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Here, deep-learning-assisted virtual screening combined DFCNN, Autodock Vina, DeepBindBC, MD, and metadynamics to identify TIPE3 inhibitors from a ZINC dataset.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/small-molecule-inhibitors-of-tipe3-protein-identified-through-deep-learning-suppress-cancer-cell-growth-in-vitro/341174/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/small-molecule-inhibitors-of-tipe3-protein-identified-through-deep-learning-suppress-cancer-cell-growth-in-vitro/341174.png","ImageObject",300,407,{"name":42,"@type":43},"Eliana","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-22",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What role does TIPE3 play in cancer cells?","Question",{"text":63,"@type":64},"TIPE3 functions as a transfer protein for lipid second messengers and is upregulated in cancers, where it promotes proliferation, migration, invasion, and inhibits apoptosis.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were potential TIPE3 inhibitors identified in this study?",{"text":68,"@type":64},"The study used deep-learning-driven virtual screening integrating DFCNN with docking (Autodock Vina), DeepBindBC, molecular dynamics, and metadynamics on compounds from a ZINC dataset.",{"name":70,"@type":61,"acceptedAnswer":71},"Which candidates showed anti-tumor activity and what was their selectivity?",{"text":72,"@type":64},"Three small-molecule compounds, K784-8160, E745-0011, and 7238-1516, showed significant anti-tumor activity in vitro. 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Chen 1,2,4, * and Haiping Zhang 3, *  \nCitation: Chen, X.; Lu, Z.; Xiao, J.; Xia, W.; Pan, Y.; Xia, H.; Chen, Y.H.; Zhang, H. Small-Molecule Inhibitors of TIPE3 Protein Identified through Deep Learning Suppress Cancer Cell Growth In Vitro. Cells 2024, 13, 771 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)cells13090771  \nAcademic Editors: Alexander  \nE. Kalyuzhny and David Qian  \nReceived: 14 March 2024  \nRevised: 17 April 2024  \nAccepted: 26 April 2024  \nPublished: 30 April 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Center for Cancer Immunology, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; [xd.chen@siat.ac.cn](xd.chen@siat.ac.cn) (X.C.); [zhen.lu@siat.ac.cn](zhen.lu@siat.ac.cn) (Z.L.); [hj.xia@siat.ac.cn](hj.xia@siat.ac.cn) (H.X.)  \n2 University of Chinese Academy of Sciences, Beijing 100049, China  \n3 Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; [52264300064@stu.ecnu.edu.cn](52264300064@stu.ecnu.edu.cn) (J.X.); [weixia@vip.126.com](weixia@vip.126.com) (W.X.); [yi.pan@siat.ac.cn](yi.pan@siat.ac.cn) (Y.P.)  \n4 Faculty of Pharmaceutical Sciences, Shenzhen University of Advanced Technology, Shenzhen 518055, China  \n* [Correspondence: yh.chen@siat.ac.cn](Correspondence: yh.chen@siat.ac.cn) (Y.H.C.); [hp.zhang@siat.ac.cn](hp.zhang@siat.ac.cn) (H.Z.)† These authors contributed equally to this work.  \nAbstract: Tumor necrosis factor-α-induced protein 8-like 3 (TNFAIP8L3 or TIPE3) functions as a transfer protein for lipid second messengers. TIPE3 is highly upregulated in several human cancersand has been established to significantly promote tumor cell proliferation, migration, and invasion and inhibit the apoptosis of cancer cells. Thus, inhibiting the function of TIPE3 is expected to bean effective strategy against cancer. The advancement of artificial intelligence (AI)-driven drug development has recently invigorated research in anti-cancer drug development. In this work, we incorporated DFCNN, Autodock Vina docking, DeepBindBC, MD, and metadynamics to efficiently identify inhibitors of TIPE3 from a ZINC compound dataset. Six potential candidates were selected for further experimental study to validate their anti-tumor activity. Among these, three small-molecule compounds (K784-8160, E745-0011, and 7238-1516) showed significant anti-tumor activity in vitro, leading to reduced tumor cell viability, proliferation, and migration and enhanced apoptotic tumor cell death. Notably, E745-0011 and 7238-1516 exhibited selective cytotoxicity toward tumor cells with high TIPE3 expression while having little or no effect on normal human cells or tumor cells with low TIPE3 expression. A molecular docking analysis further supported their interactions with TIPE3, highlighting hydrophobic interactions and their shared interaction residues and offering insights for designing more effective inhibitors. Taken together, this work demonstrates the feasibility of incorporating deep learning and MD simulations in virtual drug screening and provides inhibitors with significant potential for anti-cancer drug development against TIPE3 − .  \nKeywords: TIPE3; small-molecule compounds; anti-tumor; deep learning  \n1. Introduction  \nThe TIPE (tumor necrosis factor-α-induced protei","cbCaip3vOOKbye4A","https://ap.wps.com/l/cbCaip3vOOKbye4A","pdf",7547826,18,"English","# Introduction\n## TIPE Family and TIPE3 Background\n## Rationale for Inhibiting TIPE3 in Cancer","[{\"question\":\"What role does TIPE3 play in cancer cells?\",\"answer\":\"TIPE3 functions as a transfer protein for lipid second messengers and is upregulated in cancers, where it promotes proliferation, migration, invasion, and inhibits apoptosis.\"},{\"question\":\"How were potential TIPE3 inhibitors identified in this study?\",\"answer\":\"The study used deep-learning-driven virtual screening integrating DFCNN with docking (Autodock Vina), DeepBindBC, molecular dynamics, and metadynamics on compounds from a ZINC dataset.\"},{\"question\":\"Which candidates showed anti-tumor activity and what was their selectivity?\",\"answer\":\"Three small-molecule compounds, K784-8160, E745-0011, and 7238-1516, showed significant anti-tumor activity in vitro. E745-0011 and 7238-1516 demonstrated selective cytotoxicity toward tumor cells with high TIPE3 expression, with little or no effect on normal human cells or low-TIPE3 tumor cells.\"}]","Small-Molecule Inhibitors of TIPE3 Protein Identified through Deep Learning Suppress Cancer Cell Growth In Vitro | PDF",1790040716,45]