Filters








48 Hits in 7.3 sec

Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4 [article]

Bo Wang, Ho Leung Ng
2019 bioRxiv   pre-print
Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) offered a unique opportunity for designing and testing novel methodology for accurate docking and affinity prediction of ligands in an open and blinded  ...  According to the results released by D3R, we achieved a Spearman's rank correlation coefficient of 0.43(7) for predicting the affinity of 154 ligands.  ...  Introduction The Drug Design Data Resource (D3R) has organized four Grand Challenges (GC) for docking, affinity, and free energy predictions for protein-ligand complexes.  ... 
doi:10.1101/680306 fatcat:k5pjjlo4avfibbqqbwdtjifika

D3R grand challenge 2015: Evaluation of protein–ligand pose and affinity predictions

Symon Gathiaka, Shuai Liu, Michael Chiu, Huanwang Yang, Jeanne A. Stuckey, You Na Kang, Jim Delproposto, Ginger Kubish, James B. Dunbar, Heather A. Carlson, Stephen K. Burley, W. Patrick Walters (+3 others)
2016 Journal of Computer-Aided Molecular Design  
second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses.  ...  The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the  ...  Acknowledgments We thank the National Institutes of Health (NIH) for grant 1U01GM111528 for the Drug Design Data Resource (D3R) and U01 GM086873 to the Community Structure Activity Resource (CSAR).  ... 
doi:10.1007/s10822-016-9946-8 pmid:27696240 pmcid:PMC5562487 fatcat:akvv3ttra5d65msjn62jwntsqm

Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge

Zhaofeng Ye, Matthew P. Baumgartner, Bentley M. Wingert, Carlos J. Camacho
2016 Journal of Computer-Aided Molecular Design  
Pose prediction using our "close" models resulted in average ligand RMSDs of 0.32 Å and 1.6 Å for HSP90 and MAP4K4, respectively, the most accurate models of the community-wide challenge.  ...  The 2015 Drug Design Data Resource (D3R) Grand Challenge provided a unique opportunity to prospectively test optimal strategies for virtual screening in these type of targets: heat shock protein 90 (HSP90  ...  Acknowledgement The authors thank D3R for organizing and evaluating the 2015 Grand Challenge. We are grateful to the OpenEye Scientific for providing an academic license for their software.  ... 
doi:10.1007/s10822-016-9941-0 pmid:27573981 pmcid:PMC5079819 fatcat:65dh5fjdsvah7pe6qrfy7qw5qi

Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R grand challenge

Antonia S.J.S. Mey, Jordi Juárez-Jiménez, Alexis Hennessy, Julien Michel
2016 Bioorganic & Medicinal Chemistry  
Abstract In the framework of the 2015 D3R inaugural grand challenge, blind binding pose and affinity predictions were performed for a set of 180 ligands of the Heat Shock Protein HSP 90- protein, a relevant  ...  Structured as a two-stage contest, the first D3R grand challenge aimed to put different computational approaches to the test to predict binding modes and binding affinities.  ... 
doi:10.1016/j.bmc.2016.07.044 pmid:27485604 fatcat:fjwhzhvypnhjxktorsckpwrksi

Performance evaluation of molecular docking and free energy calculations protocols using the D3R Grand Challenge 4 dataset

Eddy Elisée, Vytautas Gapsys, Nawel Mele, Ludovic Chaput, Edithe Selwa, Bert L. de Groot, Bogdan I. Iorga
2019 Journal of Computer-Aided Molecular Design  
to predict the binding modes and ranking of ligands.  ...  Using the D3R Grand Challenge 4 dataset containing Beta-secretase 1 (BACE) and Cathepsin S (CatS) inhibitors, we have evaluated the performance of our in-house docking workflow that involves in the first  ...  The D3R Grand Challenge 4 was organized in 2018 and was based on two protein targets: cathepsin S (CatS, Fig. 1b) , which was already present in the previous D3R Grand Challenge 3, and beta-secretase  ... 
doi:10.1007/s10822-019-00232-w pmid:31677003 fatcat:h57locrhsnbg3o7cyivy73auee

Large scale free energy calculations for blind predictions of protein–ligand binding: the D3R Grand Challenge 2015

Nanjie Deng, William F. Flynn, Junchao Xia, R. S. K. Vijayan, Baofeng Zhang, Peng He, Ahmet Mentes, Emilio Gallicchio, Ronald M. Levy
2016 Journal of Computer-Aided Molecular Design  
We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90.  ...  None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds.  ...  The D3R Grand Challenge 2015 (GC2015) consists of 180 ligands (147 actives, 33 inactives) targeting the Hsp90 ATP binding site [12] .  ... 
doi:10.1007/s10822-016-9952-x pmid:27562018 pmcid:PMC5869689 fatcat:flkyagqv5fgzlezomzplgntdb4

Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation

Sergei Grudinin, Maria Kadukova, Andreas Eisenbarth, Simon Marillet, Frédéric Cazals
2016 Journal of Computer-Aided Molecular Design  
Predicting binding poses and affinities for protein-ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimationAbstract The 2015 D3R Grand Challenge  ...  Our pose predictions were ranked 3-9 for the HSP90 dataset, depending on the assessment metric.  ...  Petr Popov from MIPT Moscow for the initial analysis of the HSP90 targets.  ... 
doi:10.1007/s10822-016-9976-2 pmid:27718029 fatcat:ofyasi7lzbbazl66nyh4e22jku

Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2

Zeynep Kurkcuoglu, Panagiotis I. Koukos, Nevia Citro, Mikael E. Trellet, J. P. G. L. M. Rodrigues, Irina S. Moreira, Jorge Roel-Touris, Adrien S. J. Melquiond, Cunliang Geng, Jörg Schaarschmidt, Li C. Xue, Anna Vangone (+1 others)
2017 Journal of Computer-Aided Molecular Design  
This simple structure-based binding affinity predictor shows a Kendall's Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups).  ...  Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor.  ...  , and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1007/s10822-017-0049-y pmid:28831657 pmcid:PMC5767195 fatcat:5yvgxm64ajfnheop4e2ogppbci

Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations

Majda Misini Ignjatović, Octav Caldararu, Geng Dong, Camila Muñoz-Gutierrez, Francisco Adasme-Carreño, Ulf Ryde
2016 Journal of Computer-Aided Molecular Design  
Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations. Journal of Computer-Aided Molecular Design, 30(9), 707-730.  ...  Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations Misini Ignjatović, Majda; Caldararu, Octav; Dong, Geng; Muñoz-Gutierrez,  ... 
doi:10.1007/s10822-016-9942-z pmid:27565797 pmcid:PMC5078160 fatcat:5zhysvqf4vesvemorjrsyw4gti

Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4

Andrea Basciu, Panagiotis I. Koukos, Giuliano Malloci, Alexandre M. J. J. Bonvin, Attilio V. Vargiu
2019 Journal of Computer-Aided Molecular Design  
in the 2019 iteration of the Grand Challenge (GC4) organized by the D3R consortium.  ...  We identified at least one pose whose heavy-atoms RMSD was less than 2.5 Å from the native conformation for 16 (80%) and 17 (85%) of the 20 targets using AutoDock and HADDOCK, respectively.  ...  Introduction The Drug Design Data Resource (D3R) 2019 Grand Challenge is the fourth iteration (GC4) of the major docking competition organized by the D3R consortium [1] [2] [3] .  ... 
doi:10.1007/s10822-019-00244-6 pmid:31720895 fatcat:nvfaqtryxvgnhdsu4mdw333ove

Blinded evaluation of cathepsin S inhibitors from the D3RGC3 dataset using molecular docking and free energy calculations

Ludovic Chaput, Edithe Selwa, Eddy Elisée, Bogdan I. Iorga
2018 Journal of Computer-Aided Molecular Design  
We applied this protocol to the D3R Grand Challenge 3 dataset containing cathepsin S (CatS) inhibitors.  ...  As expected, the correct ranking of docking poses is still challenging.  ...  This work was supported by the Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LER-MIT) (Agence Nationale de la Recherche, Grant Number ANR-10-LABX-33), by the JPIAMR transnational  ... 
doi:10.1007/s10822-018-0161-7 pmid:30206740 fatcat:6pp7u2pk75gutnej5njlnyvm5e

Large-scale prediction of binding affinity in protein-small ligand complexes: the PRODIGY-LIG web server

2018 Bioinformatics  
the D3R Grand Challenge 2.  ...  The predictive method, properly readapted for small ligand by making use of atomic instead of residue contacts, has been successfully applied for the blind prediction of 102 protein-ligand complexes during  ...  D3R Grand Challenge 2-Stage 2.  ... 
doi:10.1093/bioinformatics/bty816 pmid:31051038 fatcat:iw5glpm6yrf3xkv7qfwenzpqoy

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking [article]

Jeffrey Wagner, Christoper Churas, Shuai Liu, Robert Swift, Michael Chiu, Chenghua Shao, Victoria Feher, Stephen Burley, Michael Gilson, Rommie Amaro
2018 bioRxiv   pre-print
To address such issues, we have developed the Continuous Evaluation of Ligand Protein Predictions (CELPP), a weekly blinded challenge for automated docking workflows.  ...  Docking calculations can be used to accelerate drug discovery by providing predictions of the poses of candidate ligands bound to a targeted protein.  ...  We thank Torsten Schwede and Jürgen Haas for helpful discussions and for developing the inspirational CAMEO challenge. D3R is supported by NIH grant U01 GM111528 to REA and MKG.  ... 
doi:10.1101/469940 fatcat:u3fros72l5f4devwhoom7v7ayu

Benchmarking ensemble docking methods as a scientific outreach project [article]

Jessie L Gan, Dhruv Kumar, Cynthia Chen, Bryn C Taylor, Benjamin R Jagger, Rommie E Amaro, Christopher T Lee
2020 biorxiv/medrxiv   pre-print
challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations.  ...  Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease,  ...  "D3R Grand Challenge 3: blind pre-651 diction of proteinligand poses and affinity rankings". 652 In: Journal of Computer-Aided Molecular Design 33.Michael K. Ameriks et al.  ... 
doi:10.1101/2020.10.02.324343 fatcat:nkbbaalbdjg6dnq6siucvjiqmi

Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations

Antonia S. J. S. Mey, Jordi Juárez Jiménez, Julien Michel
2017 Journal of Computer-Aided Molecular Design  
latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy calculations.  ...  Keywords D3R · computer-aided drug design · protein-ligand interactions · alchemical free energy calculations The drug design data resource (D3R) consortium organises blinded challenges to address the  ...  The D3R Grand challenge 2 was the second blinded prediction challenge organised by the D3R consortium in this case looking at predicting binding poses, binding affinity ranking, and free energies for a  ... 
doi:10.1007/s10822-017-0083-9 pmid:29134431 pmcid:PMC5767197 fatcat:7j2nmdpz65egzmzntdbxov2goi
« Previous Showing results 1 — 15 out of 48 results