Ligand- and structure-based identification of novel CDK9 inhibitors for the potential treatment of leukemia

Zhang, Huimin; Huang, Jindi; Chen, Rui; Cai, Hanxuan; Chen, Yi-Hao; He, Shuyun; Xu, Jianrong; Zhang, Jiquan

doi:10.1016/j.bmc.2022.116994

Cited by 16 publications

(7 citation statements)

References 58 publications

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“…We successfully selected five compounds using the FP-GNN model to target cycle-dependent family kinase 9 (CDK9) inhibition and demonstrated good anti-cancer activity on eight tumor cells by in vitro cell assay. ( Zhang et al, 2022 ). However, most datasets in drug discovery feature significant linkages between subtasks.…”

Section: Methodsmentioning

confidence: 99%

DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction

Cai

Wei

et al. 2023

Front. Pharmacol.

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Cytochrome P450 (CYP) is a superfamily of heme-containing oxidizing enzymes involved in the metabolism of a wide range of medicines, xenobiotics, and endogenous compounds. Five of the CYPs (1A2, 2C9, 2C19, 2D6, and 3A4) are responsible for metabolizing the vast majority of approved drugs. Adverse drug-drug interactions, many of which are mediated by CYPs, are one of the important causes for the premature termination of drug development and drug withdrawal from the market. In this work, we reported in silicon classification models to predict the inhibitory activity of molecules against these five CYP isoforms using our recently developed FP-GNN deep learning method. The evaluation results showed that, to the best of our knowledge, the multi-task FP-GNN model achieved the best predictive performance with the highest average AUC (0.905), F1 (0.779), BA (0.819), and MCC (0.647) values for the test sets, even compared to advanced machine learning, deep learning, and existing models. Y-scrambling testing confirmed that the results of the multi-task FP-GNN model were not attributed to chance correlation. Furthermore, the interpretability of the multi-task FP-GNN model enables the discovery of critical structural fragments associated with CYPs inhibition. Finally, an online webserver called DEEPCYPs and its local version software were created based on the optimal multi-task FP-GNN model to detect whether compounds bear potential inhibitory activity against CYPs, thereby promoting the prediction of drug-drug interactions in clinical practice and could be used to rule out inappropriate compounds in the early stages of drug discovery and/or identify new CYPs inhibitors.

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Section: Methodsmentioning

confidence: 99%

DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction

Cai

Wei

et al. 2023

Front. Pharmacol.

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“…FP‐GNN is a novel DL architecture for molecular property prediction, which combined and simultaneously studied the information from molecular graphs and molecular fingerprints 52–54 . The evaluation outcomes illustrated that FP‐GNN was a competitive DL algorithm for the molecular property prediction tasks.…”

Section: Methodsmentioning

confidence: 99%

“…FP-GNN is a novel DL architecture for molecular property prediction, which combined and simultaneously studied the information from molecular graphs and molecular fingerprints. [52][53][54] The evaluation outcomes illustrated that FP-GNN was a competitive DL algorithm for the molecular property prediction tasks. To achieve the best FP-GNN model, six hyperparameters are chosen to optimize by using the Hyperopt Pythonpackage 55 : the dropout rate of GAN, the number of multihead attentions, the hidden size of attentions, the hidden size and dropout rate of FPN, and the ratio of GNN in FP-GNN.…”

Section: Algorithms and Model Constructionmentioning

confidence: 99%

VDDB: A comprehensive resource and machine learning tool for antiviral drug discovery

Tao

Chen

et al. 2023

MedComm – Future Medicine

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Virus infection is one of the major diseases that seriously threaten human health. To meet the growing demand for mining and sharing data resources related to antiviral drugs and to accelerate the design and discovery of new antiviral drugs, we presented an open‐access antiviral drug resource and machine learning platform (VDDB), which, to the best of our knowledge, is the first comprehensive dedicated resource for experimentally verified potential drugs/molecules based on manually curated data. Currently, VDDB highlights 848 clinical vaccines and 199 clinical antibodies, as well as over 710,000 small molecules targeting 39 medically important viruses including severe acute respiratory syndrome coronavirus 2. Furthermore, VDDB stores approximately three million records of pharmacological data for these collected potential antiviral drugs/molecules, involving 314 cell infection‐based phenotypic and 234 target‐based genotypic assays. Based on these annotated pharmacological data, VDDB allows users to browse, search, and download reliable information about these collects for various viruses of interest. In particular, VDDB also integrates 57 cell infection‐ and 117 target‐based associated high‐accuracy machine learning models to support various antivirals identification‐related tasks, such as compound activity prediction, virtual screening, drug repositioning, and target fishing. VDDB is freely accessible at https://vddb.idruglab.cn.

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“…In another study, Brindha et al [ 119 ] successfully implemented machine learning algorithms to predict the efficacy of five drugs based on clinical as well as molecular characteristics of oral squamous cell carcinomas. Furthermore, machine learning has recently been implemented in numerous aspects of oncology, such as drug target prediction [ 120 ], drug repurposing [ 121 ], and prognostic profiles for immunotherapy [ 122 ].…”

Section: Predicting Gut Microbiota–xenobiotic Interactionsmentioning

confidence: 99%

Orally Administered Drugs and Their Complicated Relationship with Our Gastrointestinal Tract

Bashiardes,

Christodoulou

2024

Microorganisms

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Orally administered compounds represent the great majority of all pharmaceutical compounds produced for human use and are the most popular among patients since they are practical and easy to self-administer. Following ingestion, orally administered drugs begin a “perilous” journey down the gastrointestinal tract and their bioavailability is modulated by numerous factors. The gastrointestinal (GI) tract anatomy can modulate drug bioavailability and accounts for interpatient drug response heterogeneity. Furthermore, host genetics is a contributor to drug bioavailability modulation. Importantly, a component of the GI tract that has been gaining notoriety with regard to drug treatment interactions is the gut microbiota, which shares a two-way interaction with pharmaceutical compounds in that they can be influenced by and are able to influence administered drugs. Overall, orally administered drugs are a patient-friendly treatment option. However, during their journey down the GI tract, there are numerous host factors that can modulate drug bioavailability in a patient-specific manner.

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Ligand- and structure-based identification of novel CDK9 inhibitors for the potential treatment of leukemia

Cited by 16 publications

References 58 publications

DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction

DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction

VDDB: A comprehensive resource and machine learning tool for antiviral drug discovery

Orally Administered Drugs and Their Complicated Relationship with Our Gastrointestinal Tract

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