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Performance evaluation of molecular docking and free energy calculations protocols using the D3R Grand Challenge 4 dataset
2019
Journal of Computer-Aided Molecular Design
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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 ...
step the selection of the most suitable docking software for the system of interest based on structural and functional information available in public databases, followed by the docking of the dataset ...
Results and discussion From our participation to previous docking and virtual screening challenges, SAMPL3 (2011) [63] , SAMPL4 (2013) [64] , CSAR (2014) [65] , D3R Grand Challenge (2015) [66] , D3R ...
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
2016
Journal of Computer-Aided Molecular Design
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We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. ...
The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. ...
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
Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge
2016
Journal of Computer-Aided Molecular Design
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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 ...
In the 2010 Community Structure-Activity Resource (CSAR) Exercise, Carlson and collaborators analyzed the performance of different scoring functions on the CSAR-NRC data set [5, 24] . ...
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
D3R grand challenge 2015: Evaluation of protein–ligand pose and affinity predictions
2016
Journal of Computer-Aided Molecular Design
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The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. ...
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
Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R grand challenge
2016
Bioorganic & Medicinal Chemistry
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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 ...
To address this obstacle, the 2015 D3R's (Drug Design Data Resource) inaugural grand challenge was setup by a consortium of academics and pharmaceutical companies to assess the state of the art of molecular ...
doi:10.1016/j.bmc.2016.07.044
pmid:27485604
fatcat:fjwhzhvypnhjxktorsckpwrksi
MathDL: Mathematical deep learning for D3R Grand Challenge 4
[article]
2019
arXiv
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We present the performances of our mathematical deep learning (MathDL) models for D3R Grand Challenge 4 (GC4). ...
It is worthy to mention that our method for docking pose predictions has significantly improved from our previous ones. ...
The latest D3R Grand Challenge 4 (GC4), took place from September 4th 2018 to December 4th, 2018. ...
arXiv:1909.07784v1
fatcat:xk7mgjyhqvclhfeivxx2xsldzq
Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2
2017
Journal of Computer-Aided Molecular Design
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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 ...
Overall, for docking of ligands of diverse chemical series we suggest to perform docking of each of the ligands to a set of multiple receptors that are homologous to the target. ...
The authors also thank Andreas Eisenbarth from the University of Kaiserslautern for the development of docking protocols. ...
doi:10.1007/s10822-017-0062-1
pmid:28913782
fatcat:fi4a2b7aazdw3kwn54j6biyatm
Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization
2018
Frontiers in Chemistry
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Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the ...
In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. ...
The authors thank the National System of High Performance Computing (Sistemas Nacionales de Computación de Alto Rendimiento, SNCAD) and the Computational Centre of High Performance Computing (Centro de ...
doi:10.3389/fchem.2018.00188
pmid:29896472
pmcid:PMC5986912
fatcat:ggq5gvj4xnepfeu6dh67a5kzme
Machine‐learning scoring functions for structure‐based drug lead optimization
2020
Wiley Interdisciplinary Reviews. Computational Molecular Science
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In this review, we analyzed machine-learning SFs for drug lead optimization in the 2015-2019 period. ...
Molecular docking can be used to predict how strongly small-molecule binders and their chemical derivatives bind to a macromolecular target using its available three-dimensional structures. ...
The D3R Grand Challenge 3 28 was a larger blind evaluation and more specific regarding whether methods employ ML or not. ...
doi:10.1002/wcms.1465
fatcat:qnrk4qw3h5gjtcxncourqzhkxe
Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches
2020
Molecules
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Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature. ...
Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid ...
[164] also evaluated in the D3R Grand Challenge 4 an algorithm named SkeleDock to define the binding mode based on the structure of a protein−ligand complex. ...
doi:10.3390/molecules25204723
pmid:33076254
fatcat:4gu5plnjwngr7dez7k3f5mnkre
A review of mathematical representations of biomolecules
[article]
2019
arXiv
pre-print
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Data Resource (D3R) Grand Challenges. ...
We focus the performance analysis on the protein-ligand binding predictions in this review although these methods have had tremendous success in many other applications, such as protein classification, ...
Kaifu Gao for his contribution to our team's pose prediction in D3R Grand Challenge 4. ...
arXiv:1912.04724v1
fatcat:q4rxq55c7bckbcn7hk56lnwmxq
Recent Developments in Linear Interaction Energy Based Binding Free Energy Calculations
2020
Frontiers in Molecular Biosciences
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As such it combines explicit conformational sampling (of the protein-bound and unbound-ligand states) with efficiency in calculating values for the protein-ligand binding free energy ΔG bind . ...
Such proteins pose challenges on ΔG bind computation, which we tackle using a previously introduced statistically weighted LIE scheme. ...
The performances of LIE and the above mentioned AD analysis approach were evaluated in a real-life scenario of a community blind affinity prediction challenge organized by Drug Design Data Resource (D3R ...
doi:10.3389/fmolb.2020.00114
pmid:32626725
pmcid:PMC7311763
fatcat:ztufqo63xrgw3impxurmshf7hu
Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors
2017
Molecules
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One of the key steps in such studies is proper computational reconstruction of actual ligand-receptor or protein-protein interactions, a process called molecular docking. ...
In this review, we focus particularly on innovations in docking to GPCRs. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/molecules22020340
pmid:28241450
fatcat:xyytfxpfcvd43dyoefto4qr5pi
Shape-restrained modelling of protein-small molecule complexes with HADDOCK
[article]
2021
bioRxiv
pre-print
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We have benchmarked the performance of these protocols with a fully unbound version of the widely used DUD-E dataset. ...
In this unbound docking scenario, our template/shape-based docking protocol reaches an overall success rate of 81% on 99 complexes, which is close to the best results reported for bound docking on the ...
Acknowledgments We would like to acknowledge support from the European Union Horizon 2020 projects BioExcel (675728, 823830) and EOSC-hub (777536) and from the Innovative Medicines
Bibliography ...
doi:10.1101/2021.06.10.447890
fatcat:ghqfdwiubzhtzggfsah2ukbqiu
A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community)
2020
F1000Research
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e.g. for human health, drug and protein design. ...
Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms ...
like the PDB from the National Institutes of Health and the National Science Foundation. ...
doi:10.12688/f1000research.20559.1
pmid:32566135
pmcid:PMC7284151
fatcat:4um6orxypbcfxfq6uh7ue56fsm
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