Application of MM-PBSA Methods in Virtual Screening
暂无分享,去创建一个
Tiziano Tuccinardi | Giulio Poli | Flavio Rizzolio | Carlotta Granchi | T. Tuccinardi | F. Rizzolio | C. Granchi | G. Poli
[1] Flavio Rizzolio,et al. Virtual screening identifies a PIN1 inhibitor with possible antiovarian cancer effects , 2019, Journal of cellular physiology.
[2] Maurizio Fermeglia,et al. Homology Model and Docking-Based Virtual Screening for Ligands of the σ1 Receptor. , 2011, ACS medicinal chemistry letters.
[3] Yoshiaki Nakagawa,et al. Structure-based virtual screening for insect ecdysone receptor ligands using MM/PBSA. , 2019, Bioorganic & medicinal chemistry.
[4] Koji Tsuda,et al. Machine learning accelerates MD-based binding pose prediction between ligands and proteins , 2017, Bioinform..
[5] Sanna P Niinivehmas,et al. Case-specific performance of MM-PBSA, MM-GBSA, and SIE in virtual screening. , 2015, Journal of molecular graphics & modelling.
[6] Walter Filgueira de Azevedo,et al. Optimized Virtual Screening Workflow: Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease. , 2017, Combinatorial chemistry & high throughput screening.
[7] B. Kuhn,et al. Validation and use of the MM-PBSA approach for drug discovery. , 2005, Journal of medicinal chemistry.
[8] F. Kirchhoff,et al. An optimized MM/PBSA virtual screening approach applied to an HIV‐1 gp41 fusion peptide inhibitor , 2011, Proteins.
[9] Tingjun Hou,et al. Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations , 2011, J. Chem. Inf. Model..
[10] P. Kollman,et al. Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of TIBO derivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and MM-PBSA. , 2001, Journal of the American Chemical Society.
[11] Yu-chian Chen. Beware of docking! , 2015, Trends in pharmacological sciences.
[12] Abdulmujeeb T. Onawole,et al. Structure based virtual screening of the Ebola virus trimeric glycoprotein using consensus scoring , 2017, Comput. Biol. Chem..
[13] P. Kollman,et al. Binding of a diverse set of ligands to avidin and streptavidin: an accurate quantitative prediction of their relative affinities by a combination of molecular mechanics and continuum solvent models. , 2000, Journal of medicinal chemistry.
[14] C. Supuran,et al. Development of a Fingerprint-Based Scoring Function for the Prediction of the Binding Mode of Carbonic Anhydrase II Inhibitors , 2018, International journal of molecular sciences.
[15] Douglas R. Houston,et al. Consensus Docking: Improving the Reliability of Docking in a Virtual Screening Context , 2013, J. Chem. Inf. Model..
[16] T. Langer,et al. Binding investigation and preliminary optimisation of the 3-amino-1,2,4-triazin-5(2H)-one core for the development of new Fyn inhibitors , 2018, Journal of enzyme inhibition and medicinal chemistry.
[17] T. Tuccinardi. Docking-based virtual screening: recent developments. , 2009, Combinatorial chemistry & high throughput screening.
[18] John P. Overington,et al. ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..
[19] Hege S. Beard,et al. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. , 2004, Journal of medicinal chemistry.
[20] Stefano Moro,et al. Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview , 2018, Front. Pharmacol..
[21] Xiao Hu,et al. Rescoring Virtual Screening Results with the MM-PBSA Methods: Beware of Internal Dielectric Constants , 2019, J. Chem. Inf. Model..
[22] Lingling Jiang,et al. Pharmacophore-Based Similarity Scoring for DOCK , 2014, The journal of physical chemistry. B.
[23] O. Werz,et al. A Multi‐step Virtual Screening Protocol for the Identification of Novel Non‐acidic Microsomal Prostaglandin E2 Synthase‐1 (mPGES‐1) Inhibitors , 2018, ChemMedChem.
[24] Chao Shen,et al. From machine learning to deep learning: Advances in scoring functions for protein–ligand docking , 2019, WIREs Computational Molecular Science.
[25] Fabrizio Manetti,et al. Construction and Validation of a RET TK Catalytic Domain by Homology Modeling , 2007, J. Chem. Inf. Model..
[26] Irina S Moreira,et al. Computational Alanine Scanning Mutagenesis-An Improved Methodological Approach for Protein-DNA Complexes. , 2013, Journal of chemical theory and computation.
[27] Steven W. Muchmore,et al. POSIT: Flexible Shape-Guided Docking For Pose Prediction , 2015, J. Chem. Inf. Model..
