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

Parks, Conor D.; Gaieb, Zied; Chiu, Michael; Yang, Huanwang; Shao, Chenghua; Walters, W. Patrick; Jansen, Johanna M.; McGaughey, Georgia; Lewis, Richard A.; Bembenek, Scott D.; Ameriks, Michael K.; Mirzadegan, Tara; Burley, Stephen K.; Amaro, Rommie E.; Gilson, Michael K.

doi:10.1007/s10822-020-00289-y

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

Journal Article · 22 January 2020 · Journal of Computer-Aided Molecular Design

DOI: https://doi.org/10.1007/s10822-020-00289-y · OSTI ID:1803880

Parks, Conor D. ^[1]; Gaieb, Zied ^[2]; Chiu, Michael ^[2]; Yang, Huanwang ^[3]; Shao, Chenghua ^[3]; Walters, W. Patrick ^[4]; Jansen, Johanna M. ^[5]; McGaughey, Georgia ^[6]; Lewis, Richard A. ^[7]; Bembenek, Scott D. ^[8]; Ameriks, Michael K. ^[9]; Mirzadegan, Tara ^[9]; Burley, Stephen K. ^[3];

^[2];

^[2]

Univ. of California San Diego, La Jolla, CA (United States); OSTI
Univ. of California San Diego, La Jolla, CA (United States)
Rutgers Univ., New Brunswick, NJ (United States). Inst. for Quantitative Biomedicine, RCSB Protein Data Bank; Univ. of California San Diego, La Jolla, CA (United States). San Diego Supercomputing Center
Relay Therapeutics, Cambridge, MA (United States)
Novartis Institutes for BioMedical Research, Emeryville, CA (United States)
Vertex Pharmaceuticals Inc., Boston, MA (United States)
Novartis Pharma AG, Basel (Switzerland). Novartis Institutes for BioMedical Research
Denovicon Therapeutics, San Diego, CA (United States)
Janssen Research & Development, San Diego, CA (United States)

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. Finally, we provide an analysis of the results and discuss insights into determined best practice methods.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Rutgers Univ., Piscataway, NJ (United States)

Sponsoring Organization:: National Institutes of Health (NIH); National Science Foundation (NSF); USDOE Office of Science (SC)

Grant/Contract Number:: SC0019749

OSTI ID:: 1803880

Journal Information:: Journal of Computer-Aided Molecular Design, Journal Name: Journal of Computer-Aided Molecular Design Journal Issue: 2 Vol. 34; ISSN 0920-654X

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

References (95)

Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction Cang, Zixuan; Wei, Guo-Wei International Journal for Numerical Methods in Biomedical Engineering, Vol. 34, Issue 2 https://doi.org/10.1002/cnm.2914	journal	August 2017
AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility Morris, Garrett M.; Huey, Ruth; Lindstrom, William Journal of Computational Chemistry, Vol. 30, Issue 16 https://doi.org/10.1002/jcc.21256	journal	December 2009
AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading Trott, Oleg; Olson, Arthur J. Journal of Computational Chemistry https://doi.org/10.1002/jcc.21334	journal	January 2009
ICM?A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation Abagyan, Ruben; Totrov, Maxim; Kuznetsov, Dmitry Journal of Computational Chemistry, Vol. 15, Issue 5 https://doi.org/10.1002/jcc.540150503	journal	May 1994
e FindSite: Enhanced Fingerprint-Based Virtual Screening Against Predicted Ligand Binding Sites in Protein Models Feinstein, Wei P.; Brylinski, Michal Molecular Informatics, Vol. 33, Issue 2 https://doi.org/10.1002/minf.201300143	journal	February 2014
Improved protein-ligand docking using GOLD Verdonk, Marcel L.; Cole, Jason C.; Hartshorn, Michael J. Proteins: Structure, Function, and Bioinformatics, Vol. 52, Issue 4 https://doi.org/10.1002/prot.10465	journal	August 2003
A hierarchical approach to all-atom protein loop prediction Jacobson, Matthew P.; Pincus, David L.; Rapp, Chaya S. Proteins: Structure, Function, and Bioinformatics, Vol. 55, Issue 2 https://doi.org/10.1002/prot.10613	journal	March 2004
Biased Probability Monte Carlo Conformational Searches and Electrostatic Calculations for Peptides and Proteins Abagyan, Ruben; Totrov, Maxim Journal of Molecular Biology, Vol. 235, Issue 3 https://doi.org/10.1006/jmbi.1994.1052	journal	January 1994
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen Jones, Gareth; Willett, Peter; Glen, Robert C. Journal of Molecular Biology, Vol. 267, Issue 3 https://doi.org/10.1006/jmbi.1996.0897	journal	April 1997
Virtual Computational Chemistry Laboratory – Design and Description Tetko, Igor V.; Gasteiger, Johann; Todeschini, Roberto Journal of Computer-Aided Molecular Design, Vol. 19, Issue 6 https://doi.org/10.1007/s10822-005-8694-y	journal	June 2005
An improved relaxed complex scheme for receptor flexibility in computer-aided drug design Amaro, Rommie E.; Baron, Riccardo; McCammon, J. Andrew Journal of Computer-Aided Molecular Design, Vol. 22, Issue 9 https://doi.org/10.1007/s10822-007-9159-2	journal	January 2008
D3R grand challenge 2015: Evaluation of protein–ligand pose and affinity predictions Gathiaka, Symon; Liu, Shuai; Chiu, Michael Journal of Computer-Aided Molecular Design, Vol. 30, Issue 9 https://doi.org/10.1007/s10822-016-9946-8	journal	September 2016
Ranking docking poses by graph matching of protein–ligand interactions: lessons learned from the D3R Grand Challenge 2 da Silva Figueiredo Celestino Gomes, Priscila; Da Silva, Franck; Bret, Guillaume Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0046-1	journal	August 2017
A cross docking pipeline for improving pose prediction and virtual screening performance Kumar, Ashutosh; Zhang, Kam Y. J. Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0048-z	journal	August 2017
Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2 Kurkcuoglu, Zeynep; Koukos, Panagiotis I.; Citro, Nevia Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0049-y	journal	August 2017
CDOCKER and $$\lambda$$-dynamics for prospective prediction in D3R Grand Challenge 2 Ding, Xinqiang; Hayes, Ryan L.; Vilseck, Jonah Z. Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0050-5	journal	September 2017
Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2 Salmaso, Veronica; Sturlese, Mattia; Cuzzolin, Alberto Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0051-4	journal	August 2017
Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016 Fradera, Xavier; Verras, Andreas; Hu, Yuan Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0053-2	journal	September 2017
Blinded evaluation of farnesoid X receptor (FXR) ligands binding using molecular docking and free energy calculations Selwa, Edithe; Elisée, Eddy; Zavala, Agustin Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0054-1	journal	September 2017
Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2 Rifai, Eko Aditya; van Dijk, Marc; Vermeulen, Nico P. E. Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0055-0	journal	September 2017
Binding affinities of the farnesoid X receptor in the D3R Grand Challenge 2 estimated by free-energy perturbation and docking Olsson, Martin A.; García-Sosa, Alfonso T.; Ryde, Ulf Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0056-z	journal	September 2017
Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach Lam, Polo C. -H.; Abagyan, Ruben; Totrov, Maxim Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0058-x	journal	September 2017
Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2 Kadukova, Maria; Grudinin, Sergei Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0062-1	journal	September 2017
Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study Réau, Manon; Langenfeld, Florent; Zagury, Jean-François Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0063-0	journal	September 2017
Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP+ Schindler, Christina; Rippmann, Friedrich; Kuhn, Daniel Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0064-z	journal	September 2017
Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges Wingert, Bentley M.; Oerlemans, Rick; Camacho, Carlos J. Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0065-y	journal	September 2017
Protein–ligand docking using FFT based sampling: D3R case study Padhorny, Dzmitry; Hall, David R.; Mirzaei, Hanieh Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0069-7	journal	November 2017
Binding pose and affinity prediction in the 2016 D3R Grand Challenge 2 using the Wilma-SIE method Hogues, Hervé; Sulea, Traian; Gaudreault, Francis Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0071-0	journal	October 2017
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 Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0072-z	journal	October 2017
Prediction of binding poses to FXR using multi-targeted docking combined with molecular dynamics and enhanced sampling Bhakat, Soumendranath; Åberg, Emil; Söderhjelm, Pär Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0074-x	journal	October 2017
Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2 Athanasiou, Christina; Vasilakaki, Sofia; Dellis, Dimitris Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0075-9	journal	November 2017
Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2 Baumgartner, Matthew P.; Evans, David A. Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0081-y	journal	November 2017
Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor Duan, Rui; Xu, Xianjin; Zou, Xiaoqin Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0082-x	journal	November 2017
Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations Mey, Antonia S. J. S.; Jiménez, Jordi Juárez; Michel, Julien Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0083-9	journal	November 2017
Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations Yakovenko, Oleksandr; Jones, Steven J. M. Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0085-7	journal	November 2017
D3R Grand Challenge 2: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies Gaieb, Zied; Liu, Shuai; Gathiaka, Symon Journal of Computer-Aided Molecular Design, Vol. 32, Issue 1 https://doi.org/10.1007/s10822-017-0088-4	journal	December 2017
Convolutional neural network scoring and minimization in the D3R 2017 community challenge Sunseri, Jocelyn; King, Jonathan E.; Francoeur, Paul G. Journal of Computer-Aided Molecular Design, Vol. 33, Issue 1 https://doi.org/10.1007/s10822-018-0133-y	journal	July 2018
Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3 Lam, Polo C. -H.; Abagyan, Ruben; Totrov, Maxim Journal of Computer-Aided Molecular Design, Vol. 33, Issue 1 https://doi.org/10.1007/s10822-018-0139-5	journal	August 2018
Shape similarity guided pose prediction: lessons from D3R Grand Challenge 3 Kumar, Ashutosh; Zhang, Kam Y. J. Journal of Computer-Aided Molecular Design, Vol. 33, Issue 1 https://doi.org/10.1007/s10822-018-0142-x	journal	August 2018
Alchemical Grid Dock (AlGDock) calculations in the D3R Grand Challenge 3: Binding free energies between flexible ligands and rigid receptors Xie, Bing; Minh, David D. L. Journal of Computer-Aided Molecular Design, Vol. 33, Issue 1 https://doi.org/10.1007/s10822-018-0143-9	journal	August 2018
Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges Nguyen, Duc Duy; Cang, Zixuan; Wu, Kedi Journal of Computer-Aided Molecular Design, Vol. 33, Issue 1 https://doi.org/10.1007/s10822-018-0146-6	journal	August 2018
Protein–ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3 Koukos, Panagiotis I.; Xue, Li C.; Bonvin, Alexandre M. J. J. Journal of Computer-Aided Molecular Design, Vol. 33, Issue 1 https://doi.org/10.1007/s10822-018-0148-4	journal	August 2018
Blinded evaluation of cathepsin S inhibitors from the D3RGC3 dataset using molecular docking and free energy calculations Chaput, Ludovic; Selwa, Edithe; Elisée, Eddy Journal of Computer-Aided Molecular Design, Vol. 33, Issue 1 https://doi.org/10.1007/s10822-018-0161-7	journal	September 2018
Calculate protein–ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3 He, Xibing; Man, Viet H.; Ji, Beihong Journal of Computer-Aided Molecular Design, Vol. 33, Issue 1 https://doi.org/10.1007/s10822-018-0162-6	journal	September 2018
Monte Carlo on the manifold and MD refinement for binding pose prediction of protein–ligand complexes: 2017 D3R Grand Challenge Ignatov, Mikhail; Liu, Cong; Alekseenko, Andrey Journal of Computer-Aided Molecular Design, Vol. 33, Issue 1 https://doi.org/10.1007/s10822-018-0176-0	journal	November 2018
D3R Grand Challenge 3: blind prediction of protein–ligand poses and affinity rankings Gaieb, Zied; Parks, Conor D.; Chiu, Michael Journal of Computer-Aided Molecular Design, Vol. 33, Issue 1 https://doi.org/10.1007/s10822-018-0180-4	journal	January 2019
Role of computer-aided drug design in modern drug discovery Macalino, Stephani Joy Y.; Gosu, Vijayakumar; Hong, Sunhye Archives of Pharmacal Research, Vol. 38, Issue 9 https://doi.org/10.1007/s12272-015-0640-5	journal	July 2015
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings Lipinski, Christopher A.; Lombardo, Franco; Dominy, Beryl W. Advanced Drug Delivery Reviews, Vol. 23, Issue 1-3 https://doi.org/10.1016/S0169-409X(96)00423-1	journal	January 1997
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings Lipinski, Christopher A.; Lombardo, Franco; Dominy, Beryl W. Advanced Drug Delivery Reviews, Vol. 64 https://doi.org/10.1016/j.addr.2012.09.019	journal	December 2012
Macrocyclic peptidomimetic β-secretase (BACE-1) inhibitors with activity in vivo Machauer, Rainer; Laumen, Kurt; Veenstra, Siem Bioorganic & Medicinal Chemistry Letters, Vol. 19, Issue 5 https://doi.org/10.1016/j.bmcl.2009.01.055	journal	March 2009
Ensemble Docking in Drug Discovery Amaro, Rommie E.; Baudry, Jerome; Chodera, John Biophysical Journal, Vol. 114, Issue 10 https://doi.org/10.1016/j.bpj.2018.02.038	journal	May 2018
Conformer Selection and Induced Fit in Flexible Backbone Protein–Protein Docking Using Computational and NMR Ensembles Chaudhury, Sidhartha; Gray, Jeffrey J. Journal of Molecular Biology, Vol. 381, Issue 4 https://doi.org/10.1016/j.jmb.2008.05.042	journal	September 2008
Alchemical free energy methods for drug discovery: progress and challenges Chodera, John D.; Mobley, David L.; Shirts, Michael R. Current Opinion in Structural Biology, Vol. 21, Issue 2 https://doi.org/10.1016/j.sbi.2011.01.011	journal	April 2011
CSAR Benchmark Exercise 2013: Evaluation of Results from a Combined Computational Protein Design, Docking, and Scoring/Ranking Challenge Smith, Richard D.; Damm-Ganamet, Kelly L.; Dunbar, James B. Journal of Chemical Information and Modeling, Vol. 56, Issue 6 https://doi.org/10.1021/acs.jcim.5b00387	journal	October 2015
CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma Carlson, Heather A.; Smith, Richard D.; Damm-Ganamet, Kelly L. Journal of Chemical Information and Modeling, Vol. 56, Issue 6 https://doi.org/10.1021/acs.jcim.5b00523	journal	May 2016
Lessons Learned over Four Benchmark Exercises from the Community Structure–Activity Resource Carlson, Heather A. Journal of Chemical Information and Modeling, Vol. 56, Issue 6 https://doi.org/10.1021/acs.jcim.6b00182	journal	May 2016
Rigidity Strengthening: A Mechanism for Protein–Ligand Binding Nguyen, Duc D.; Xiao, Tian; Wang, Menglun Journal of Chemical Information and Modeling, Vol. 57, Issue 7 https://doi.org/10.1021/acs.jcim.7b00226	journal	June 2017
Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization Wallach, Izhar; Heifets, Abraham Journal of Chemical Information and Modeling, Vol. 58, Issue 5 https://doi.org/10.1021/acs.jcim.7b00403	journal	April 2018
Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations Cournia, Zoe; Allen, Bryce; Sherman, Woody Journal of Chemical Information and Modeling, Vol. 57, Issue 12 https://doi.org/10.1021/acs.jcim.7b00564	journal	December 2017
K _DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks Jiménez, José; Škalič, Miha; Martínez-Rosell, Gerard Journal of Chemical Information and Modeling, Vol. 58, Issue 2 https://doi.org/10.1021/acs.jcim.7b00650	journal	January 2018
Exhaustive Conformational Sampling of Complex Fused Ring Macrocycles Using Inverse Kinematics Coutsias, Evangelos A.; Lexa, Katrina W.; Wester, Michael J. Journal of Chemical Theory and Computation, Vol. 12, Issue 9 https://doi.org/10.1021/acs.jctc.6b00250	journal	August 2016
Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets Yu, Haoyu S.; Deng, Yuqing; Wu, Yujie Journal of Chemical Theory and Computation, Vol. 13, Issue 12 https://doi.org/10.1021/acs.jctc.7b00885	journal	November 2017
Docking Screens for Novel Ligands Conferring New Biology: Miniperspective Irwin, John J.; Shoichet, Brian K. Journal of Medicinal Chemistry, Vol. 59, Issue 9 https://doi.org/10.1021/acs.jmedchem.5b02008	journal	March 2016
Computation and management of chemical properties in CACTVS: An extensible networked approach toward modularity and compatibility Ihlenfeldt, Wolf Dietrich; Takahashi, Yoshimasa; Abe, Hidetsugu Journal of Chemical Information and Modeling, Vol. 34, Issue 1 https://doi.org/10.1021/ci00017a013	journal	January 1994
Protein Kinases: Docking and Homology Modeling Reliability Tuccinardi, Tiziano; Botta, Maurizio; Giordano, Antonio Journal of Chemical Information and Modeling, Vol. 50, Issue 8 https://doi.org/10.1021/ci100161z	journal	July 2010
Potential and Limitations of Ensemble Docking Korb, Oliver; Olsson, Tjelvar S. G.; Bowden, Simon J. Journal of Chemical Information and Modeling, Vol. 52, Issue 5 https://doi.org/10.1021/ci2005934	journal	April 2012
CSAR Benchmark Exercise 2011–2012: Evaluation of Results from Docking and Relative Ranking of Blinded Congeneric Series Damm-Ganamet, Kelly L.; Smith, Richard D.; Dunbar, James B. Journal of Chemical Information and Modeling, Vol. 53, Issue 8 https://doi.org/10.1021/ci400025f	journal	May 2013
Accuracy Assessment and Automation of Free Energy Calculations for Drug Design Christ, Clara D.; Fox, Thomas Journal of Chemical Information and Modeling, Vol. 54, Issue 1 https://doi.org/10.1021/ci4004199	journal	December 2013
Conformational Analysis of Macrocycles: Finding What Common Search Methods Miss Bonnet, Pascal; Agrafiotis, Dimitris K.; Zhu, Fangqiang Journal of Chemical Information and Modeling, Vol. 49, Issue 10 https://doi.org/10.1021/ci900238a	journal	October 2009
Prediction of Physicochemical Parameters by Atomic Contributions Wildman, Scott A.; Crippen, Gordon M. Journal of Chemical Information and Computer Sciences, Vol. 39, Issue 5 https://doi.org/10.1021/ci990307l	journal	August 1999
Perspective on Free-Energy Perturbation Calculations for Chemical Equilibria Jorgensen, William L.; Thomas, Laura L. Journal of Chemical Theory and Computation, Vol. 4, Issue 6 https://doi.org/10.1021/ct800011m	journal	May 2008
Molecular Properties That Influence the Oral Bioavailability of Drug Candidates Veber, Daniel F.; Johnson, Stephen R.; Cheng, Hung-Yuan Journal of Medicinal Chemistry, Vol. 45, Issue 12 https://doi.org/10.1021/jm020017n	journal	June 2002
Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy Friesner, Richard A.; Banks, Jay L.; Murphy, Robert B. Journal of Medicinal Chemistry, Vol. 47, Issue 7 https://doi.org/10.1021/jm0306430	journal	March 2004
Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening Halgren, Thomas A.; Murphy, Robert B.; Friesner, Richard A. Journal of Medicinal Chemistry, Vol. 47, Issue 7 https://doi.org/10.1021/jm030644s	journal	March 2004
Structure-Based Design and Synthesis of Macroheterocyclic Peptidomimetic Inhibitors of the Aspartic Protease β-Site Amyloid Precursor Protein Cleaving Enzyme (BACE) Hanessian, Stephen; Yang, Gaoqiang; Rondeau, Jean-Michel Journal of Medicinal Chemistry, Vol. 49, Issue 15 https://doi.org/10.1021/jm060154a	journal	June 2006
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
Perspective: Alchemical free energy calculations for drug discovery Mobley, David L.; Klimovich, Pavel V. The Journal of Chemical Physics, Vol. 137, Issue 23 https://doi.org/10.1063/1.4769292	journal	December 2012
