KinomeMETA: a web platform for kinome-wide polypharmacology profiling with meta-learning.
Nucleic Acids Res
; 52(W1): W489-W497, 2024 Jul 05.
Article
en En
| MEDLINE
| ID: mdl-38752486
ABSTRACT
Kinase-targeted inhibitors hold promise for new therapeutic options, with multi-target inhibitors offering the potential for broader efficacy while minimizing polypharmacology risks. However, comprehensive experimental profiling of kinome-wide activity is expensive, and existing computational approaches often lack scalability or accuracy for understudied kinases. We introduce KinomeMETA, an artificial intelligence (AI)-powered web platform that significantly expands the predictive range with scalability for predicting the polypharmacological effects of small molecules across the kinome. By leveraging a novel meta-learning algorithm, KinomeMETA efficiently utilizes sparse activity data, enabling rapid generalization to new kinase tasks even with limited information. This significantly expands the repertoire of accurately predictable kinases to 661 wild-type and clinically-relevant mutant kinases, far exceeding existing methods. Additionally, KinomeMETA empowers users to customize models with their proprietary data for specific research needs. Case studies demonstrate its ability to discover new active compounds by quickly adapting to small dataset. Overall, KinomeMETA offers enhanced kinome virtual profiling capabilities and is positioned as a powerful tool for developing new kinase inhibitors and advancing kinase research. The KinomeMETA server is freely accessible without registration at https//kinomemeta.alphama.com.cn/.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Proteínas Quinasas
/
Internet
/
Inhibidores de Proteínas Quinasas
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Polifarmacología
Límite:
Humans
Idioma:
En
Revista:
Nucleic Acids Res
Año:
2024
Tipo del documento:
Article
País de afiliación:
China