Optimisation of momelotinib with improved potency and efficacy as pan-JAK inhibitor

Desai, Jigar; Patel, Bhaumin; Gite, Archana; Panchal, Nandini; Gite, Sanjay; Argade, A. B.; Kumar, Jeevan; Sachchidanand, S.; Bandyopadhyay, Debdutta; Ghoshdastidar, Krishnarup; Patel, Hoshang; Chatterjee, Abhijit; Mahapatra, Jogeshwar; Sharma, Manoranjan; Giri, Poonam; Kumar, Sanjay; Jain, Mukul; Sharma, R. P.; Desai, Ranjit C.

doi:10.1016/j.bmcl.2022.128728

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Eight steps were carried out in the depicted study which includes feature extraction using RDKit (1), machine learning model training (2), library screening for lead compounds (3), JAK2 3D structure retrieval (4), molecular docking utilizing discovery studio (5), and molecular dynamic simulation (6). Experimental validation (7) was carried out to monitor the blockage of JAK2 by the screened compounds. Therefore, results were analyzed and compiled (8).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…3−5 The dysregulation of JAKs has been linked to the development of several inflammatory disorders, including rheumatoid arthritis, psoriasis, and inflammatory bowel disease. 6,7 There are four known members of the JAK family: JAK1, JAK2, JAK3, and TYK2. 8 Each JAK member has distinct characteristics and specific functions in the cell.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Accordingly, drug repurposing is becoming an increasingly important area of research in drug development. Janus kinases (JAKs) are a family of nonreceptor tyrosine kinases that play important roles in cell signaling, particularly in the immune system. − The dysregulation of JAKs has been linked to the development of several inflammatory disorders, including rheumatoid arthritis, psoriasis, and inflammatory bowel disease. , There are four known members of the JAK family: JAK1, JAK2, JAK3, and TYK2 . Each JAK member has distinct characteristics and specific functions in the cell.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation

Yasir,

Park,

Han

et al. 2023

J. Chem. Inf. Model.

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Machine learning algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Machine learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained machine learning model to screen a vast chemical library for new JAK2 inhibitors, the biological activities of which were reported. Reference JAK2 inhibitors, comprising 1911 compounds, have experimentally determined IC 50 values. To generate the input to the machine learning model, reference compounds were subjected to RDKit, a cheminformatic toolkit, to extract molecular descriptors. A Random Forest Regression model from the Scikit-learn machine learning library was applied to obtain a predictive regression model and to analyze each molecular descriptor's role in determining IC 50 values in the reference data set. Then, IC 50 values of the library compounds, comprised of 1,576,903 compounds, were predicted using the generated regression model. Interestingly, some compounds that exhibit high IC 50 values from the prediction were reported to possess JAK inhibition activity, which indicates the limitations of the prediction model. To confirm the JAK2 inhibition activity of predicted compounds, molecular docking and molecular dynamics simulation were carried out with the JAK inhibitor reference compound, tofacitinib. The binding affinity of docked compounds in the active region of JAK2 was also analyzed by the gmxMMPBSA approach. Furthermore, experimental validation confirmed the results from the computational analysis. Results showed highly comparable outcomes concerning tofacitinib. Conclusively, the machine learning model can efficiently improve the virtual screening of drugs and drug development.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation

Yasir,

Park,

Han

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…Janus kinases (JAKs) belong to a family of non-receptor tyrosine kinases crucial for cellular signaling, especially within the immune system [ 13 , 14 , 15 ]. Disruption in JAK function is associated with various inflammatory disorders, such as rheumatoid arthritis, psoriasis, and inflammatory bowel disease [ 16 , 17 ]. Four main members constitute the JAK family: JAK1, JAK2, JAK3, and TYK2 [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation

Yasir,

Park,

Han

et al. 2024

Molecules

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The increasing utilization of artificial intelligence algorithms in drug development has proven to be highly efficient and effective. One area where deep learning-based approaches have made significant contributions is in drug repositioning, enabling the identification of new therapeutic applications for existing drugs. In the present study, a trained deep-learning model was employed to screen a library of FDA-approved drugs to discover novel inhibitors targeting JAK2. To accomplish this, reference datasets containing active and decoy compounds specific to JAK2 were obtained from the DUD-E database. RDKit, a cheminformatic toolkit, was utilized to extract molecular features from the compounds. The DeepChem framework’s GraphConvMol, based on graph convolutional network models, was applied to build a predictive model using the DUD-E datasets. Subsequently, the trained deep-learning model was used to predict the JAK2 inhibitory potential of FDA-approved drugs. Based on these predictions, ribociclib, topiroxostat, amodiaquine, and gefitinib were identified as potential JAK2 inhibitors. Notably, several known JAK2 inhibitors demonstrated high potential according to the prediction results, validating the reliability of our prediction model. To further validate these findings and confirm their JAK2 inhibitory activity, molecular docking experiments were conducted using tofacitinib—an FDA-approved drug for JAK2 inhibition. Experimental validation successfully confirmed our computational analysis results by demonstrating that these novel drugs exhibited comparable inhibitory activity against JAK2 compared to tofacitinib. In conclusion, our study highlights how deep learning models can significantly enhance virtual screening efforts in drug discovery by efficiently identifying potential candidates for specific targets such as JAK2. These newly discovered drugs hold promises as novel JAK2 inhibitors deserving further exploration and investigation.

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Current application status and structure–activity relationship of selective and non-selective JAK inhibitors in diseases

Yang

Zhu

et al. 2023

International Immunopharmacology

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Optimisation of momelotinib with improved potency and efficacy as pan-JAK inhibitor

Cited by 3 publications

References 13 publications

Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation

Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation

Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation

Current application status and structure–activity relationship of selective and non-selective JAK inhibitors in diseases

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