[{"_1":2,"_160":-5,"_161":-5},"loaderData",{"_3":4,"_17":18,"_25":26,"_139":140},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7,"_13":14,"_15":16},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","theme","system","contrast","normal","routes/_layout",{"_19":-5,"_20":21,"_22":21,"_23":11,"_24":21},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_27":28,"_29":-5,"_30":11,"_31":11,"_32":33,"_34":35,"_36":-5,"_37":11,"_38":39,"_88":-5,"_89":90,"_128":-5,"_129":11,"_130":21,"_131":11,"_132":-5,"_133":11,"_134":-5,"_135":136,"_137":91,"_138":-7},"canonicalUrl","https://hub.docker.com/r/jongseopark/autodock_gpu_js/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","jongseopark","reponame","autodock_gpu_js","cloudBuildSource","isAutobuildsAvailable","repo",{"_40":41,"_42":43,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":55,"_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":74,"_79":11,"_80":81,"_85":33,"_86":87},"categories",[],"content_types",[44],"image","date_registered","2021-08-03T09:45:53.954379Z","description","","is_private","last_updated","2021-11-07T02:21:26.355432Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[56],"application/vnd.docker.container.image.v1+json","name","pull_count",200,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","# ***AutoDock-GPU_for_ZINC***\n\n
\n\n*[Link to related GitHub repository](https://github.com/jongseo-park/AutoDock-GPU_for_ZINC)*\n\n
\n\nThis script helps preparing input files for protein-ligand(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\n
\n\nBefore start, the Anaconda *(Miniconda)* and Nvidia-docker must be installed in your system.\n\nIn addition, you have to prepared input files, `protein.maps.fld` and `ligand.pdbqt` files.\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\nIn the case of ligand files, you 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\nThis script has five tools, `splitligs` / `listgen` / `result2df` / `dlg2qt` / `znparsing`, for docking and organizing results.\n\nor you can use `oneclick` tool for using the pipeline automatically.\n\n
\n\n`oneclick`: Run **[splitligs > listgen > result2df > znparsing]** automatically\n\n`splitligs`: Split the merged ligand files downloaded from the ZINC database into each file.\n\n`listgen`: Generate the list.txt file which is essential for the running of AutoDock-GPU for batch mode.\n\n`result2df`: Organize the highest score among each result into a pandas dataframe csv file.\n\n`dlg2qt`: Convert the dlg formatted files (result files) to the pdbqt files that enable to open in pymol.\n\n`znparsing`: Collect the detailed information about ligands from ZINC database using web crawling tool, and organize into the csv file.\n\n\n- - -\n\n
\n\n## ***How to use***\n
\n\n### **1. Pull the docker image**\n\n docker pull jongseopark/autodock_gpu_js:latest\n\n
\n\n### **2. Prepare several files**\n\nFirst, create a environment of Anaconda and install **mgltools** and **autodock** as follows.\n\n conda create -n autodock\n\n conda activate autodock\n\n conda install -c bioconda mgltools\n conda install -c hcc autodock\n\n\nNow, when you type **pmv** in the terminal, the GUI program will be opened.\n\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 ***'ligand'***.\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 run/\n autodock_gpu_for_zinc.py # The script in this repository\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 merged_ligs/\n merged_ligands.pdbqt\n ...\n\n
\n\n### **4. Run the docker container from the image with mounting of the working directory**\n\n docker run -it --gpus all -v \"/path/to/working/directory/:/home/run/\" jongseopark/autodock_gpu_js:latest\n\n
\n\n### **5. Run the script**\n\nRun the script with various arguments.\n\n\nYou have to choose one argument out of `splitligs` / `listgen` / `result2df` / `dlg2qt` / `znparsing` (+ alpha: `oneclick`)\n\nThe workflow is ...\n\n1. Run the `splitligs` to split all of the ligand files into each file.\n2. Generate the `list.txt` file for running AutoDock-GPU in batch mode using `listgen`.\n3. Run the AutoDock-GPU in batch mode (without this python script).\n4. Organize the highest score among each result into a text file using `result2df`.\nIf you need, you can convert the .dlg files to .pdbqt files using `dlg2qt`.\n5. Parsing detailed information about ligands using `znparsing`.\n\n
