.png){kind=icon}
.png){kind=icon}
Return
Getting Docking into Shape Using Negative Image-Based Restoring
.png){kind=icon}
OA
AI
DOI：10.1021/acs.jcim.9b00383.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in structure-based drug discovery. To remedy this problem, elaborate rescoring and postprocessing schemes have been developed with a varying degree of success, specificity, and cost. The negative image-based rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets. The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein's ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is directly used in the similarity comparison to rank the explicit docking poses. Here, the PANTHER/ShaEP-based R-NiB methodology is tested with six popular docking softwares, including GLIDE, PLANTS, GOLD, DOCK, AUTODOCK, and AUTODOCK VINA, using five validated benchmark sets. Overall, the results indicate that R-NiB outperforms the default docking scoring consistently and inexpensively, demonstrating that the methodology is ready for wide-scale virtual screening usage.
Keywords:
EMPIRICAL SCORING FUNCTIONS
PROTEIN-LIGAND DOCKING
POSE PREDICTION
DRUG DISCOVERY
MOLECULAR DOCKING
SCREENING METHODS
ACCURATE DOCKING
FORCE-FIELD
BINDING
PHOSPHODIESTERASE-5
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.3
Papers:
9.1K
Citations:
4.0W