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izvor podataka: crosbi

Crowdsourced mapping of unexplored target space of kinase inhibitors (CROSBI ID 296215)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Cichońska, Anna ; Ravikumar, Balaguru ; Allaway, Robert J. ; Wan, Fangping ; Park, Sungjoon ; Isayev, Olexandr ; Li, Shuya ; Mason, Michael ; Lamb, Andrew ; Tanoli, Ziaurrehman et al. Crowdsourced mapping of unexplored target space of kinase inhibitors // Nature communications, 12 (2021), 3307, 18. doi: 10.1038/s41467-021-23165-1

Podaci o odgovornosti

Cichońska, Anna ; Ravikumar, Balaguru ; Allaway, Robert J. ; Wan, Fangping ; Park, Sungjoon ; Isayev, Olexandr ; Li, Shuya ; Mason, Michael ; Lamb, Andrew ; Tanoli, Ziaurrehman ; Jeon, Minji ; Kim, Sunkyu ; Popova, Mariya ; Capuzzi, Stephen ; Zeng, Jianyang ; Dang, Kristen ; Koytiger, Gregory ; Kang, Jaewoo ; Wells, Carrow I. ; Willson, Timothy M. ; The IDG-DREAM Drug-Kinase Binding Prediction Challenge Consortium ; Oršolić, Davor ; Lučić, Bono ; Stepanić, Višnja ; Šmuc, Tomislav ; Oprea, Tudor I. ; Schlessinger, Avner ; Drewry, David H. ; Stolovitzky, Gustavo ; Wennerberg, Krister ; Guinney, Justin ; Aittokallio, Tero

engleski

Crowdsourced mapping of unexplored target space of kinase inhibitors

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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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.

Cheminformatics ; Kinases ; Machine learning

Davor Oršolić, Bono Lučić, Višnja Stepanić & Tomislav Šmuc participated in the IDG-DREAM Drug-Kinase Binding Prediction Challengea s the members of the team Prospectors Davor Oršolić, Bono Lučić, Višnja Stepanić & Tomislav Šmuc

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Podaci o izdanju

12

2021.

3307

18

objavljeno

2041-1723

10.1038/s41467-021-23165-1

Povezanost rada

Biologija, Interdisciplinarne prirodne znanosti, Računarstvo

Poveznice
Indeksiranost