Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach

Bao, Lingjie; Wang, Zhe; Wu, Zhenxing; Luo, Hao; Yu, Jiahui; Kang, Yu; Cao, Dong‐Sheng; Hou, Tingjun

doi:10.1016/j.apsb.2022.05.004

Cited by 20 publications

(8 citation statements)

References 45 publications

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“…Thus, understanding their mode of action requires a more sophisticated view than the consideration of a single signaling pathway. Many questions come with this challenge, and there is a great potential for answering them through applying new approaches [ 235 , 236 , 237 ], especially in the field of newly emerged cancer hallmarks.…”

Section: Discussionmentioning

confidence: 99%

Curcuminoids as Anticancer Drugs: Pleiotropic Effects, Potential for Metabolic Reprogramming and Prospects for the Future

Pouliquen

Trošelj

Anto³

2023

Pharmaceutics

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The number of published studies on curcuminoids in cancer research, including its lead molecule curcumin and synthetic analogs, has been increasing substantially during the past two decades. Insights on the diversity of inhibitory effects they have produced on a multitude of pathways involved in carcinogenesis and tumor progression have been provided. As this wealth of data was obtained in settings of various experimental and clinical data, this review first aimed at presenting a chronology of discoveries and an update on their complex in vivo effects. Secondly, there are many interesting questions linked to their pleiotropic effects. One of them, a growing research topic, relates to their ability to modulate metabolic reprogramming. This review will also cover the use of curcuminoids as chemosensitizing molecules that can be combined with several anticancer drugs to reverse the phenomenon of multidrug resistance. Finally, current investigations in these three complementary research fields raise several important questions that will be put among the prospects for the future research related to the importance of these molecules in cancer research.

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Section: Discussionmentioning

confidence: 99%

Curcuminoids as Anticancer Drugs: Pleiotropic Effects, Potential for Metabolic Reprogramming and Prospects for the Future

Pouliquen

Trošelj

Anto³

2023

Pharmaceutics

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“…In recent years, machine learning (ML) and artificial intelligence (AI) have also been applied to the rational drug design of TKIs. These methods can rapidly process large amounts of data and generate predictive models that can guide the design of novel inhibitors with improved properties ( Urbina et al, 2021 ; Moriwaki et al, 2022 ; Bao et al, 2023 ).…”

Section: Future Directionmentioning

confidence: 99%

Characterization of prevalent tyrosine kinase inhibitors and their challenges in glioblastoma treatment

Rahban,

Joushi,

Bashiri

et al. 2024

Front. Chem.

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Glioblastoma multiforme (GBM) is a highly aggressive malignant primary tumor in the central nervous system. Despite extensive efforts in radiotherapy, chemotherapy, and neurosurgery, there remains an inadequate level of improvement in treatment outcomes. The development of large-scale genomic and proteomic analysis suggests that GBMs are characterized by transcriptional heterogeneity, which is responsible for therapy resistance. Hence, knowledge about the genetic and epigenetic heterogeneity of GBM is crucial for developing effective treatments for this aggressive form of brain cancer. Tyrosine kinases (TKs) can act as signal transducers, regulate important cellular processes like differentiation, proliferation, apoptosis and metabolism. Therefore, TK inhibitors (TKIs) have been developed to specifically target these kinases. TKIs are categorized into allosteric and non-allosteric inhibitors. Irreversible inhibitors form covalent bonds, which can lead to longer-lasting effects. However, this can also increase the risk of off-target effects and toxicity. The development of TKIs as therapeutics through computer-aided drug design (CADD) and bioinformatic techniques enhance the potential to improve patients’ survival rates. Therefore, the continued exploration of TKIs as drug targets is expected to lead to even more effective and specific therapeutics in the future.

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“…Various computational methods have been employed for kinase inhibitor pro ling, including ligand-based approaches relying on the chemical properties of small molecules and structure-based approaches incorporating protein structure information [13][14][15][16][17][18][19][20] . Machine learning-based predictive models have shown considerable success in pro ling the bioactivity of kinase inhibitors.…”

Section: Introductionmentioning

confidence: 99%

AiKPro: Deep Learning Model for Kinome-Wide Bioactivity Profiling Using Structure-based Sequence Alignments and Molecular 3D Conformer Ensemble Descriptors

Park¹,

Hong²,

Lee³

et al. 2023

Preprint

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The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for understanding kinase function and identifying selective inhibitors. In this study, we propose AiKPro, a deep learning model that combines structure-validated multiple sequence alignments (svMSA) and molecular 3D conformer ensemble descriptors (3CED) to predict kinase-ligand binding affinities. Our deep learning model uses an attention-based mechanism to capture complex patterns in the interactions between the kinase and the ligand. To assess the performance of AiKPro, we evaluated the impact of descriptors, the predictability for untrained kinases and compounds, and kinase activity profiling based on odd ratios. Our model, AiKPro, shows good Pearson's correlation coefficients of 0.88 and 0.87 for the test set and for the untrained sets of compounds, respectively, which also shows the robustness of the model. AiKPro shows good kinase-activity profiles across the kinome, potentially facilitating the discovery of novel interactions and selective inhibitors. Our approach holds potential implications for the discovery of novel, selective kinase inhibitors and guiding rational drug design.

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Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach

Cited by 20 publications

References 45 publications

Curcuminoids as Anticancer Drugs: Pleiotropic Effects, Potential for Metabolic Reprogramming and Prospects for the Future

Curcuminoids as Anticancer Drugs: Pleiotropic Effects, Potential for Metabolic Reprogramming and Prospects for the Future

Characterization of prevalent tyrosine kinase inhibitors and their challenges in glioblastoma treatment

AiKPro: Deep Learning Model for Kinome-Wide Bioactivity Profiling Using Structure-based Sequence Alignments and Molecular 3D Conformer Ensemble Descriptors

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