{"id":13958599,"url":"https://github.com/kekegg/DLEPS","last_synced_at":"2025-07-21T00:31:24.793Z","repository":{"id":37606808,"uuid":"333689374","full_name":"kekegg/DLEPS","owner":"kekegg","description":"A Deep Learning based Efficacy Prediction System for drug discovery","archived":false,"fork":false,"pushed_at":"2022-11-22T08:39:42.000Z","size":1363,"stargazers_count":62,"open_issues_count":9,"forks_count":34,"subscribers_count":8,"default_branch":"main","last_synced_at":"2024-11-28T02:34:51.972Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kekegg.png","metadata":{"files":{"readme":"README.pm","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-01-28T08:09:21.000Z","updated_at":"2024-11-20T05:34:50.000Z","dependencies_parsed_at":"2023-01-21T11:46:12.341Z","dependency_job_id":null,"html_url":"https://github.com/kekegg/DLEPS","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/kekegg/DLEPS","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kekegg%2FDLEPS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kekegg%2FDLEPS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kekegg%2FDLEPS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kekegg%2FDLEPS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kekegg","download_url":"https://codeload.github.com/kekegg/DLEPS/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kekegg%2FDLEPS/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266221262,"owners_count":23894965,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-08T13:01:45.942Z","updated_at":"2025-07-21T00:31:19.781Z","avatar_url":"https://github.com/kekegg.png","language":"Jupyter Notebook","funding_links":[],"categories":["药物发现、药物设计"],"sub_categories":["网络服务_其他"],"readme":"# DLEPS\nA Deep Learning based Efficacy Prediction System for Drug Discovery\n\n# Setup\n\n- ## Install package\n\nThis package requires the **rdkit**, **tensorflow \u003e=1.15.0** and **Keras \u003e=2.3.0**.\n\nconda install -c rdkit rdkit\napt-get update\napt install libxrender1\napt install libxext6\npip install nltk\npip install tensorflow==1.15.0\npip install keras==2.3.0\n\n- ## On Code ocean\n\nThe supporting files and sample input files for the model locates in the data folder. Results were saved in results folder.\n\n# Run the model\n\n- **Script options**\n\ninput files\n1. The csv file with all the chemical SMILES in the column with string SMILES as the header, other columns will be copied to the output file and an efficacy score column will be appended.\n2. The upregulated gene signatures using ENTREZGENE_ACC in a file without header, each gene occupy a row\n3. The downregulated gene signatures using the same format\n\nConversion of gene names can be accomplished at https://biit.cs.ut.ee/gprofiler/convert\n\nA sample command is as followed:\npython driv_DLEPS.py --input=../../data/Brief_Targetmol_natural_product_2719 --output=../../results/np2719_Browning.csv --upset=../../data/BROWNING_up --downset=../../data/BROWNING_down --reverse=False\n\nBatch jobs were put into run_script\n\nOther options include:\n    '--input', default=INPUTFILE,\n                        'Brief format of chemicals: contains SMILES column. '\n    '--use_onehot',  default=True,\n                        'If use pre-stored one hot array to save time.'\n    '--use_l12k',  default=None,\n                        'Use pre-calculated L12k'\n    '--upset',  default=None,\n                        'Up set of genes'\n    '--downset',  default=None,\n                        'Down set of genes. '\n    '--reverse',  default=True,\n                        'If the drug Reverse the Up / Down set of genes. '\n    '--output',  default='out.csv',\n                        'Output file name. '\n\nJupyter notebook users may run DLEPS_tutorial.ipynb for better iterative computing and analysis.\n\nDenseweight file is here:\nhttps://kaggle.com/datasets/b0a096e3c550146f2a786f0ffd3c8bd37d68b04c7b09697efd282f91f8f6e36f\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkekegg%2FDLEPS","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkekegg%2FDLEPS","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkekegg%2FDLEPS/lists"}