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\n
\n
\n\n# ***Auto_AutoDock-GPU***\n\nThis script helps preparing input files for protein-ligand (usually from ZINC db?) docking using AutoDock-GPU in batch mode, as well as merging results and parsing detailed information about ligands.\n\n*[AutoDock-GPU GitHub](https://github.com/ccsb-scripps/AutoDock-GPU)*\n\nYou can use smi or pdbqt formatted ligand files.\n\n
\n\n### Requirements\n```\nAutoDock-GPU\nConda\nautodock-vina\nOpenBabel (2.4.1)\n```\n\nor you can use the preset docker image for this repository that is using the nvidia-docker.\n\n```\ndocker pull jongseopark/auto_autodock-gpu:latest\n```\n\nor If you do not want to pull this docker image, then use the dockerfile in this repository.\n\n```\ndocker build -t image_name:tag ./\n```\n\n### Set conda env. for docking\n```\nconda env create -f requirements.yml\nconda activate autodock_gpu\n```\n\n*Due to the Python dependency problem, I recommend that you compile openbabel (version 2.4.1) manually, not installing it using conda,*\n\n*or please use the docker image I provide.*\n\n
\n
\n\n## How to use\n\n### 1. Download ligand files from ZINC database\n\nYou can download the pdbqt formatted files from the ZINC database *(not described in this repository)*.\n\n*[ZINC database](https://zinc.docking.org)*\n\n(Tranches > 3D > select you want > download as pdbqt)\n\n
\n\n### 2. Generate a protein.maps.fld file that containing the information about docking box\n\n\nIn the case of protein files, you can prepare the maps.fld formatted file of protein structure using MGLtools and Autogrid softwares.\n\nPlease refer to the explanation below.\n\n
\n\nFirst, create a environment of conda and install mgltools and autodock as follows.\n\n conda create -n proteinprep\n\n conda activate proteinprep\n\n conda install -c bioconda mgltools\n conda install -c hcc autodock\n\nNow, when you type **pmv** in the terminal, the GUI program will be opened.\n\n![pmv](https://github.com/jongseo-park/AutoDock-GPU_for_ZINC/blob/master/images/a.png)\n\n\nYou can create a pdbqt formatted file of protein and gpf formatted file containing several parameters for docking grid. Before you begin, I recommend deleting all waters in the pdb file.\n\n Edit > Hydrogen > Add > OK (All hydrogens / noBondOrder / yes)\n\n Click Autodock icon \n\n Grid > Macromolecule > Choose > Select Molecule with a selection > ignore an error (click 'OK')\n\nThen, the new window for saving of pdbqt file will be opened. Please save it to the working directory.\n\n![Save_PDBQT](https://github.com/jongseo-park/AutoDock-GPU_for_ZINC/blob/master/images/c.gif)\n\n
\n\nIn that programv (pmv), you are also able to obtain the gpf formatted file as follows.\n\n Grid > Grid box > set grid you want\n File > Close saving current\n Grid > Output > Save GPF\n\nPlease refer the gif image below. Then, you can save the gpf file for the grid you want.\n\n![Save_GPF](https://github.com/jongseo-park/AutoDock-GPU_for_ZINC/blob/master/images/b.gif)\n\n
\n\nThe pdbqt and gpf files should be located in the ***same directory***.\n\n
\n\nIn the terminal, enter the directory containing pdbqt and gpf files, and use this command.\n\n autogrid4 -p yourprotein.gpf -l yourprotein.glg\n\nNow, finally, the input files for receptor will be created in the same directory. \n\n
\n\n### 3. Directory setting\n\nWhen I run this script, I prepare the directories named `protein` and `ligands`.\n\nIn the `protein` directory, the prepared **fld** formatted file with several **map** files are located,\n\nand in the `ligands` directory, the ligand files formatted in pdbqt are located.\n\n\n Working_dir/\n\n autodock_gpu_for_zinc.py # The script in this repository\n tools /\n ...py\n ...py\n\n protein/ \n protein.maps.fld\n protein.A.map\n protein.C.map\n protein.Br.map\n ...\n\n ligands/\n ligand_1.pdbqt\n ligand_2.pdbqt\n ...\n ligand_n.pdbqt\n\n\n
