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 in | Journal of computer-aided molecular design Vol. 34; no. 2; pp. 99 - 119 |
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Main Authors | , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Cham
Springer International Publishing
01.02.2020
Springer Nature B.V Springer |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | 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 |
ISSN: | 0920-654X 1573-4951 1573-4951 |
DOI: | 10.1007/s10822-020-00289-y |