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Literature DB >> 31974851

D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.

Conor D Parks¹, Zied Gaieb¹, Michael Chiu¹, Huanwang Yang^2,3, Chenghua Shao^2,3, W Patrick Walters⁴, Johanna M Jansen⁵, Georgia McGaughey⁶, Richard A Lewis⁷, Scott D Bembenek⁸, Michael K Ameriks⁹, Tara Mirzadegan⁹, Stephen K Burley^2,3, Rommie E Amaro^10,11, Michael K Gilson^12,13.

Abstract

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.

Entities: Chemical Disease Gene Species

Keywords: Blinded prediction challenge; D3R; Docking; Free-energy; Ligand ranking; Scoring

Mesh：

Substances：

Year: 2020 PMID： 31974851 PMCID： PMC7261493 DOI： 10.1007/s10822-020-00289-y

Source DB: PubMed Journal: J Comput Aided Mol Des ISSN： 0920-654X Impact factor: 3.686

83 in total

1. Virtual computational chemistry laboratory--design and description.

Authors: Igor V Tetko; Johann Gasteiger; Roberto Todeschini; Andrea Mauri; David Livingstone; Peter Ertl; Vladimir A Palyulin; Eugene V Radchenko; Nikolay S Zefirov; Alexander S Makarenko; Vsevolod Yu Tanchuk; Volodymyr V Prokopenko
Journal: J Comput Aided Mol Des Date: 2005-06 Impact factor: 3.686

2. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.

Authors: Oleg Trott; Arthur J Olson
Journal: J Comput Chem Date: 2010-01-30 Impact factor: 3.376

3. Development and validation of a genetic algorithm for flexible docking.

Authors: G Jones; P Willett; R C Glen; A R Leach; R Taylor
Journal: J Mol Biol Date: 1997-04-04 Impact factor: 5.469

4. Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor.

Authors: Rui Duan; Xianjin Xu; Xiaoqin Zou
Journal: J Comput Aided Mol Des Date: 2017-11-10 Impact factor: 3.686

5. Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study.

Authors: Manon Réau; Florent Langenfeld; Jean-François Zagury; Matthieu Montes
Journal: J Comput Aided Mol Des Date: 2017-09-14 Impact factor: 3.686

6. K_DEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks.

Authors: José Jiménez; Miha Škalič; Gerard Martínez-Rosell; Gianni De Fabritiis
Journal: J Chem Inf Model Date: 2018-01-29 Impact factor: 4.956

7. CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.

Authors: Heather A Carlson; Richard D Smith; Kelly L Damm-Ganamet; Jeanne A Stuckey; Aqeel Ahmed; Maire A Convery; Donald O Somers; Michael Kranz; Patricia A Elkins; Guanglei Cui; Catherine E Peishoff; Millard H Lambert; James B Dunbar
Journal: J Chem Inf Model Date: 2016-05-17 Impact factor: 4.956

8. CDOCKER and λ-dynamics for prospective prediction in D₃R Grand Challenge 2.

Authors: Xinqiang Ding; Ryan L Hayes; Jonah Z Vilseck; Murchtricia K Charles; Charles L Brooks
Journal: J Comput Aided Mol Des Date: 2017-09-07 Impact factor: 3.686

9. Protein-ligand docking using FFT based sampling: D3R case study.

Authors: Dzmitry Padhorny; David R Hall; Hanieh Mirzaei; Artem B Mamonov; Mohammad Moghadasi; Andrey Alekseenko; Dmitri Beglov; Dima Kozakov
Journal: J Comput Aided Mol Des Date: 2017-11-03 Impact factor: 3.686

10. An improved relaxed complex scheme for receptor flexibility in computer-aided drug design.

Authors: Rommie E Amaro; Riccardo Baron; J Andrew McCammon
Journal: J Comput Aided Mol Des Date: 2008-01-15 Impact factor: 3.686

23 in total

1. CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

Authors: Suzanne Ackloo; Rima Al-Awar; Rommie E Amaro; Cheryl H Arrowsmith; Hatylas Azevedo; Robert A Batey; Yoshua Bengio; Ulrich A K Betz; Cristian G Bologa; John D Chodera; Wendy D Cornell; Ian Dunham; Gerhard F Ecker; Kristina Edfeldt; Aled M Edwards; Michael K Gilson; Claudia R Gordijo; Gerhard Hessler; Alexander Hillisch; Anders Hogner; John J Irwin; Johanna M Jansen; Daniel Kuhn; Andrew R Leach; Alpha A Lee; Uta Lessel; Maxwell R Morgan; John Moult; Ingo Muegge; Tudor I Oprea; Benjamin G Perry; Patrick Riley; Sophie A L Rousseaux; Kumar Singh Saikatendu; Vijayaratnam Santhakumar; Matthieu Schapira; Cora Scholten; Matthew H Todd; Masoud Vedadi; Andrea Volkamer; Timothy M Willson
Journal: Nat Rev Chem Date: 2022-02-15 Impact factor: 34.571

10. SAMPL7 Host-Guest Challenge Overview: assessing the reliability of polarizable and non-polarizable methods for binding free energy calculations.

Authors: Martin Amezcua; Léa El Khoury; David L Mobley
Journal: J Comput Aided Mol Des Date: 2021-01-04 Impact factor: 3.686

D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.

1. Virtual computational chemistry laboratory--design and description.

2. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.

3. Development and validation of a genetic algorithm for flexible docking.

4. Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor.

5. Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study.

6. K_DEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks.

7. CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.

8. CDOCKER and λ-dynamics for prospective prediction in D₃R Grand Challenge 2.

9. Protein-ligand docking using FFT based sampling: D3R case study.

10. An improved relaxed complex scheme for receptor flexibility in computer-aided drug design.

1. CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

2. Lin_F9: A Linear Empirical Scoring Function for Protein-Ligand Docking.

3. Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.

Review 4. Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.

Review 5. Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective.

6. Are 2D fingerprints still valuable for drug discovery?

7. Challenges Encountered Applying Equilibrium and Nonequilibrium Binding Free Energy Calculations.

8. Scaffold Hopping Transformations Using Auxiliary Restraints for Calculating Accurate Relative Binding Free Energies.

Review 9. Open-access data: A cornerstone for artificial intelligence approaches to protein structure prediction.

10. SAMPL7 Host-Guest Challenge Overview: assessing the reliability of polarizable and non-polarizable methods for binding free energy calculations.