\n\n### 4. Run docking\n\n*flags*\n\n```\nusage: autodock_gpu_for_zinc.py [-h] [-smi] [-sl] [-p PROTEINPATH] [-l LIGANDPATH] [-v VINAPATH]\n [-ls LISTPATH] [-d RESULTPATH] [-bin AUTODOCKBIN] [--gpu GPU]\n [--qtpath QTPATH] [--np NP] [--fn FN] [--csv CSV] [--splitnum SPLITNUM]\n\nTools for high-throughput docking screening using autodock-gpu\n\noptions:\n -h, --help show this help message and exit\n -smi, --smi\n -sl, --splitligs\n -p PROTEINPATH, --proteinpath PROTEINPATH\n Set the path to maps.fld file for receptor (file)\n -l LIGANDPATH, --ligandpath LIGANDPATH\n Set the path to directory containing ligands (directory)\n -v VINAPATH, --vinapath VINAPATH\n Path to autodock_vina (directory)\n -ls LISTPATH, --listpath LISTPATH\n Path to list.txt (file)\n -d RESULTPATH, --resultpath RESULTPATH\n Set the path to result directory (directory)\n -bin AUTODOCKBIN, --autodockbin AUTODOCKBIN\n Binary file of AutoDock-GPU (bin_file)\n --gpu GPU Which GPU do you use ? (starts at 1)\n --qtpath QTPATH path to pdbqt (for running obabel)\n --np NP Number of cores for execute\n --fn FN A function name to use\n --csv CSV csv file name\n --splitnum SPLITNUM How many divided list files you want ?\n```\n\n
\n\n*Functions*\n\nWhen you type the command as belows, you can find the list of functions.\n\n```\npython3 autodock_gpu_for_zinc.py --fn x\n```\n\n
\n\n```\n\nPlease select/enter the one of functions below and enter it.\n\n - Essential fxns\n 1) splitligs (ligandpath, vinapath) \n 2) listgen (proteinpath, ligandpath, listpath)\n 3) run_docking (listpath, autodockbin, resultpath, gpu)\n 4) dlg2qt (resultpath)\n 5) result2df (resultpath)\n 6) obabel (csv, qtpath, np)\n 7) molproperty (csv, np)\n 8) clusterting (csv, np)\n\n 999) postproc (csv, qtpath, np)\n >>> obabel - molproperty - cutoff - clustering - scatterplot\n\n - Miscellaneous fxns\n 10) smi2pdbqt (ligandpath, np)\n 11) listsplit (listpath, splitnum)\n 12) znparsing (np, csv)\n 13) value_cutoff (csv)\n 14) scatterplot (csv)\n 15) copypdbqt (csv, qtpath)\n\n```\n\nThis script has six tools, `splitligs` / `listgen` / `run_docking` / `dlg2qt` / `result2df` / `postproc`, for docking and organizing results.\n\n
\n\n`splitligs`: Split merged ligand files downloaded from the ZINC database into each file.\n\n`listgen`: Generate the list.txt file that is essential for the running of AutoDock-GPU for batch mode.\n\n`run_docking`: Run the AutoDock-GPU\n\n`dlg2qt`: Convert the dlg formatted files (result files) to the pdbqt files that enable to open in molecular visualization programs.\n\n`result2df`: Organize the highest score among each result into a pandas dataframe and save it to csv file.\n\n`postproc`: Calculate the detailed information about chemicals using rdkit.\n\nYou can use these functions one by one when you enter the specific function name in argument `--fn`,\n\nand if you want to perform protein-ligand docking automatically, \n\nthen you don't need to enter the `--fn` argument.\n\n\n
\n\nFor example ...\n\nIf you want to use only one function such as `splitligs`, not entire process,\n\n```\npython3 autodock_gpu_for_zinc.py \\\n -fn splitligs\n -l ./ligands\n -v /opt/programs/autodock_vina/bin/\n```\n\nor it you want to run the entire process, then use as follows.\n\n```\npython3 autodock_gpu_for_zinc.py \\\n -p ./protein/protein.maps.fld\n -l ./ligands\n -v /opt/programs/autodock_vina/bin/\n -bin /opt/programs/AutoDock-GPU/bin/autodock_gpu_64wi\n -sl # when your ligand file is merged version, then use -sl switch.\n```\n\nWhen you enter the command for entire process, you can see the check list in your terminal window.\n\n```\n* vinapath set to \"/opt/programs/autodock_vina/bin/vina_split\" by user\n* listpath set to \"./list.txt\" by user\n* resultpath automatically set to \"./result\"\n* autodockbin set to \"/opt/programs/AutoDock-GPU/bin/autodock_gpu_64wi\" by user\n \nInput arguments are correct !\n-----------------------------\n[proteinpath] ./protein/protein.maps.fld\n[ligandpath] ./ligands\n[vinapath] /opt/programs/autodock_vina/bin/vina_split\n[listpath] ./list.txt\n[resultpath] ./result\n[autodockbin] /opt/programs/AutoDock-GPU/bin/autodock_gpu_64wi\n[gpu] 1\n[qtpath] None\n[smi_dest] False\n[splitligs_dest] False\n[np] 8\n[fn] \n[csv] results.csv\n[splitnum] 4\n-----------------------------\nPlease check your inputs before continuing !! (y / n):\n\n```\n\nBefore run the script, you can lastly check all of the settings.\n\nIf they are correct, enter `y` in the terminal window, then docking will begin.\n\n
