Name is the name of the experiment, all files will be saved the data directory. Gd.generate(name, target_mass, nligands=X, mol=) The following commands should be followed to run GenDock: import gendock as gd one that contains receptors in a directory named receptors. There is already a default config file named receptor1-config.txt and the parameters are set as: receptor = receptor/receptor.pdbqtĮnsure you run gendock from your working directory i.e. I recommend using AutoDock Tools to find out the suitable grid size and location for your receptor, as well as saving to a. These should be edited with your vina properties, grid size and location etc. pdbqt and the Autodock Vina configuration files must then be named -config.txt. Save your macromolecules/receptors in the receptor directory as. GenDock depends upon the installation of autodock-vina, rdkit, openbabel, and scikit-learn - all of which can be handled by Anaconda and should installed to a single environment. I recommend using Anaconda to setup your python environment. Windows users can install Ubuntu using WSL and use GenDock from there. GenDock is only supported on macOS and Linux.
GenDock is not designed to give highly detailed computational analysis for the ligand-receptor binding, but is designed for high throughput screening, the results of which can then be taken on for further analysis. Machine learning alogrithms are also being designed to aid in the identification process.
GenDock is a Python script that can either randomly generate molecules or use a preexisting library of molecules to screen against a macromolecule (or multiple macromolecules) using AutoDock Vina, to aid in finding new ligands for receptor binding.
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