2022
DOI: 10.1038/s41598-022-22324-8
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KUALA: a machine learning-driven framework for kinase inhibitors repositioning

Abstract: The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a double role. On the one hand it is a critical issue for selectivity, on the other hand, according to poly-pharmacology, a synergistic controlled effect on more than one target could be of great pharmacological interest. Anoth… Show more

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Cited by 6 publications
(3 citation statements)
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“…Integration of data on kinase-ligand interaction from different source is a challenging task, due to the wide range of experiment types and definitions of activities, which could lead to significant systematic errors in results 54 , 55 . To address this challenge, previous studies have employed classification models, rather than regression models, or trained each data source separately 16 , 18 , 56 , 57 . On the contrary, our study used both IC50 and Ki values as targets for bioactivity, which are commonly used in the literature as measures of inhibitor potency and can provide a more comprehensive coverage of the dataset.…”
Section: Discussionmentioning
confidence: 99%
“…Integration of data on kinase-ligand interaction from different source is a challenging task, due to the wide range of experiment types and definitions of activities, which could lead to significant systematic errors in results 54 , 55 . To address this challenge, previous studies have employed classification models, rather than regression models, or trained each data source separately 16 , 18 , 56 , 57 . On the contrary, our study used both IC50 and Ki values as targets for bioactivity, which are commonly used in the literature as measures of inhibitor potency and can provide a more comprehensive coverage of the dataset.…”
Section: Discussionmentioning
confidence: 99%
“…91 In cancer research, a novel approach called KUALA (Kinase drUgs mAchine Learning framework) utilized AI to automatically identify kinase active ligands for effective drug repositioning within the protein kinase family. 92 AI is not limited to ligand identification, but it can help identify molecular targets. For example, PandaOmics, an AI platform, was used to identify 11 novel therapeutic targets for ALS from CNS and iPSC-derived motor neuron data.…”
Section: Computer-aided Drug Design and Artificial Intelligencementioning
confidence: 99%
“…Beyond traditional molecular docking approaches, machine learning methods have emerged as promising tools in this context, offering time- and cost-effective means to navigate the kinase chemical space. In fact, several new models for compound-kinase binding prediction are introduced every month [CCAS + 15, CRA + 21, DQJ + 22, DSSGP22]. They differ in the learning algorithm used, such as simple k-nearest neighbor regression [BHS + 21], decision trees [TAA + 22], kernel learning [MM12, NPC16, CRP + 17, CPS + 18] and deep learning methods [BHS + 21, O18, KZEK23, LLP23, SSB + 23], as well as compound and protein descriptors, including compound SMILES and graphs [DTME20], protein amino acid sequences [BHS + 21, KZEK23] and, lately, more complex 3D structure-based features [KZK + 23, PHL + 23, LKN + 23, LTZ + 23] and embeddings from pretrained large language models [SSB + 23].…”
Section: Introductionmentioning
confidence: 99%