\n\nyou can pull the script as follows in the docker container.\n\n wget https://raw.githubusercontent.com/jongseo-park/AutoDock-GPU_for_batch/master/autodock_gpu_for_zinc.py\n\n
\n\n- - -\n\n### ***Automatic running***\n\n\nYou can use the `oneclick` tool to automatically generate list, dock ligands, organize results, and parse chemical information from ZINC database.\n\nJust prepare the `.maps.fld` file and `merged ligand` downloaded from ZINC database.\n\nAnd set the directory as follows\n\n Working_dir/\n\n run/\n autodock_gpu_for_zinc.py # The script in this repository\n\n protein/ \n protein.maps.fld\n protein.A.map\n protein.C.map\n protein.Br.map\n ...\n\n ligands/\n merged_ligands.pdbqt (or sdf / smi format)\n ...\n\n
\n\nThen, run just one command in docker container.\n\n python3 autodock_gpu_for_zinc.py\n --oneclick y\n --proteinpath ./protein/protein.maps.fld/\n --ligandpath ./ligands/\n --ligandfmt pdbqt (or sdf / smi) \n --mergedlig y (or n) # non-essential (default: y) \n --np 60 \n\nYou can find the result in the `./result/` directory.\n\nIn that directory, there are two csv files (result_merged.csv / result_merged_parsing.csv).\n\nThe result_merged_parsing.csv file comprises \n\nfile name / ZINC codes / several chemical informations / etc.\n\n
\n\nIf you want to run using non-merged ligands such as a single ligand,\n\nyou can set the `--mergedlig` argument as `n`.\n\nThen, the ligand splitting will be passed.\n\n\n\n
\n\n* In the case of SMILE string (smi format), please note that there are unexpected errors sometimes.\n* SDF or pdbqt formatted files are recommended as inputs.\n\n- - -\n### ***Sequential running*** \n\n
\n\n### **step 0**\n\nDirectory setup\n\n Working_dir/\n\n run/\n autodock_gpu_for_zinc.py # The script in this repository\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 merged_ligs/\n merged_ligands.pdbqt\n ...\n\n\n### **step 1**\n\nWhen you donwload the pdbqt formatted ligand files from ZINC database,\n\nyou can see that all of the files are composed of multiple ligand bundles.\n\nThen, you can use `splitligs` as follows, and the merged ligand files will be splitted into each file.\n\nI have confirmed that `splitligs` works well with the merged pdbqt files,\n\nbut in the case of other file formats such as sdf, I cannot guarantee the operation.\n\n\n python3 autodock_gpu_for_zinc.py\n --splitligs y\n --ligandpath ./merged_ligs/ # Path to the directory \n # containing merged ligands\n\n
\n\n### **step 2**\n\nThe list.txt file is essential file for running the AutoDock-GPU in batch mode.\n\nFor creating the list.txt file, use `listgen` as follows.\n\n\n\n python3 autodock_gpu_for_zinc.py\n --listgen y\n --proteinpath ./protein/protein.maps.fld # Path to the maps.fld file\n --ligandpath ./ligands/ # Path to the directory containing ligands\n\n\nThe list file is comprised of the path to protein, path to ligand, and the job name.\n\nGenerated list.txt file comprises of a line of the path to protein,\n\nand several lines of the path to ligand and job names.\n\n ./protein/protein.maps.fld # Path to protein.maps.fld\n ./ligands/ligand_1.pdbqt # Path to ligand\n ligand_1 # Job name\n ./ligands/ligand_2.pdbqt # Path to ligand\n ligand_2 # Job name\n ./ligands/ligand_3.pdbqt # ...\n ligand_3 # ...\n ...\n ...\n ./ligands/ligand_n.pdbqt\n ligand_n\n \nYou can also specify the path to list.txt files using `--listpath` argument.\n\nDefault path is `./`.\n\n
\n\n### **step 3**\n\nFor running the AutoDock-GPU in batch mode, use this command in the result directory.\n\n autodock_gpu_128wi -filelist /path/to/list\n \n # example\n autodock_gpu_128wi -filelist ../list.txt\n\nThen, the result files (xml , dlg) will be generated in the result directory.\n\n\n
