Chemical fragment-based CDK4/6 inhibitors prediction and web server

Li, Yecheng; Xu, Mengyan; Pang, Xiaoqian; Liu, Zhihong; Tan, Wen; Xu, Jing

doi:10.1039/c5ra23289a

Cited by 13 publications

(4 citation statements)

References 44 publications

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“…The structural diversity and large chemical space of the molecules in the modelling datasets can help to build accurate and robust predictive models ( Wang et al, 2016b ; Luo et al, 2019 ; Guo et al, 2020 ). According to Bemis Murcko scaffold analysis ( Bemis and Murcko, 1996 ), the proportion of the scaffolds in the PARP inhibitors modelling datasets ranged from 17.55% to 26.70% ( Table 1 ), indicating considerable structural diverse of compounds within each PARP subtype.…”

Section: Resultsmentioning

confidence: 99%

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors

Cai

et al. 2022

Front. Pharmacol.

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PARP (poly ADP-ribose polymerase) family is a crucial DNA repair enzyme that responds to DNA damage, regulates apoptosis, and maintains genome stability; therefore, PARP inhibitors represent a promising therapeutic strategy for the treatment of various human diseases including COVID-19. In this study, a multi-task FP-GNN (Fingerprint and Graph Neural Networks) deep learning framework was proposed to predict the inhibitory activity of molecules against four PARP isoforms (PARP-1, PARP-2, PARP-5A, and PARP-5B). Compared with baseline predictive models based on four conventional machine learning methods such as RF, SVM, XGBoost, and LR as well as six deep learning algorithms such as DNN, Attentive FP, MPNN, GAT, GCN, and D-MPNN, the evaluation results indicate that the multi-task FP-GNN method achieves the best performance with the highest average BA, F1, and AUC values of 0.753 ± 0.033, 0.910 ± 0.045, and 0.888 ± 0.016 for the test set. In addition, Y-scrambling testing successfully verified that the model was not results of chance correlation. More importantly, the interpretability of the multi-task FP-GNN model enabled the identification of key structural fragments associated with the inhibition of each PARP isoform. To facilitate the use of the multi-task FP-GNN model in the field, an online webserver called PARPi-Predict and its local version software were created to predict whether compounds bear potential inhibitory activity against PARPs, thereby contributing to design and discover better selective PARP inhibitors.

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Section: Resultsmentioning

confidence: 99%

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors

Cai

et al. 2022

Front. Pharmacol.

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“…Apart from ANN-driven LBVS, there are many instances in which different algorithms, such as the SVM method, − and Bayesian inference − have played an active role in drug classification. There are many publications relating to the successful application of Bayesian models, for example, in the discovery of novel vascular endothelial growth factor receptor-2 scaffolds and in identifying potential farnesoid X receptor agonists, liver X receptor β agonists, ROCK II inhibitors, cyclin-dependent kinase inhibitors, DNA gyrase inhibitors, breast cancer resistance protein (BCRP) inhibitors, , dipeptidyl peptidase IV (DPP-IV) inhibitors, butyrylcholinesterase (BuChE) inhibitors, and inhibitors of β-hematin formation . The generalizability of this method to new targets is well-established.…”

Section: Artificial Intelligence Approaches In Ligand-based Virtual S...mentioning

confidence: 99%

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

et al. 2019

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Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

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“…Notably, the experiment did not employ any data augmentation techniques, as the goal was to maintain the original form of the compound molecules. The dataset's diversity in molecular structures is advantageous for establishing accurate molecular property prediction models [27], assessed through Scaled Shannon Entropy (SSE) to measure the structural diversity [28][29][30]. SSE values for all subtypes within the Src family were greater than 0.75, indicating signi cant structural diversity within the dataset constructed for this study.…”

Section: Dataset Analysismentioning

confidence: 99%

Multi-task MSSP model can accurately predict selective Src inhibitors

Tian,

Han,

et al. 2024

Preprint

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Src family kinases (SFKs), non-receptor tyrosine kinases, crucially contribute to invasion, tumor progression, epithelial-mesenchymal transition, angiogenesis, and metastasis. Thus, Src inhibitors offer a promising avenue for cancer therapy. This study introduced a multitask MSSP deep learning model to predict molecule inhibitory activity across multiple Src subtypes. Comparative assessment against four traditional machine learning methods—Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost)—established the superior performance of the multitask MSSP model. It demonstrated the best comprehensive performance, achieving F1-Score and AUC values of 0.906 and 0.975, respectively. An online web server, "SRC-Predictor," was created to aid the practical application of the multitask MSSP model, predicting compounds' potential inhibitory activity against Src. Finally, compounds ranking in the top twenty based on model predictions were selected for experimental validation. Literature search for these compounds revealed limited research on four of them concerning Src. Molecular docking identified Doramapimod as exhibiting better affinity towards Src compared to reference compounds. It significantly inhibited Lyn kinase activity and influenced the secretion levels of inflammatory factors in LPS-induced macrophages. Experimental validation confirmed that our study provides a novel approach for identifying and screening lead compounds as Src inhibitors.

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Chemical fragment-based CDK4/6 inhibitors prediction and web server

Abstract: Cyclin-dependent kinases (CDKs), a family of mammalian heterodimeric kinases, play central roles in the regulation of cell cycle progression, transcription, neuronal differentiation, and metabolism.

Cited by 13 publications

References 44 publications

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Multi-task MSSP model can accurately predict selective Src inhibitors

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