Positioning of an unprecedented 1,5-oxaza spiroquinone scaffold into SMYD2 inhibitors in epigenetic space

Dhorma, Lama Prema; Teli, Mahesh Kumar; Nangunuri, Bhargav Gupta; Venkanna, A.; Ragam, Rao; Maturi, Arunkranthi; Mirzaei, Ali; Vo, Dang-Khoa; Maeng, Han‐Joo; Kim, Mi‐Hyun

doi:10.1016/j.ejmech.2021.113880

Cited by 4 publications

(1 citation statement)

References 53 publications

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“…The chemo-centric approach means if two similar molecules are likely to possess similar properties, they can share biological targets or may show similar pharmacological pro les. [27][28][29][30][31][32] Notably, this study used only two types real data: chemical structures of all chemicals (ChEMBL, CWAs, and NPSs) and cholinergic activities of ChEMBL chemicals (Fig. 3B).…”

Section: Resultsmentioning

confidence: 99%

How to Predict Future Upcoming Harmful Agents: Array Type Meta-Predictor Based Classification

Kumar

Kumari

Ahn

et al. 2022

Preprint

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Molecular insights into chemical safety are very important for sustainable development as well as risk assessment. This study considers how to manage future upcoming harmful agents, especially potentially cholinergic chemical warfare agents (CWAs). For this purpose, structures of known cholinergic agents were encoded by molecular descriptors. And then each drug target interaction (DTI) was learned from the encoded structures and their cholinergic activities to build DTI classification models for five cholinergic targets with reliable statistical validation (ensemble-AUC: up to 0.790, MCC: up to 0.991, accuracy: up to 0.995). The collected classifiers were transformed to 2D or 3D array type meta-predictors for multi-tasking: (1) cholinergic prediction and (2) CWA detection. The detection ability of the array classifiers was verified under the imbalanced dataset between CWAs and none CWAs (area under the precision-recall curve: up to 0.997, MCC: up to 0.638, F1-sore of none CWAs: up to 0.991, F1-sore of CWAs: up to 0.585).

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Section: Resultsmentioning

confidence: 99%

How to Predict Future Upcoming Harmful Agents: Array Type Meta-Predictor Based Classification

Kumar

Kumari

Ahn

et al. 2022

Preprint

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Side Chain Investigation of Imidazopyridazine as a Hinge Binder for Targeting Actionable Mutations of RET Kinase

Maturi,

Sastry,

Kumar

et al. 2024

ACS Med. Chem. Lett.

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Actionable mutations of RET kinase have been identified as oncogenic drivers of solid tumors, including thyroid cancer, metastatic colorectal cancer, and nonsmall cell lung cancer. Although multikinase inhibitors and RET selective inhibitors are used to treat patients with RET alterations, there is insufficient research addressing certain issues: which actionable mutations arise from these therapies, how to improve the clinical response rate to RET inhibitors, and how to design new inhibitors to overcome drug resistance. Therefore, the development of sophisticated tool compounds is required to investigate the molecular mechanisms of actionable mutations and to develop breakthrough therapeutics for different RET alterations. Herein, we present our investigation into the side chains of imidazopyridazine hinge binders that are capable of inducing protein–ligand interaction patterns from the gatekeeper to the waterfront regions. Extending the substituents at the second and sixth positions enhanced the IC50 up to < 0.5 nM for diverse RET alterations.

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Prediction of chemical warfare agents based on cholinergic array type meta-predictors

Kumar

Kumari

Ahn

et al. 2022

Sci Rep

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Molecular insights into chemical safety are very important for sustainable development as well as risk assessment. This study considers how to manage future upcoming harmful agents, especially potentially cholinergic chemical warfare agents (CWAs). For this purpose, the structures of known cholinergic agents were encoded by molecular descriptors. And then each drug target interaction (DTI) was learned from the encoded structures and their cholinergic activities to build DTI classification models for five cholinergic targets with reliable statistical validation (ensemble-AUC: up to 0.790, MCC: up to 0.991, accuracy: up to 0.995). The collected classifiers were transformed into 2D or 3D array type meta-predictors for multi-task: (1) cholinergic prediction and (2) CWA detection. The detection ability of the array classifiers was verified under the imbalanced dataset between CWAs and none CWAs (area under the precision-recall curve: up to 0.997, MCC: up to 0.638, F1-score of none CWAs: up to 0.991, F1-score of CWAs: up to 0.585).

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Positioning of an unprecedented 1,5-oxaza spiroquinone scaffold into SMYD2 inhibitors in epigenetic space

Cited by 4 publications

References 53 publications

How to Predict Future Upcoming Harmful Agents: Array Type Meta-Predictor Based Classification

How to Predict Future Upcoming Harmful Agents: Array Type Meta-Predictor Based Classification

Side Chain Investigation of Imidazopyridazine as a Hinge Binder for Targeting Actionable Mutations of RET Kinase

Prediction of chemical warfare agents based on cholinergic array type meta-predictors

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