{"id":32639464,"url":"https://github.com/raghavagps/toxinpred3","last_synced_at":"2025-10-31T02:12:03.903Z","repository":{"id":174954039,"uuid":"653076913","full_name":"raghavagps/toxinpred3","owner":"raghavagps","description":"An improved  method for predicting toxicity of the peptides and designing of non-toxic peptides","archived":false,"fork":false,"pushed_at":"2025-05-29T11:47:56.000Z","size":10120,"stargazers_count":15,"open_issues_count":3,"forks_count":3,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-15T01:58:41.391Z","etag":null,"topics":["bioinformatics","ensemble-machine-learning","machine-learning-algorithms","motif-prediction","peptide-therapeutics","toxicity-prediction"],"latest_commit_sha":null,"homepage":"http://webs.iiitd.edu.in/raghava/toxinpred3","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/raghavagps.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-06-13T11:10:37.000Z","updated_at":"2025-07-10T23:05:17.000Z","dependencies_parsed_at":"2024-07-23T17:11:06.639Z","dependency_job_id":"834361cd-8616-4dfd-9cb4-36888c102580","html_url":"https://github.com/raghavagps/toxinpred3","commit_stats":{"total_commits":23,"total_committers":2,"mean_commits":11.5,"dds":"0.13043478260869568","last_synced_commit":"75661048cd324f1a3c233531d8f56ed304de5581"},"previous_names":["anandr88/toxinpred3","raghavagps/toxinpred3"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/raghavagps/toxinpred3","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raghavagps%2Ftoxinpred3","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raghavagps%2Ftoxinpred3/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raghavagps%2Ftoxinpred3/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raghavagps%2Ftoxinpred3/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/raghavagps","download_url":"https://codeload.github.com/raghavagps/toxinpred3/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raghavagps%2Ftoxinpred3/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":281914569,"owners_count":26583084,"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","status":"online","status_checked_at":"2025-10-31T02:00:07.401Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["bioinformatics","ensemble-machine-learning","machine-learning-algorithms","motif-prediction","peptide-therapeutics","toxicity-prediction"],"created_at":"2025-10-31T02:12:02.534Z","updated_at":"2025-10-31T02:12:03.895Z","avatar_url":"https://github.com/raghavagps.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ToxinPred3.0\nA method for predicting toxicity of the peptides\n# Introduction\nToxinPred3.0 is developed for predicting, mapping and scanning toxic/non-toxic peptides. It uses only composition based features for predicting toxic/non-toxic peptides. The final model also deploys a motif-based module which has been implemented using MERCI. More information on ToxinPred3.0 is available from its web server http://webs.iiitd.edu.in/raghava/toxinpred3. Please read/cite the content about toxinpred3.0 for complete information including algorithm behind the approach.\n\n## PIP Installation\nPIP version is also available for easy installation and usage of this tool. The following command is required to install the package \n```\npip install toxinpred3\n```\nTo know about the available option for the pip package, type the following command:\n```\ntoxinpred3 -h\n```\n\n# Standalone\n\nStandalone version of ToxinPred3.0 is written in python3 and the following libraries are necessary for a successful run:\n\n- scikit-learn\n```\n !pip install scikit-learn==1.0.2\n```\n- Pandas\n- Numpy\n\n# Important Note\n\n- Due to large size of the model file, we have compressed model. \n- It is crucial to unzip the file before attempting to use the code or model. The compressed file must be extracted to its original form for the code to function properly.\n\n\n\n**Minimum USAGE** \n\nTo know about the available option for the standalone, type the following command:\n```\ntoxinpred3.py -h\n```\nTo run the example, type the following command:\n```\ntoxinpred3.py -i peptide.fa\n\n```\n**Full Usage**: \n```\nFollowing is complete list of all options, you may get these options\nusage: toxinpred3.py [-h] \n                     [-i INPUT]\n                     [-o OUTPUT]\n                     [-t THRESHOLD]\n                     [-m {1,2}] \n                     [-d {1,2}]\n```\n```\nPlease provide following arguments\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -i INPUT, --input INPUT\n                        Input: protein or peptide sequence in FASTA format or\n                        single sequence per line in single letter code\n  -o OUTPUT, --output OUTPUT\n                        Output: File for saving results by default outfile.csv\n  -t THRESHOLD, --threshold THRESHOLD\n                        Threshold: Value between 0 to 1 by default 0.38\n  -m {1,2}, -- model Model\n                        Model: 1: ML model, 2: Hybrid model, by default 2\n  -d {1,2}, --display {1,2}\n                        Display: 1:Toxin peptide, 2: All peptides, by\n                        default 1\n\n```\n\n**Input File**: It allow users to provide input in two format; i) FASTA format (standard) (e.g. peptide.fa) and ii) Simple Format. In case of simple format, file should have one peptide sequence in a single line in single letter code (eg. peptide.seq). \n\n**Output File**: Program will save result in CSV format, in case user do not provide output file name, it will be stored in outfile.csv.\n\n**Threshold**: User should provide threshold between 0 and 1, please note score is proportional to toxic potential of peptide.\n\n**Models**:  In this program, two models have been incorporated;  i) Model1 for predicting given input peptide sequence as toxic and non-toxic peptide using Extra tree based on amino-acid composition (AAC) and di peptide composition (DPC) of the peptide; \n\nii) Model2 for predicting given input peptide sequence as toxic and non-toxic peptide using Hybrid approach, which is the ensemble of Extra tree + MERCI. It combines the scores generated from machine learning (ET), and MERCI as Hybrid Score, and the prediction is based on Hybrid Score.\n\n\nToxinPred3.0 Package Files\n=======================\nIt contain following files, brief description of these files given below\n\nINSTALLATION  \t: Installation instructions\n\nLICENSE       \t: License information\n\nmerci : This folder contains the program to run MERCI\n\nREADME.md     \t: This file provide information about this package\n\ntoxinpred3.py \t: Main python program\n\npeptide.fa\t: Example file contain peptide sequences in FASTA format\n\npeptide.seq\t: Example file contain peptide sequences in simple format\n\n## Installation via PIP\nUser can install ToxinPred3 via PIP also\n```\npip install toxinpred3\n```\n## Reference: \nRathore AS, Arora A, Choudhury S, Tijare P, Raghava GPS (2024) ToxinPred3.0:An improved method for predicting the toxicity of peptides. \nComput Biol Med. 179:108926 . https://doi.org/10.1016/j.compbiomed.2024.108926\n\nRathore AS, Arora A, Choudhury S, Tijare P, Raghava GPS. ToxinPred3.0:An improved method for predicting the toxicity of peptides. bioRxiv 2023.08.11.552911; doi: https://doi.org/10.1101/2023.08.11.552911\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraghavagps%2Ftoxinpred3","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fraghavagps%2Ftoxinpred3","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraghavagps%2Ftoxinpred3/lists"}