journal of Cheminformatics
- Function-specific virtual screening for GPCR ligands using a combined scoring method
- (2016) Albert J. Kooistra et al. Scientific Reports
- Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest
- (2015) Hongjian Li et al. MOLECULES
- iPPI-DB: an online database of modulators of protein–protein interactions
- (2015) Céline M. Labbé et al. NUCLEIC ACIDS RESEARCH
- Optimizing the affinity and specificity of ligand binding with the inclusion of solvation effect
- (2015) Zhiqiang Yan et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening
- (2015) Qurrat Ul Ain et al. Wiley Interdisciplinary Reviews-Computational Molecular Science
- PDB-wide collection of binding data: current status of the PDBbind database
- (2014) Zhihai Liu et al. BIOINFORMATICS
- TS-Chemscore, a Target-Specific Scoring Function, Significantly Improves the Performance of Scoring in Virtual Screening
- (2014) Wen-Jing Wang et al. Chemical Biology & Drug Design
- A dynamic niching genetic algorithm strategy for docking highly flexible ligands
- (2014) Camila Silva de Magalhães et al. INFORMATION SCIENCES
- 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
- Beware of Machine Learning-Based Scoring Functions—On the Danger of Developing Black Boxes
- (2014) Joffrey Gabel et al. Journal of Chemical Information and Modeling
- Which Three-Dimensional Characteristics Make Efficient Inhibitors of Protein–Protein Interactions?
- (2014) Mélaine A. Kuenemann et al. Journal of Chemical Information and Modeling
- Protoss: a holistic approach to predict tautomers and protonation states in protein-ligand complexes
- (2014) Stefan Bietz et al. Journal of Cheminformatics
- Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments
- (2013) G. Madhavi Sastry et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Importance of Polar Solvation and Configurational Entropy for Design of Antiretroviral Drugs Targeting HIV-1 Protease
- (2013) Parimal Kar et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Chemical and structural lessons from recent successes in protein–protein interaction inhibition (2P2I)
- (2011) Xavier Morelli et al. CURRENT OPINION IN CHEMICAL BIOLOGY
- PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKaPredictions
- (2011) Mats H. M. Olsson et al. Journal of Chemical Theory and Computation
- Inclusion of Solvation and Entropy in the Knowledge-Based Scoring Function for Protein−Ligand Interactions
- (2010) Sheng-You Huang et al. Journal of Chemical Information and Modeling
- Designing Focused Chemical Libraries Enriched in Protein-Protein Interaction Inhibitors using Machine-Learning Methods
- (2010) Christelle Reynès et al. PLoS Computational Biology
- Targeted scoring functions for virtual screening
- (2009) Markus H.J. Seifert DRUG DISCOVERY TODAY
- Comparative Assessment of Scoring Functions on a Diverse Test Set
- (2009) Tiejun Cheng et al. Journal of Chemical Information and Modeling
- Robust optimization of scoring functions for a target class
- (2009) Markus H. J. Seifert JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Recent advances in implicit solvent-based methods for biomolecular simulations
- (2008) Jianhan Chen et al. CURRENT OPINION IN STRUCTURAL BIOLOGY
- Surrogate AutoShim: Predocking into a Universal Ensemble Kinase Receptor for Three Dimensional Activity Prediction, Very Quickly, without a Crystal Structure
- (2008) Eric J. Martin et al. Journal of Chemical Information and Modeling
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