Name
Abstract | ||
|---|---|---|
Macrocycles represent a potentially vast extension of drug chemical space still largely untapped by synthetic compounds. Sampling of flexible rings is incorporated in the ICM-dock protocol. We tested the ability of ICM-dock to reproduce macrocyclic ligand–protein receptor complexes, first in a large retrospective benchmark (246 complexes), and next, in context of the D3R Grand Challenge 4 (GC4), where we modeled bound complexes and predicted activities for a series of macrocyclic BACE inhibitors. Sub-angstrom accuracy was achieved in ligand pose prediction both in cross-docking (D3R Challenge Stage 1A) and cognate (Stage 1B) setup. Stage 1B submission was top ranked by mean and average RMSDs, even though no ligand knowledge was used in our simulations on this Stage. Furthermore, we demonstrate successful receptor conformational selection in Stage 1A, aided by the enhanced ‘4D’ multiple receptor conformation docking protocol with optimized scoring offsets. In the activity 3D QSAR modeling, predictivity of the BACE pKd model was modest, while for the second target (Cathepsin-S), leading performance was achieved. Difference in activity prediction performance between the targets is likely explained by the amount of available and relevant training data. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10822-019-00225-9 | Journal of Computer-Aided Molecular Design |
Keywords | Field | DocType |
D3R, Docking, Macrocycles, ICM, Internal coordinate mechanics, LigBEnD | Training set,Quantitative structure–activity relationship,Ranking,Docking (dog),Computational chemistry,Chemistry,Computational biology,Chemical space,Benchmarking | Journal |
Volume | Issue | ISSN |
33 | 12 | 0920-654X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Polo C-H Lam | 1 | 0 | 0.34 |
| Ruben Abagyan | 2 | 430 | 55.44 |
| Maxim Totrov | 3 | 269 | 31.59 |