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image of Machine Learning, Virtual Screening and Bioactivity Evaluation to Identify AJ-292/12941271 as an Anti-proliferative Agent and Target mTOR Protein

Abstract

Objectives

The objective of this study is to obtain inhibitors against mTOR targets with virtual screening, dynamic simulation and bioactivity assessment. This pursuit aims to obtain a rapid and accurate method for the discovery of new mTOR inhibitors.

Methods

Firstly, the researchers obtained nearly 9000 compounds by using ROC-guided machine learning from a library of over 200000 compounds. Secondly, virtual screening was used to evaluate the affinity of 45 compounds. Further analysis was performed to identify 6 hit compounds. Simultaneously, MTT antitumor activity evaluation and kinase inhibition assays are conducted for the active compounds to discern the most promising candidates. Furthermore, AO staining and JC-1 assays are performed for the selected compounds. Simultaneously, MTT antitumor activity evaluation and kinase inhibition assays are conducted for the active compounds to discern the most promising candidates. Furthermore, AO staining, JC-1 and hemolytic toxicity evaluation assays are performed for the selected compounds.

Results

The kinase assay demonstrates that these 6 compounds display greater sensitivity to mTOR than to PI3K. Among them, compounds AJ-292/12941271 and AG-205/12550019 show better activity against mTOR target than PI3K, with an IC of 2.55 and 4.48 μM, respectively. Additionally, the anti-proliferative activity of the six hit compounds was also considered. Compound AJ-292/12941271 shows the best anticancer activity against A549 cell lines with an IC value of 4.3 μM. Further analysis reveals that compound AJ-292/12941271 induces apoptosis in the A549 cell line in a concentration-dependent or time-dependent manner. Hemolytic toxicity evaluation suggests that the compound AJ-292/12941271 is safe for further study.

Conclusion

This research proposes that the fused method of ROC-based machine learning, virtual screening, and bioactivity evaluation could be used to discover novel mTOR inhibitors quickly and precisely.

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2025-07-18
2025-09-08

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Machine Learning, Virtual Screening and Bioactivity Evaluation to Identify AJ-292/12941271 as an Anti-proliferative Agent and Target mTOR Protein
Bentham Science Publishers ; https://doi.org/10.2174/0109298673362945250628044807
/content/journals/cmc/10.2174/0109298673362945250628044807
/content/journals/cmc/10.2174/0109298673362945250628044807
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  • Article Type:     Research Article
Keywords: anti-proliferative ; bioactivity ; virtual screening ; inhibitors ; Machine learning ; mTOR inhibitor

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