A Hybrid Energy-Based and AI-Based Screening Approach for the Discovery of Novel Inhibitors of AXL.

IF 3.5 3区医学 Q2 CHEMISTRY, MEDICINAL

ACS Medicinal Chemistry Letters Pub Date : 2025-02-10 eCollection Date: 2025-03-13 DOI:10.1021/acsmedchemlett.4c00511

Xinting Lv, Youkun Kang, Xinglong Chi, Jingyi Zhao, Zhichao Pan, Xiaojun Ying, Long Li, Youlu Pan, Wenhai Huang, Linjun Wang

{"title":"A Hybrid Energy-Based and AI-Based Screening Approach for the Discovery of Novel Inhibitors of AXL.","authors":"Xinting Lv, Youkun Kang, Xinglong Chi, Jingyi Zhao, Zhichao Pan, Xiaojun Ying, Long Li, Youlu Pan, Wenhai Huang, Linjun Wang","doi":"10.1021/acsmedchemlett.4c00511","DOIUrl":null,"url":null,"abstract":"AXL, part of the TAM receptor tyrosine kinase family, plays a significant role in the growth and survival of various tissues and tumors, making it a critical target for cancer therapy. This study introduces a novel high-throughput virtual screening (HTVS) methodology that merges an AI-enhanced graph neural network, PLANET, with a geometric deep learning algorithm, DeepDock. Using this approach, we identified potent AXL inhibitors from our database. Notably, compound 9, with an IC50 of 9.378 nM, showed excellent inhibitory activity, suggesting its potential as a candidate for further research. We also performed molecular dynamics simulations to explore the interactions between compound 9 and AXL, providing insights for future enhancements. This hybrid screening method proves effective in finding promising AXL inhibitors, and advancing the development of new cancer therapies.","PeriodicalId":20,"journal":{"name":"ACS Medicinal Chemistry Letters","volume":"16 3","pages":"410-419"},"PeriodicalIF":3.5000,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11921171/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Medicinal Chemistry Letters","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1021/acsmedchemlett.4c00511","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/13 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}

引用次数: 0

Abstract

AXL, part of the TAM receptor tyrosine kinase family, plays a significant role in the growth and survival of various tissues and tumors, making it a critical target for cancer therapy. This study introduces a novel high-throughput virtual screening (HTVS) methodology that merges an AI-enhanced graph neural network, PLANET, with a geometric deep learning algorithm, DeepDock. Using this approach, we identified potent AXL inhibitors from our database. Notably, compound 9, with an IC₅₀ of 9.378 nM, showed excellent inhibitory activity, suggesting its potential as a candidate for further research. We also performed molecular dynamics simulations to explore the interactions between compound 9 and AXL, providing insights for future enhancements. This hybrid screening method proves effective in finding promising AXL inhibitors, and advancing the development of new cancer therapies.

查看原文本刊更多论文

基于能量和人工智能的混合筛选方法发现新的AXL抑制剂。

AXL是TAM受体酪氨酸激酶家族的一员，在各种组织和肿瘤的生长和存活中起着重要作用，是癌症治疗的重要靶点。本研究介绍了一种新的高通量虚拟筛选（HTVS）方法，该方法将人工智能增强的图形神经网络PLANET与几何深度学习算法DeepDock结合在一起。使用这种方法，我们从数据库中确定了有效的AXL抑制剂。其中化合物9的IC50值为9.378 nM，具有良好的抑菌活性，值得进一步研究。我们还进行了分子动力学模拟，以探索化合物9和AXL之间的相互作用，为未来的增强提供见解。这种混合筛选方法在发现有前途的AXL抑制剂和促进新的癌症治疗的发展方面被证明是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

ACS Medicinal Chemistry Letters CHEMISTRY, MEDICINAL-

CiteScore

7.30

自引率

2.40%

发文量

328

审稿时长

1 months

期刊介绍： ACS Medicinal Chemistry Letters is interested in receiving manuscripts that discuss various aspects of medicinal chemistry. The journal will publish studies that pertain to a broad range of subject matter, including compound design and optimization, biological evaluation, drug delivery, imaging agents, and pharmacology of both small and large bioactive molecules. Specific areas include but are not limited to: Identification, synthesis, and optimization of lead biologically active molecules and drugs (small molecules and biologics) Biological characterization of new molecular entities in the context of drug discovery Computational, cheminformatics, and structural studies for the identification or SAR analysis of bioactive molecules, ligands and their targets, etc. Novel and improved methodologies, including radiation biochemistry, with broad application to medicinal chemistry Discovery technologies for biologically active molecules from both synthetic and natural (plant and other) sources Pharmacokinetic/pharmacodynamic studies that address mechanisms underlying drug disposition and response Pharmacogenetic and pharmacogenomic studies used to enhance drug design and the translation of medicinal chemistry into the clinic Mechanistic drug metabolism and regulation of metabolic enzyme gene expression Chemistry patents relevant to the medicinal chemistry field.