Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges

Guedes, Isabella A., Pereira, Felipe S. S., Dardenne, Laurent E.·2018·Frontiers in Pharmacology

Cites (239)

Items 201–239 of 239

  1. Which three-dimensional characteristics make efficient inhibitors of protein–protein interactions?
    Kuenemann, Mélaine A., Bourbon, Laura M. L., Labbé, Céline M., Villoutreix, Bruno O., Sperandio, Olivier · 2014 · Journal of Chemical Information and Modeling
  2. Scoring Ensembles of Docked Protein:Ligand Interactions for Virtual Lead Optimization
    Janet L. Paulsen, Amy C. Anderson · 2009 · Journal of Chemical Information and Modeling
  3. Evaluating the Potential of Halogen Bonding in Molecular Design: Automated Scaffold Decoration Using the New Scoring Function XBScore
    Markus O. Zimmermann, Andreas Lange, Frank M. Boeckler · 2015 · Journal of Chemical Information and Modeling
  4. Inexpensive Method for Selecting Receptor Structures for Virtual Screening
    Zunnan Huang, Chung F. Wong · 2015 · Journal of Chemical Information and Modeling
  5. Estimating binding constants – The hydrophobic effect and cooperativity
    Dudley Howard Williams, Ben Bardsley · 1999 · Perspectives in Drug Discovery and Design
  6. SeleX-CS: A New Consensus Scoring Algorithm for Hit Discovery and Lead Optimization
    Shay Bar‐Haim, Ayelet Aharon, Tal Ben-Moshe, Yael Marantz, Hanoch Senderowitz · 2009 · Journal of Chemical Information and Modeling
  7. A Hybrid Knowledge-Based and Empirical Scoring Function for Protein–Ligand Interaction: SMoG2016
    Théau Debroise, Eugene I. Shakhnovich, Nicolas Chéron · 2017 · Journal of Chemical Information and Modeling
  8. Ranking power of the SQM/COSMO scoring function on carbonic anhydrase II-inhibitor complexes.
    Pecina, Adam, Brynda, Jiří, Vrzal, Lukáš, Gnanasekaran, Ramachandran, Hořejší, Magdalena, Eyrilmez, Saltuk M., Řezáč, Jan, Lepšík, Martin, Řezáčová, Pavlína, Hobza, Pavel et al. · 2018 · ChemPhysChem
  9. Quantum-mechanics methodologies in drug discovery: applications of docking and scoring in lead optimization.
    Crespo, Alejandro, Rodriguez-Granillo, Agustina, Lim, Victoria T. · 2017 · Current Topics in Medicinal Chemistry
  10. Empirical Scoring Functions for Affinity Prediction of Protein‐ligand Complexes
    Lukas P. Pason, Christoph Sotriffer · 2016 · Molecular Informatics
  11. Lessons Learned over Four Benchmark Exercises from the Community Structure–Activity Resource
    Heather A. Carlson · 2016 · Journal of Chemical Information and Modeling
  12. Ligand Identification Scoring Algorithm (LISA)
    Zheng Zheng, Kenneth M. Merz · 2011 · Journal of Chemical Information and Modeling
  13. Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2
    Matthew P. Baumgartner, David A. Price Evans · 2017 · Journal of Computer-Aided Molecular Design
  14. Importance of protein flexibility in molecular recognition: a case study on Type-I1/2 inhibitors of ALK
    Xiaotian Kong, Huiyong Sun, Peichen Pan, Feng Zhu, Shan Chang, Lei Xu, Youyong Li, Tingjun Hou · 2018 · Physical Chemistry Chemical Physics
  15. Distance Dependent Scoring Function for Describing Protein−Ligand Intermolecular Interactions
    Natalia V. Artemenko · 2008 · Journal of Chemical Information and Modeling
  16. In situ extension as an approach for identifying novel α-amylase inhibitors.
    Numao, Shin, Damager, Iben, Li, Chunmin, Wrodnigg, Tanja M., Begum, Anjuman, Overall, Christopher M., Brayer, Gary D., Withers, Stephen G. · 2004 · Journal of Biological Chemistry
  17. Prospective performance evaluation of selected common virtual screening tools, case study: cyclooxygenase (COX) 1 and 2.
    Kaserer, Teresa, Temml, Veronika, Kutil, Zsofia, Vanek, Tomas, Landa, Premysl, Schuster, Daniela · 2015 · European Journal of Medicinal Chemistry
  18. Free enthalpies of replacing water molecules in protein binding pockets.
    Riniker, Sereina, Barandun, Luzi J., Diederich, François, Krämer, Oliver, Steffen, Andreas, van Gunsteren, Wilfred F. · 2012 · Journal of Computer-Aided Molecular Design