\n\n### **step 4**\n\nAfter finish the doking, you can use `result2df` to organize results.\n\n python3 autodock_gpu_for_zinc.py\n --result2df y\n --dfpath ./result/ # Set the path you want\n --dlgpath ./result/ # Path to the directory containing dlg files\n --rearr y # Sort Lowest_binding_energy in ascending order (non-essential)\n --setnum 1 # The value that distinguish sets (non-essential)\n\nAfter some time, you can find a pandas dataframe csv file (result_merged.csv) in the `result` directory declared by `--dfpath` argument.\n\nIf you set the `--rearr` argument to `y`, then the *Lowest_binding_energy* will be sorted in ascending order (default = n).\n\nYou can use the `--setnum` argument to distinguish several sets. \n\nThe value you set using `--setnum` is added to the last column of the csv file.\n\nIf you don't need to distinguish `--setnum`, you don't need to enter it (default = 1).\n\n
\n\nYou can also use `dlg2qt` tools as follows, and the pdbqt files (result) will be generated in the `dlgpath`.\n\n python3 autodock_gpu_for_zinc.py\n --dlg2qt y\n --dlgpath ./result/ # Path to the dlg files (result files)\n\n\n### **step 5**\n\nYou can collect the detailed information about all of the ligands using `znparsing` tool.\n\n python3 autodock_gpu_for_zinc.py\n --znparsing y\n --dfpath ./result/result_merged.csv\n --dst ./result/dst/result_parsed.csv\n --rearr y # Sort Lowest_binding_energy in ascending order.\n --np 60\n\nThen, you can obtain one csv file containing the detailed information such as \n\n`Lowest_E` / `LogP` / `Mwt` / `Rotatable bonds` / `H-donors` / `H-acceptors` / `PSA` / `Net charge` / `Chiral centers & nums`.\n\nYou have to specify the path of `result_merged.csv` file generated by the `result2df` tool.\n\nIn addition, you can set the path to result file containing parsed data from ZINC database using `--dst` argument.\n\nIf you set the `--rearr` argument to `y`, then the *Lowest_E* will be sorted in ascending order (default = n).\n\nThe number of cores for running the `znparsing` can be declared using `--np` argument.\n\nI recommend the finding of the optimal np value using small dataset. \n\n
\n\n### **plus alpha**\n\nYou can use openbabel directly in the docker container, or use `--obabel` argument as follows.\n\n python3 autodock_gpu_for_zinc.py\n --obabel y\n --ligandpath /path/to/ligands (eg. ./ligands/)\n --ligandfmt sdf (or smi)\n\nThen, sdf (or smi) formatted ligand files will be converted to the pdbqt files.\n\nYou can also use openbabel in the docker container yourself as follows.\n\n obabel -isdf ./ligands/ligand.sdf -opdbqt -O ./ligands/ligands.pdbqt --AddPolarH --partialcharge mmff94\n\n obabel -ismi ./ligands/ligand.smi -opdbqt -O ./ligands/ligands.pdbqt --gen3d --AddPolarH --partialcharge mmff94\n\n\n
\n\nYou can check all of the arguments with the explanation using this command.\n\n python3 autodock_gpu_for_zinc.py -h\n\n
\n\n- - -\n\n*Thanks.*\n","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",888742535,"orgData","recentTags",[91],{"_92":44,"_93":94,"_95":96,"_97":98,"_99":100,"_50":117,"_118":94,"_119":33,"_120":56,"_57":121,"_122":123,"_124":108,"_125":117,"_126":66,"_127":21},"content_type","creator",10703569,"full_size",693595434,"id",176109867,"images",[101],{"_102":103,"_104":105,"_106":48,"_107":108,"_109":110,"_111":112,"_113":48,"_114":-5,"_115":96,"_63":66,"_116":-5},"architecture","amd64","digest","sha256:5602ef3f4d47d9b4d116433e6579be9d0ec81e56f56249609f742d14285172ea","features","last_pulled","2026-09-27T02:52:57.512480814Z","last_pushed","2021-11-07T02:21:26Z","os","linux","os_features","os_version","size","variant","2021-11-07T02:21:25.931151Z","last_updater","last_updater_username","media_type","latest","repository",15048517,"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":141,"_148":149,"_135":158,"_159":11},{"_40":142,"_42":143,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":144,"_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":145,"_79":11,"_80":147,"_85":33,"_86":87},[],[44],[56],{"_75":11,"_76":146},[78],{"_82":21,"_83":11,"_84":11},"tags",{"_150":64,"_151":-5,"_152":-5,"_153":154},"count","next","previous","results",[155],{"_92":44,"_93":94,"_95":96,"_97":98,"_99":156,"_50":117,"_118":94,"_119":33,"_120":56,"_57":121,"_122":123,"_124":108,"_125":117,"_126":66,"_127":21},[157],{"_102":103,"_104":105,"_106":48,"_107":108,"_109":110,"_111":112,"_113":48,"_114":-5,"_115":96,"_63":66,"_116":-5},[],"canDeleteTags","actionData","errors"]

jongseopark/autodock_gpu_js

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