Discovery and Evaluation of 3-Quinoxalin Urea Derivatives as Potent, Selective, and Orally Available ATM Inhibitors Combined with Chemotherapy for the Treatment of Cancer via Goal-Oriented Molecule Generation and Virtual Screening

Deng, Dexin; Yang, Yingxue; Zou, Yurong; Liu, Kongjun; Zhang, Chufeng; Tang, Minghai; Tong, Yunguang; Chen, Yong; Yang, Xue; Guo, Yanzhen; Zhang, Shunjie; Si, Wenting; Peng, Bin; Xu, Qing; He, Wen; Xu, Dingguo; Xiang, Mingli; Chen, Lijuan

doi:10.1021/acs.jmedchem.3c00082

Cited by 8 publications

(3 citation statements)

References 87 publications

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“…In Balb/c female mice bearing MV-4-11 tumor cells, orally administered B026 significantly inhibited tumor growth by 75% at 50 mg/kg and 85.7% at 100 mg/kg. Deng et al (2023) proposed a combination of goal-oriented molecule generation and virtual screening to discover new inhibitors of the ATM (ataxia telangiectasia mutated) enzyme. This latter is a member of the Phosphoinositide 3-kinase (PI3K)-related kinases (PIKKs) and is activated in response to DNA damage, particularly DNA double-strand breaks.…”

Section: Model Description Referencesmentioning

confidence: 99%

Deep generative models in the quest for anticancer drugs: ways forward

Romanelli,

Cerchia,

Lavecchia

2024

Front. Drug Discov.

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Drug discovery is a costly and time-consuming process, especially because of the significant expenses connected with the high percentage of clinical trial failures. As such, there is a need for new paradigms enabling the optimization of the various stages, from hit identification to market approval. The upsurge in the use of artificial intelligence (AI) technologies and the advent of deep learning (DL) demonstrated a lot of promise in rethinking and redesigning the traditional pipelines in drug discovery, including de novo molecular design. In this regard, generative models have greatly impacted the de novo design of molecules with desired properties and are being increasingly integrated into real world drug discovery campaigns. Herein, we will briefly appraise recent case studies utilizing generative models for chemical structure generation in the area of anticancer drug discovery. Finally, we will analyze current challenges and limitations as well as the possible strategies to overcome them, outlining potential future directions to advance this exciting field.

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Section: Model Description Referencesmentioning

confidence: 99%

Deep generative models in the quest for anticancer drugs: ways forward

Romanelli,

Cerchia,

Lavecchia

2024

Front. Drug Discov.

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“…Among them, virtual screening tools could significantly reduce the time and cost of drug discovery and increase development efficiency ( 17 ). In the meantime, molecular dynamics (MD) simulation would accurately assess the interaction between active molecules and targets ( 18 ). We combined these two methods and constructed a computational screening protocol to identify novel compounds as potential JAK3 inhibitors.…”

Section: Introductionmentioning

confidence: 99%

A hybrid energy-based and AI-based screening approach for the discovery of novel inhibitors of JAK3

Wei,

Pan,

Shen

et al. 2023

Front. Med.

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The JAKs protein family is composed of four isoforms, and JAK3 has been regarded as a druggable target for the development of drugs to treat various diseases, including hematologic tumors, cancer, and neuronal death. Therefore, the discovery of JAK3 inhibitors with novel scaffolds possesses the potential to provide additional options for drug development. This article presents a structure-based hybrid high-throughput virtual screening (HTVS) protocol as well as the DeepDock algorithm, which is based on geometric deep learning. These techniques were used to identify inhibitors of JAK3 with a novel sketch from a specific “In-house” database. Using molecular docking with varying precision, MM/GBSA, geometric deep learning scoring, and manual selection, 10 compounds were obtained for subsequent biological evaluation. One of these 10 compounds, compound 8, was found to have inhibitory potency against JAK3 and the MOLM-16 cell line, providing a valuable lead compound for further development of JAK3 inhibitors. To gain a better understanding of the interaction between compound 8 and JAK3, molecular dynamics (MD) simulations were conducted to provide more details on the binding conformation of compound 8 with JAK3 to guide the subsequent structure optimization. In this article, we achieved compound 8 with a novel sketch possessing inhibitory bioactivity against JAK3, and it would provide an acceptable “hit” for further structure optimization and modification to develop JAK3 inhibitors.

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“…They are increasingly acknowledged as a distinctive category of chemotherapeutic agents exhibiting notable efficacy against various types of tumors. 33 Quinoxaline-based compounds have shown signicant anticancer efficacy by selectively inhibiting many cellular targets, 34 including topoisomerase, 35 VEGFR2, 36 telomerase, 37 farnesyltransferase, 38 tyrosine kinase, 39 and protein kinase CK-11 (Fig. 1).…”

Section: Introductionmentioning

confidence: 99%