Multiscale weighted colored graphs for protein flexibility and rigidity analysis Bramer, David; Wei, Guo-Wei The Journal of Chemical Physics, Vol. 148, Issue 5 https://doi.org/10.1063/1.5016562	journal	February 2018
A new Measure of rank Correlation Kendall, M. G. Biometrika, Vol. 30, Issue 1-2 https://doi.org/10.1093/biomet/30.1-2.81	journal	June 1938
The ChEMBL database in 2017 Gaulton, Anna; Hersey, Anne; Nowotka, Michał Nucleic Acids Research, Vol. 45, Issue D1 https://doi.org/10.1093/nar/gkw1074	journal	November 2016
Identification of a Potent and Selective Noncovalent Cathepsin S Inhibitor Thurmond, Robin L.; Sun, Siquan; Sehon, Clark A. Journal of Pharmacology and Experimental Therapeutics, Vol. 308, Issue 1 https://doi.org/10.1124/jpet.103.056879	journal	October 2003
Computational Methods in Drug Discovery Sliwoski, Gregory; Kothiwale, Sandeepkumar; Meiler, Jens Pharmacological Reviews, Vol. 66, Issue 1 https://doi.org/10.1124/pr.112.007336	journal	December 2013
The Many Roles of Computation in Drug Discovery Jorgensen, W. L. Science, Vol. 303, Issue 5665 https://doi.org/10.1126/science.1096361	journal	March 2004
Derivation of sensitive discrimination potential for virtual ligand screening Totrov, Maxim; Abagyan, Ruben Proceedings of the third annual international conference on Computational molecular biology - RECOMB '99 https://doi.org/10.1145/299432.299509	conference	January 1999
Predicting Binding Free Energies: Frontiers and Benchmarks Mobley, David L.; Gilson, Michael K. Annual Review of Biophysics, Vol. 46, Issue 1 https://doi.org/10.1146/annurev-biophys-070816-033654	journal	May 2017
Open Babel: An open chemical toolbox O'Boyle, Noel M.; Banck, Michael; James, Craig A. Journal of Cheminformatics, Vol. 3, Issue 1 https://doi.org/10.1186/1758-2946-3-33	journal	October 2011
Role of BACE1 in Alzheimer’s synaptic function Das, Brati; Yan, Riqiang Translational Neurodegeneration, Vol. 6, Issue 1 https://doi.org/10.1186/s40035-017-0093-5	journal	August 2017
CRC Standard Probability and Statistics Tables and Formulae Zwillinger, Daniel; Kokoska, Stephen https://doi.org/10.1201/9780367802417	book	December 1999
TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions Cang, Zixuan; Wei, Guo-Wei PLOS Computational Biology, Vol. 13, Issue 7 https://doi.org/10.1371/journal.pcbi.1005690	journal	July 2017
Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening Cang, Zixuan; Mu, Lin; Wei, Guo-Wei PLOS Computational Biology, Vol. 14, Issue 1 https://doi.org/10.1371/journal.pcbi.1005929	journal	January 2018
A New Measure of Rank Correlation Kendall, M. G. Biometrika, Vol. 30, Issue 1/2 https://doi.org/10.2307/2332226	journal	June 1938
The Treatment of Ties in Ranking Problems Kendall, M. G. Biometrika, Vol. 33, Issue 3 https://doi.org/10.2307/2332303	journal	November 1945
Software and resources for computational medicinal chemistry Liao, Chenzhong; Sitzmann, Markus; Pugliese, Angelo Future Medicinal Chemistry, Vol. 3, Issue 8 https://doi.org/10.4155/fmc.11.63	journal	June 2011
Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening Cang, Zixuan; Mu, Lin; Wei, Guowei arXiv https://doi.org/10.48550/arxiv.1708.08135	text	January 2017
Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges Nguyen, Duc Duy; Cang, Zixuan; Wu, Kedi arXiv https://doi.org/10.48550/arxiv.1804.10647	preprint	January 2018

Similar Records

Outcome of the First wwPDB/CCDC/D3R Ligand Validation Workshop

Journal Article · 2016 · Structure · OSTI ID:1260571

Adams, Paul D.; Aertgeerts, Kathleen; Bauer, Cary; +54 more

Protein-ligand binding affinity prediction using multi-instance learning with docking structures

Journal Article · 2025 · Frontiers in Pharmacology · OSTI ID:2516789

Kim, Hyojin; Shim, Heesung; Ranganath, Aditya; +3 more

Aminomethyl-Derived Beta Secretase (BACE1) Inhibitors: Engaging Gly230 without an Anilide Functionality

Journal Article · 2016 · Journal of Medicinal Chemistry · OSTI ID:1506536

Butler, Christopher R.; Ogilvie, Kevin; Martinez-Alsina, Luis; +20 more

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
Biochemistry & molecular biology
Biophysics
Blinded prediction challenge
Computer science
D3R
Docking
Free-energy
Ligand ranking
Scoring

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

Citation Formats

References (95)

Similar Records

Related Subjects