  19. A quantum mechanics-based halogen bonding scoring function for protein-ligand interactions.
    Yang, Zhuo, Liu, Yingtao, Chen, Zhaoqiang, Xu, Zhijian, Shi, Jiye, Chen, Kaixian, Zhu, Weiliang · 2015 · Journal of Molecular Modeling
  20. Consensus scoring model for the molecular docking study of mTOR kinase inhibitor
    Dong‐Dong Li, Xiangfeng Meng, Qiang Wang · 2017 · Journal of Molecular Graphics and Modelling
  21. Binding energy landscape analysis helps to discriminate true hits from high-scoring decoys in virtual screening.
    Wei, Dengguo, Zheng, Hao, Su, Naifang, Deng, Minghua, Lai, Luhua · 2010 · Journal of Chemical Information and Modeling
  22. Structure-Based Consensus Scoring Scheme for Selecting Class A Aminergic GPCR Fragments
    Ádám Andor Kelemen, Róbert Kiss, György G. Ferenczy · 2016 · Journal of Chemical Information and Modeling
  23. Evaluating Docking Methods for Prediction of Binding Affinities of Small Molecules to the G ProteinβγSubunits
    Min-Sun Park, Axel Lionel Dessal, Alan V. Smrcka, Harry A. Stern · 2009 · Journal of Chemical Information and Modeling
  24. Ligand-Receptor Affinities Computed by an Adapted Linear Interaction Model for Continuum Electrostatics and by Protein Conformational Averaging
    Ariane Nunes-Alves, G. M. Arantes · 2014 · Journal of Chemical Information and Modeling
  25. Prospects of Applying Enhanced Semi-Empirical QM Methods for 2101 Virtual Drug Design
    Nusret Duygu Yilmazer, Martin Korth · 2016 · Current Medicinal Chemistry
  26. Computer-aided drug design: time to play with novel chemical matter
    Xavier Barril · 2017 · Expert Opinion on Drug Discovery
  27. Scoring Functions for Protein–Ligand Interactions
    Sotriffer, Christoph · 2012 · Protein‐Ligand Interactions
  28. The Challenge of Affinity Prediction: Scoring Functions for Structure‐Based Virtual Screening
    Christoph Sotriffer, Hans Matter · 2011 · Methods and principles in medicinal chemistry
  29. Carbonic Anhydrase II Inhibitor RA1
    Jiří Brynda, P. Rezacova, Magda Hořejší, Jindřich Fanfrlík · 2017 · Worldwide Protein Data Bank
  30. Check Your Confidence: Size Really <i>Does</i> Matter
    Carlson, Heather A. · 2013 · Journal of Chemical Information and Modeling
  31. Sparse projected-gradient method as a linear-scaling low-memory alternative to diagonalization in self-consistent field electronic structure calculations.
    Birgin, Ernesto G., Martı́nez, J. M., Martı́nez, Leandro, Rocha, Gerd B. · 2013 · Journal of Chemical Theory and Computation
  32. The Basis for Target‐Based Virtual Screening: Protein Structures
    Cole, Jason C., Korb, Oliver, Olsson, Tjelvar S. G., Liebeschuetz, John · 2011 · Methods and principles in medicinal chemistry
  33. Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.
    Gao, Ying-Duo, Hu, Yuan, Crespo, Alejandro, Wang, Deping, Armacost, Kira A., Fells, James I., Fradera, Xavier, Wang, Hongwu, Wang, Huijun, Sherborne, Brad et al. · 2018 · Journal of Computer-Aided Molecular Design
  34. The Impact of Docking Pose Generation Error on the Prediction of Binding Affinity
    Hongjian Li, Kwong‐Sak Leung, Man‐Hon Wong, Pedro J. Ballester · 2015 · Lecture notes in computer science
  35. Can the Energy Gap in the Protein-Ligand Binding Energy Landscape Be Used as a Descriptor in Virtual Ligand Screening?
    A. V. Grigoryan, Hong Wang, Timothy J. Cardozo · 2012 · PLoS ONE
  36. Interaction with specific HSP90 residues as a scoring function: validation in the D3R Grand Challenge 2015.
    Santos-Martins, Diogo · 2016 · Journal of Computer-Aided Molecular Design
  37. Successful Applications of In Silico Approaches for Lead/Drug Discovery
    Andrea Bortolato, Francesca Perruccio, Stefano Moro · 2011 · BENTHAM SCIENCE PUBLISHERS eBooks
  38. Deep learning is competing random forest in computational docking
    Mohamed A. Khamis, Walid E. Gomaa, Basem Galal · 2016 · arXiv (Cornell University)
  39. Editorial: Special Issue on “Evaluation of Computational Methods”
    Terry Richard Stouch · 2008 · Journal of Computer-Aided Molecular Design