D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies
| Autor: | Johanna M. Jansen, Zied Gaieb, Michael K. Gilson, Michael K. Ameriks, Rommie E. Amaro, Chenghua Shao, Tara Mirzadegan, W. Patrick Walters, Michael Chiu, Scott D. Bembenek, Conor Parks, Georgia B. McGaughey, Huanwang Yang, Stephen K. Burley, Richard A. Lewis |
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| Rok vydání: | 2019 |
| Předmět: |
Design data
Computer science Computational biology Ligands 01 natural sciences Article Machine Learning Small Molecule Libraries 0103 physical sciences Drug Discovery Aspartic Acid Endopeptidases Humans Physical and Theoretical Chemistry Enzyme Inhibitors 010304 chemical physics 0104 chemical sciences Computer Science Applications Molecular Docking Simulation 010404 medicinal & biomolecular chemistry Docking (molecular) Drug Design Thermodynamics Free energies Pose prediction Amyloid Precursor Protein Secretases Protein ligand |
| Zdroj: | J Comput Aided Mol Des |
| ISSN: | 1573-4951 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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