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

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...

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Published inJournal of computer-aided molecular design Vol. 34; no. 2; pp. 99 - 119
Main Authors 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.
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.02.2020
Springer Nature B.V
Springer
Subjects
Affinity
Amyloid Precursor Protein Secretases - antagonists & inhibitors
Amyloid Precursor Protein Secretases - metabolism
Animal Anatomy
Aspartic Acid Endopeptidases - antagonists & inhibitors
Aspartic Acid Endopeptidases - metabolism
BASIC BIOLOGICAL SCIENCES
Best practice
Biochemistry & molecular biology
Biophysics
Blinded prediction challenge
CAD
Chemistry
Chemistry and Materials Science
Computer aided design
Computer Applications in Chemistry
Computer science
Crystal structure
D3R
Docking
Drug Design
Enzyme Inhibitors - chemistry
Enzyme Inhibitors - pharmacology
Free-energy
Histology
Humans
Identification methods
Ligand ranking
Ligands
Machine Learning
Molecular Docking Simulation
Morphology
Physical Chemistry
Proteins
Scoring
Small Molecule Libraries - chemistry
Small Molecule Libraries - pharmacology
Thermodynamics
Ligand ranking
Docking
Scoring
D3R
Free-energy
Blinded prediction challenge
Online AccessGet full text
ISSN0920-654X
1573-4951
1573-4951
DOI10.1007/s10822-020-00289-y

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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.
AbstractList 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.
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.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.
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.
Author Gaieb, Zied
Gilson, Michael K.
Parks, Conor D.
Jansen, Johanna M.
Bembenek, Scott D.
Shao, Chenghua
Lewis, Richard A.
Amaro, Rommie E.
Chiu, Michael
Yang, Huanwang
Walters, W. Patrick
McGaughey, Georgia
Ameriks, Michael K.
Mirzadegan, Tara
Burley, Stephen K.
AuthorAffiliation 3 Relay Therapeutics, Cambridge, MA 20139
7 Janssen Research & Development, San Diego, CA 92121
6 Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland 4002
1 Drug Design Data Resource, University of California, San Diego, La Jolla, CA 92093
4 Novartis Institutes for BioMedical Research, Emeryville, CA 94608
5 Vertex Pharmaceuticals Inc., 50 Northern Ave, Boston, MA 02210
2 RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903 and San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093
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  organization: Drug Design Data Resource, University of California, San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego
BackLink https://www.ncbi.nlm.nih.gov/pubmed/31974851$$D View this record in MEDLINE/PubMed
https://www.osti.gov/servlets/purl/1803880$$D View this record in Osti.gov
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ID FETCH-LOGICAL-c501t-73aac648056be16556d2cda8e8443550853570f6b5c038e8df241c9469f741323
IEDL.DBID 7X7
ISSN 0920-654X
1573-4951
IngestDate Thu Aug 21 14:02:09 EDT 2025
Thu May 18 22:20:22 EDT 2023
Fri Jul 11 06:58:33 EDT 2025
Fri Jul 25 19:13:44 EDT 2025
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Tue Jul 01 04:24:26 EDT 2025
Fri Feb 21 02:34:02 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 2
Keywords Ligand ranking
Docking
Scoring
D3R
Free-energy
Blinded prediction challenge
Language English
License Terms of use and reuse: academic research for non-commercial purposes, see here for full terms. https://www.springer.com/aam-terms-v1
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c501t-73aac648056be16556d2cda8e8443550853570f6b5c038e8df241c9469f741323
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
SC0019749; 1U01GM111528; R01GM133198; DBI-1832184
USDOE Office of Science (SC)
National Science Foundation (NSF)
National Institutes of Health (NIH)
Shared first authorship
ORCID 0000-0002-9275-9553
0000-0002-3375-1738
0000000292759553
0000000233751738
OpenAccessLink https://www.osti.gov/servlets/purl/1803880
PMID 31974851
PQID 2352892173
PQPubID 54123
PageCount 21
ParticipantIDs pubmedcentral_primary_oai_pubmedcentral_nih_gov_7261493
osti_scitechconnect_1803880
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proquest_journals_2352892173
pubmed_primary_31974851
crossref_citationtrail_10_1007_s10822_020_00289_y
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PublicationSubtitle Incorporating Perspectives in Drug Discovery and Design
PublicationTitle Journal of computer-aided molecular design
PublicationTitleAbbrev J Comput Aided Mol Des
PublicationTitleAlternate J Comput Aided Mol Des
PublicationYear 2020
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Springer Nature B.V
Springer
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SubjectTerms Affinity
Amyloid Precursor Protein Secretases - antagonists & inhibitors
Amyloid Precursor Protein Secretases - metabolism
Animal Anatomy
Aspartic Acid Endopeptidases - antagonists & inhibitors
Aspartic Acid Endopeptidases - metabolism
BASIC BIOLOGICAL SCIENCES
Best practice
Biochemistry & molecular biology
Biophysics
Blinded prediction challenge
CAD
Chemistry
Chemistry and Materials Science
Computer aided design
Computer Applications in Chemistry
Computer science
Crystal structure
D3R
Docking
Drug Design
Enzyme Inhibitors - chemistry
Enzyme Inhibitors - pharmacology
Free-energy
Histology
Humans
Identification methods
Ligand ranking
Ligands
Machine Learning
Molecular Docking Simulation
Morphology
Physical Chemistry
Proteins
Scoring
Small Molecule Libraries - chemistry
Small Molecule Libraries - pharmacology
Thermodynamics
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Title D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies
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Volume 34
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