Discovery of diaminopyrimidine-carboxamide derivatives as JAK3 inhibitors

Bahekar, Rajesh; Panchal, Nandini; Soman, Shubhangi S.; Desai, Jigar; Patel, Dipam; Argade, A. B.; Gite, Archana; Gite, Sanjay; Patel, Bhaumin; Kumar, Jeevan; Sachchidanand, S.; Patel, Harilal; Sundar, Rajesh; Chatterjee, Abhijit; Mahapatra, Jogeswar; Patel, Hoshang; Ghoshdastidar, Krishnarup; Bandyopadhyay, Debdutta; Desai, Ranjit C.

doi:10.1016/j.bioorg.2020.103851

Cited by 7 publications

(1 citation statement)

References 32 publications

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“…Furthermore, a dataset of decoys (noninhibitors) containing 19999 molecules was automatically generated by using the Directory of Useful Decoys‐Enhanced (DUD‐E) based on the inhibitor dataset. [ 55 ] In addition, a total of 29 JAK3 inhibitors, which are not included in the inhibitor dataset, were collected from recent literature, [ 56 ] and 1000 compounds of the Glide docking decoy set were used as decoys (https://www.schrodinger.com/products/glide).…”

Section: Methodsmentioning

confidence: 99%

Machine Learning‐Enabled Virtual Screening with Multiple Protein Structures toward the Discovery of Novel JAK3 Inhibitors: Integration of Molecular Docking, Pharmacophore, and Naïve Bayesian Classification

Zhu

Sun

Jia

et al. 2023

Advcd Theory and Sims

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Extensive research has accumulated suggesting that Janus kinase 3 (JAK3) is closely related to the occurrence and development of various human diseases, making JAK3 a highly potential drug target. However, JAK3 has high homology with other members of the JAK family, making the development of JAK3 inhibitors full of challenges. Thus, here, a naïve Bayesian classification (NBC) model based on multiple JAK3 protein conformations, which integrates molecular docking, pharmacophore, and molecular descriptors, is developed to find novel JAK3 inhibitors. First, the validation set is used to prove whether molecular docking or pharmacophore, integrating multiple JAK3 conformations always has higher prediction accuracy than that of any single conformation. Second, external prediction reveals that the NBC model combining molecular docking, pharmacophore, and important molecular features could significantly improve the enrichment of active JAK3 inhibitors. Finally, the optimal NBC model is utilized for virtual screening against a large chemical database and some compounds with high Bayesian scores are identified. Altogether, the machine learning-based virtual screening protocol not only has strong efficiency but also has high screening accuracy. It is hoped that the developed virtual screening strategy could provide valuable guidance for the discovery of novel JAK3 inhibitors.

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Section: Methodsmentioning

confidence: 99%