Suppr超能文献

canSAR 2024——公共药物发现知识库的更新版。

canSAR 2024-an update to the public drug discovery knowledgebase.

作者信息

Gingrich Phillip W, Chitsazi Rezvan, Biswas Ansuman, Jiang Chunjie, Zhao Li, Tym Joseph E, Brammer Kevin M, Li Jun, Shu Zhigang, Maxwell David S, Tacy Jeffrey A, Mica Ioan L, Darkoh Michael, di Micco Patrizio, Russell Kaitlyn P, Workman Paul, Al-Lazikani Bissan

机构信息

Department of Genomic Medicine; Therapeutics Discovery Division; and The Institute for Data Science in Oncology; University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

Enterprise Development and Integration, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

出版信息

Nucleic Acids Res. 2025 Jan 6;53(D1):D1287-D1294. doi: 10.1093/nar/gkae1050.

Abstract

canSAR (https://cansar.ai) continues to serve as the largest publicly available platform for cancer-focused drug discovery and translational research. It integrates multidisciplinary data from disparate and otherwise siloed public data sources as well as data curated uniquely for canSAR. In addition, canSAR deploys a suite of curation and standardization tools together with AI algorithms to generate new knowledge from these integrated data to inform hypothesis generation. Here we report the latest updates to canSAR. As well as increasing available data, we provide enhancements to our algorithms to improve the offering to the user. Notably, our enhancements include a revised ligandability classifier leveraging Positive Unlabeled Learning that finds twice as many ligandable opportunities across the pocketome, and our revised chemical standardization pipeline and hierarchy better enables the aggregation of structurally related molecular records.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b51/11701553/49355a722936/gkae1050figgra1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验