[28] Xiang Li,et al. Statistical analysis of EGFR structures’ performance in virtual screening , 2015, Journal of Computer-Aided Molecular Design.
[29] Mark McGann,et al. FRED Pose Prediction and Virtual Screening Accuracy , 2011, J. Chem. Inf. Model..
[30] Flavio Rizzolio,et al. Identification of New Fyn Kinase Inhibitors Using a FLAP-Based Approach , 2013, J. Chem. Inf. Model..
[31] Michael Levitt,et al. Finite‐difference solution of the Poisson–Boltzmann equation: Complete elimination of self‐energy , 1996, Journal of computational chemistry.
[32] Jianyou Shi,et al. Assessing the performance of docking scoring function, FEP, MM-GBSA, and QM/MM-GBSA approaches on a series of PLK1 inhibitors. , 2017, MedChemComm.
[33] J. Gertsch,et al. Optimization of a Benzoylpiperidine Class Identifies a Highly Potent and Selective Reversible Monoacylglycerol Lipase (MAGL) Inhibitor. , 2019, Journal of medicinal chemistry.
[34] Tiziano Tuccinardi,et al. Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies , 2016, Journal of enzyme inhibition and medicinal chemistry.
[35] Michael M. Mysinger,et al. Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking , 2012, Journal of medicinal chemistry.
[36] Matthew P. Repasky,et al. WScore: A Flexible and Accurate Treatment of Explicit Water Molecules in Ligand-Receptor Docking. , 2016, Journal of medicinal chemistry.
[37] Ryan G. Coleman,et al. ZINC: A Free Tool to Discover Chemistry for Biology , 2012, J. Chem. Inf. Model..
[38] A. Ferrari,et al. Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors. , 2007, Bioorganic & medicinal chemistry.
[39] Yuguang Mu,et al. OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction , 2019, ACS omega.
[40] D. Case,et al. Generalized born models of macromolecular solvation effects. , 2000, Annual review of physical chemistry.
[41] J. Irwin,et al. Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.
[42] P A Kollman,et al. Free energy calculations on dimer stability of the HIV protease using molecular dynamics and a continuum solvent model. , 2000, Journal of molecular biology.
[43] C. Gotti,et al. Design of novel alpha7-subtype-preferring nicotinic acetylcholine receptor agonists: application of docking and MM-PBSA computational approaches, synthetic and pharmacological studies. , 2009, Bioorganic & medicinal chemistry letters.
[44] Junmei Wang,et al. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. , 2019, Chemical reviews.
[45] Garland R. Marshall,et al. An Automated Strategy for Binding-Pose Selection and Docking Assessment in Structure-Based Drug Design , 2016, J. Chem. Inf. Model..
[46] Chris de Graaf,et al. Function-specific virtual screening for GPCR ligands using a combined scoring method , 2016, Scientific Reports.
[47] Tiziano Tuccinardi,et al. Application of a FLAP-Consensus Docking Mixed Strategy for the Identification of New Fatty Acid Amide Hydrolase Inhibitors , 2015, J. Chem. Inf. Model..
[48] Huiyong Sun,et al. Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes. , 2019, Physical chemistry chemical physics : PCCP.
[49] Dariusz Plewczynski,et al. VoteDock: Consensus docking method for prediction of protein–ligand interactions , 2011, J. Comput. Chem..
[50] T. Tuccinardi,et al. Receptor-based virtual screening evaluation for the identification of estrogen receptor β ligands , 2015, Journal of enzyme inhibition and medicinal chemistry.
[51] P. Kollman,et al. Continuum Solvent Studies of the Stability of DNA, RNA, and Phosphoramidate−DNA Helices , 1998 .
[52] G. Degliesposti,et al. Binding Estimation after Refinement, a New Automated Procedure for the Refinement and Rescoring of Docked Ligands in Virtual Screening , 2009, Chemical biology & drug design.
[53] Peter Kollman,et al. Molecular recognition by β-cyclodextrin derivatives: molecular dynamics, free-energy perturbation and molecular mechanics/ Poisson–Boltzmann surface area goals and problems , 2002 .
[54] Kirk E. Hevener,et al. Fragment-Based Drug Discovery Using a Multidomain, Parallel MD-MM/PBSA Screening Protocol , 2013, J. Chem. Inf. Model..
[55] Pedro J. Ballester,et al. Performance of machine-learning scoring functions in structure-based virtual screening , 2017, Scientific Reports.
[56] M. Natália D. S. Cordeiro,et al. CompScore: boosting structure-based virtual screening performance by incorporating docking scoring functions components into consensus scoring , 2019 .