Article detail · 2021
Crowdsourced mapping of unexplored target space of kinase inhibitors
- Year
- 2021
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue Nature Communications
- Catalog match (ISSN) Nature Communications
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
92 citations
OpenAlex cited_by_count (cache / database)
3 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).