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Pregled bibliografske jedinice broj: 1134301

Crowdsourced mapping of unexplored target space of kinase inhibitors


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 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1134301 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Crowdsourced mapping of unexplored target space of kinase inhibitors

Autori
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

Izvornik
Nature communications (2041-1723) 12 (2021); 3307, 18

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Cheminformatics ; Kinases ; Machine learning

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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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

Izvorni jezik
Engleski

Znanstvena područja
Biologija, Interdisciplinarne prirodne znanosti, Računarstvo

Napomena
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



POVEZANOST RADA


Projekti:
EK-KF-KK.01.1.1.01.0002 - Bioprospecting Jadranskog mora (Čož-Rakovac, Rozelinda; Dragović-Uzelac, Verica; Šantek, Božidar; Jokić, Stela; Jerković, Igor; Kraljević Pavelić, Sandra, EK - KK.01.1.1.01) ( POIROT)

Ustanove:
Institut "Ruđer Bošković", Zagreb

Profili:

Avatar Url Bono Lučić (autor)

Avatar Url Davor Oršolić (autor)

Avatar Url Tomislav Šmuc (autor)

Avatar Url Višnja Stepanić (autor)

Citiraj ovu publikaciju

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 (međunarodna recenzija, članak, znanstveni)
Cichońska, A., Ravikumar, B., Allaway, R., Wan, F., Park, S., Isayev, O., Li, S., Mason, M., Lamb, A. & Tanoli, Z. (2021) Crowdsourced mapping of unexplored target space of kinase inhibitors. Nature communications, 12, 3307, 18 doi:10.1038/s41467-021-23165-1.
@article{article, year = {2021}, pages = {18}, DOI = {10.1038/s41467-021-23165-1}, chapter = {3307}, keywords = {Cheminformatics, Kinases, Machine learning}, journal = {Nature communications}, doi = {10.1038/s41467-021-23165-1}, volume = {12}, issn = {2041-1723}, title = {Crowdsourced mapping of unexplored target space of kinase inhibitors}, keyword = {Cheminformatics, Kinases, Machine learning}, chapternumber = {3307} }
@article{article, year = {2021}, pages = {18}, DOI = {10.1038/s41467-021-23165-1}, chapter = {3307}, keywords = {Cheminformatics, Kinases, Machine learning}, journal = {Nature communications}, doi = {10.1038/s41467-021-23165-1}, volume = {12}, issn = {2041-1723}, title = {Crowdsourced mapping of unexplored target space of kinase inhibitors}, keyword = {Cheminformatics, Kinases, Machine learning}, chapternumber = {3307} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


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