ArticleJournal of computer-aided molecular design2019
Macrocycle modeling in ICM: benchmarking and evaluation in D3R Grand Challenge 4.
Article in Journal of computer-aided molecular design, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Thiadiazole-azetidinone sulfonamide hybrids with antimycobacterial activity supported by structure-based analysis.RSC advances · 2026Article
- Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries.Journal of computer-aided molecular design · 2024Article
- Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.Journal of chemical information and modeling · 2022Review
- Lin_F9: A Linear Empirical Scoring Function for Protein-Ligand Docking.Journal of chemical information and modeling · 2021Article
- AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.Journal of chemical information and modeling · 2021Article
- ClusPro LigTBM: Automated Template-based Small Molecule Docking.Journal of molecular biology · 2020Article
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Authors and funding
3 authors.
Funding
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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.
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