\n\nWhen the job is completed, then you can find several csv files in the working directory\n\nthat contains several detailed information.\n\nFinal version of the csv files will be named to as `result_postproc.csv` and `result_postproc_original.csv`.\n\n`result_postproc_original.csv`: raw data\n`result_postproc.csv`: cut-off data (QED > 0.7 / Lowest_E < -7.0)\n\n\n\n\n
\n\n### 5. etc\n\n#### *Manually parallelize*\nSince the running of AutoDock-GPU is not parallelized, if you want to use multiple gpus,\n\nfirst, you have to split your list.txt file through the `listsplit` function (--fn listsplit).\n\nAt that time, you can specify a split number using the argument `--splitnum`, then the list.txt file will be divided by the given number.\n\n
\n\nFor multiple GPUs, you can specify the GPU number to use through the argument `--gpu` when run the AutoDock-GPU using the function `--fn run_docking`.\n\nKeep in mind that the input number for `--gpu` starts at 1, not 0.\n\n
\n\nCollectively ...\n\n1) Through `listsplit` function, you can divide the list.txt file to several list files such as list_1.txt, list_2.txt, etc.\n\n2) After that, by setting the GPU number using `--gpu` argument, you can manually parallelize the docking.\n\n
\n\n#### *ZINC parsing*\nWhen you use the function `znparsing` which collect serveral chemical information from ZINC database, \n\nyou can set the number of processes to parsing information through the argument `--np`.\n\nThis argument does not mean the number of cores, but it means how many jobs will be running to parsing data from ZINC database.\n\nYou can set this value as a high enough number such as 50, 100, ...\n\nI usually use `--np 60`\n\n- - -\n\nIf you find any bug, please feel free to contact me.","has_starred","hub_user","immutable_tags_settings",{"_75":11,"_76":77},"enabled","rules",[78],".*","is_automated","permissions",{"_82":21,"_83":11,"_84":11},"read","write","admin","user","storage_size",4799995052,"orgData","recentTags",[91],{"_92":44,"_93":94,"_95":96,"_97":98,"_99":100,"_50":118,"_119":94,"_120":33,"_121":56,"_57":122,"_123":124,"_125":109,"_126":118,"_127":66,"_128":21},"content_type","creator",10703569,"full_size",3133578977,"id",255289109,"images",[101],{"_102":103,"_104":105,"_106":107,"_108":109,"_110":111,"_112":113,"_114":107,"_115":-5,"_116":96,"_63":66,"_117":-5},"architecture","amd64","digest","sha256:c920870d6991e84f8534969cb9bcbb81af3d487e3f8f2b9c794a6f4e709bc042","features","","last_pulled","2026-09-30T02:09:05.822000635Z","last_pushed","2022-07-19T13:27:12Z","os","linux","os_features","os_version","size","variant","2022-07-19T13:27:11.838422Z","last_updater","last_updater_username","media_type","latest","repository",17611622,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","showSbxLogoOverride","sbxTrademarkText","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"selectedTag","attestationsUrl","routes/_layout.r.$namespace.$reponame.tags",{"_38":142,"_149":150,"_136":159,"_160":11},{"_40":143,"_42":144,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":145,"_57":35,"_32":33,"_58":59,"_60":44,"_61":62,"_63":64,"_65":66,"_67":-5,"_68":62,"_69":70,"_71":11,"_72":33,"_73":146,"_79":11,"_80":148,"_85":33,"_86":87},[],[44],[56],{"_75":11,"_76":147},[78],{"_82":21,"_83":11,"_84":11},"tags",{"_151":64,"_152":-5,"_153":-5,"_154":155},"count","next","previous","results",[156],{"_92":44,"_93":94,"_95":96,"_97":98,"_99":157,"_50":118,"_119":94,"_120":33,"_121":56,"_57":122,"_123":124,"_125":109,"_126":118,"_127":66,"_128":21},[158],{"_102":103,"_104":105,"_106":107,"_108":109,"_110":111,"_112":113,"_114":107,"_115":-5,"_116":96,"_63":66,"_117":-5},[],"canDeleteTags","actionData","errors"]

jongseopark/auto_autodock-gpu

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