Name
Title | ||
|---|---|---|
AutoDock Bias: improving binding mode prediction and virtual screening using known protein-ligand interactions. |
Abstract | ||
|---|---|---|
The performance of docking calculations can be improved by tuning parameters for the system of interest, e.g. biasing the results towards the formation of relevant protein-ligand interactions, such as known ligand pharmacophore or interaction sites derived from cosolvent molecular dynamics. AutoDock Bias is a straightforward and easy to use script-based method that allows the introduction of different types of user-defined biases for fine-tuning AutoDock4 docking calculations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1093/bioinformatics/btz152 | BIOINFORMATICS |
Field | DocType | Volume |
Protein ligand,Data mining,Computer science,Computational biology,Virtual screening,AutoDock | Journal | 35 |
Issue | ISSN | Citations |
19 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Pablo Arcon | 1 | 2 | 2.08 |
| Carlos P. Modenutti | 2 | 2 | 1.74 |
| Demian Avendano | 3 | 0 | 0.34 |
| Elias D López | 4 | 0 | 1.35 |
| Lucas A Defelipe | 5 | 3 | 3.11 |
| Francesca Alessandra Ambrosio | 6 | 0 | 1.01 |
| Adrian Gustavo Turjanski | 7 | 1 | 2.05 |
| Stefano Forli | 8 | 50 | 8.80 |
| Marcelo A Marti | 9 | 19 | 6.52 |