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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  
The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016.  ...  second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses.  ...  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

MathDL: Mathematical deep learning for D3R Grand Challenge 4 [article]

Duc Duy Nguyen, Kaifu Gao, Menglun Wang, Guo-Wei Wei
2019 arXiv   pre-print
We present the performances of our mathematical deep learning (MathDL) models for D3R Grand Challenge 4 (GC4).  ...  This challenge involves pose prediction, affinity ranking, and free energy estimation for beta secretase 1 (BACE) as well as affinity ranking and free energy estimation for Cathepsin S (CatS).  ...  Acknowledgments This work was supported in part by NSF Grants DMS-1721024, DMS-1761320, and IIS1900473 and NIH grant GM126189. DDN and GWW are also funded by Bristol-Myers Squibb and Pfizer.  ... 
arXiv:1909.07784v1 fatcat:xk7mgjyhqvclhfeivxx2xsldzq

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  
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  ...  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.  ...  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

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.  ...  of ordered waters and the broad chemical diversity of ligands that can bind at this site.  ...  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

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  
We have estimated the binding affinity of three sets of ligands of the heat-shock protein 90 in the D3R grand challenge blind test competition.  ...  of the predictions from the various methods.  ...  the drug-design data resource (D3R) 2015 grand challenge [23] .  ... 
doi:10.1007/s10822-016-9942-z pmid:27565797 pmcid:PMC5078160 fatcat:5zhysvqf4vesvemorjrsyw4gti

Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges

Duc Duy Nguyen, Zixuan Cang, Kedi Wu, Menglun Wang, Yin Cao, Guo-Wei Wei
2018 Journal of Computer-Aided Molecular Design  
D3R Grand Challenge 2 (GC2) focused on the pose prediction and binding affinity ranking and free energy prediction for Farnesoid X receptor ligands.  ...  learning models for pose and binding affinity prediction and ranking in the last two D3R grand challenges in computer-aided drug design and discovery.  ...  , we report the performance of our mathematical deep learning models on pose and binding affinity prediction and ranking in the last two D3R grand challenges, namely D3R Grand Challenge 2 (GC2) and D3R  ... 
doi:10.1007/s10822-018-0146-6 pmid:30116918 fatcat:xvxlbafp4jhbxhhjmib44mdufm

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  
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  ...  to predict the binding modes and ranking of ligands.  ...  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

Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2

Maria Kadukova, Sergei Grudinin
2017 Journal of Computer-Aided Molecular Design  
The 2016 D3R Grand Challenge 2 provided an opportunity to test multiple protein-ligand docking protocols on a set of ligands bound to farnesoid X receptor that has many available experimental structures  ...  We participated in the Stage 1 of the Challenge devoted to the docking pose predictions, with the mean RMSD value of our submission poses of 2.9Å.  ...  This work was partially supported by the Ministry of Education and Science of the Russian Federation (No. 6.3157.2017/PP).  ... 
doi:10.1007/s10822-017-0062-1 pmid:28913782 fatcat:fi4a2b7aazdw3kwn54j6biyatm

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

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.  ...  Considering the size and conformational flexibility of ligands, the docking calculations afforded reasonable overall pose predictions, which are however dependent on the specific nature of each ligand.  ...  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

Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization

Maria Kadukova, Sergei Grudinin
2017 Journal of Computer-Aided Molecular Design  
We assess the Convex-PL scoring function using data from D3R Grand Challenge 2 submissions and the docking test of the CASF 2013 study.  ...  Also, for the training set we do not generate false poses with molecular docking packages, but use constant RMSD rigid-body deformations of the ligands inside the binding pockets.  ...  Acknowledgement The authors thank Georgy Cheremovskiy from Moscow Institute of Physics and Technology for the initial development of the potential, and Georgy Derevyanko from Concordia University who proposed  ... 
doi:10.1007/s10822-017-0068-8 pmid:28921375 fatcat:rjfyheml4vb57psk4otwi4vduq

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Isabella A. Guedes, Felipe S. S. Pereira, Laurent E. Dardenne
2018 Frontiers in Pharmacology  
Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments.  ...  Empirical scoring functions are widely used for pose and affinity prediction.  ...  The broad profile of the D3R Grand Challenges, regarding chemical space diversity and affinity data carefully collected, makes their datasets one of the more reliable sources to evaluate docking and scoring  ... 
doi:10.3389/fphar.2018.01089 pmid:30319422 pmcid:PMC6165880 fatcat:johy46r7pzfclbpigg6kqpmqza

Predicting protein–ligand binding modes for CELPP and GC3: workflows and insight

Xianjin Xu, Zhiwei Ma, Rui Duan, Xiaoqin Zou
2019 Journal of Computer-Aided Molecular Design  
In the present study, these methods were integrated, automated, and systematically tested using the large-scale data from Continuous Evaluation of Ligand Pose Prediction (CELPP) and a subset of Grand challenge  ...  We have developed several methods for protein-ligand binding mode prediction during the participation in the D3R challenges.  ...  The computations were performed on the high performance computing infrastructure supported by NSF CNS-1429294 (PI: Chi-Ren Shyu) and the HPC resources supported by the University of Missouri Bioinformatics  ... 
doi:10.1007/s10822-019-00185-0 pmid:30689079 pmcid:PMC6494980 fatcat:x2u4af6wuvhdnj2xaufwiwxetm

Machine‐learning scoring functions for structure‐based drug lead optimization

Hongjian Li, Kam‐Heung Sze, Gang Lu, Pedro J. Ballester
2020 Wiley Interdisciplinary Reviews. Computational Molecular Science  
A classical SF assumes a predetermined theory-inspired functional form for the relationship between the features characterizing the structure of the protein-ligand complex and its predicted binding affinity  ...  Scoring functions (SFs) are employed to rank these molecules by their predicted binding affinity (potency).  ...  [26] [27] [28] The D3R Grand Challenge 2 27 presented a blind evaluation of methods to predict the affinities of molecules against the nuclear receptor Farnesoid X receptor (FXR).  ... 
doi:10.1002/wcms.1465 fatcat:qnrk4qw3h5gjtcxncourqzhkxe
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