canSAR 2024—an update to the public drug discovery knowledgebase

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.

Authors

Institutions

Publication Details

Journal
Nucleic Acids Research
Published
2024-11-13
DOI
https://doi.org/10.1093/nar/gkae1050
Citations
9
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.25

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

canSAR 2024—an update to the public drug discovery knowledgebase

Joseph E Tym, Paul Workman, Li Zhao, Bissan Al‐Lazikani et al.
9 citations
Nucleic Acids Research
Computational Drug Discovery Methods
3.25
article

canSAR 2024—an update to the public drug discovery knowledgebase

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

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.

Nucleic Acids ResearchVol. 53(D1)
The University of Texas MD Anderson Cancer Center (US), Institute of Cancer Research (GB)
Cancer Prevention and Research Institute of Texas, Lyda Hill Foundation, Chordoma Foundation, Bone Cancer Research Trust, Cancer Research UK, University of Texas MD Anderson Cancer Center
Openalex Percentile: Top 7%
Computational Drug Discovery Methods
3.25
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.