{"id":20690260,"url":"https://github.com/merck/puqsar","last_synced_at":"2026-04-16T06:03:54.117Z","repository":{"id":192147941,"uuid":"686147318","full_name":"Merck/puqsar","owner":"Merck","description":"Prediction Uncertainty for QSAR","archived":false,"fork":false,"pushed_at":"2024-07-13T05:10:11.000Z","size":5127,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-12-25T18:29:12.420Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://merck.github.io/puqsar/","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/Merck.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":"2023-09-01T21:39:22.000Z","updated_at":"2025-09-08T03:40:01.000Z","dependencies_parsed_at":null,"dependency_job_id":"415e494e-634a-47a5-b02e-1e7186a7205e","html_url":"https://github.com/Merck/puqsar","commit_stats":null,"previous_names":["merck/puqsar"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Merck/puqsar","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Merck%2Fpuqsar","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Merck%2Fpuqsar/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Merck%2Fpuqsar/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Merck%2Fpuqsar/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Merck","download_url":"https://codeload.github.com/Merck/puqsar/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Merck%2Fpuqsar/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31873607,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-15T15:24:51.572Z","status":"online","status_checked_at":"2026-04-16T02:00:06.042Z","response_time":69,"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":[],"created_at":"2024-11-16T23:12:26.171Z","updated_at":"2026-04-16T06:03:54.102Z","avatar_url":"https://github.com/Merck.png","language":"Python","readme":"# Prediction Uncertainty for QSAR\n\nThis package contains Python code to construct prediction intervals for QSAR regression.\nThe implemented QSAR prediction models include: Random Forests, Fully-Connected Neural Networks, and Gradient Boosting.\nThe methodology for developing prediction intervals accompanying the point predictors can be find in Reference [1] and [2].\n\nDeveloper \u0026 Maintainer: Yuting Xu (Merck \u0026 Co., Inc.) \u003cyuting.xu@merck.com\u003e\n\nLast updated: Jul. 13th, 2024\n\n## Workflow\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"docs/readme_logo.jpg\" alt=\"Logo_workflow\" width=\"600\"\u003e\n\u003c/p\u003e\n\n## Usage\n\nThe code is written in the functional programming paradigm without the hassle of installation.\nSimply clone or download the repository to your local machine, and use the provided examples as a starting point to experiment with your own workflow.\n\n### Prerequisites\n\n* numpy\n* pandas\n* dill\n* scipy\n* scikit-learn\n* tensorflow\n* keras\n* lightgbm\n\n## Reference\n\n[1] Xu, Y., Liaw, A., Sheridan, R. P., \u0026 Svetnik, V. (2024). Development and Evaluation of Conformal Prediction Methods for Quantitative Structure–Activity Relationship. ACS Omega. [Link](https://pubs.acs.org/doi/full/10.1021/acsomega.4c02017)\n\n[2] Cortes-Ciriano, I.; Bender, A. Reliable prediction errors for deep neural networks using test-time dropout. Journal of chemical information and modeling 2019, 59, 3330–3339.\n\n## License\nThis project is licensed under the GNU General Public License v3.0 License - see the [LICENSE](LICENSE) file for details.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmerck%2Fpuqsar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmerck%2Fpuqsar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmerck%2Fpuqsar/lists"}