{"title":"Graph_RG:支配CASP16小分子亲和预测亚类-十亿尺度虚拟筛选的无姿态框架。","authors":"Haiping Zhang","doi":"10.1002/prot.70010","DOIUrl":null,"url":null,"abstract":"<p><p>Protein-ligand interaction prediction is pivotal in early-stage drug development, enabling large-scale virtual screening, drug optimization, and reverse target searching. In this work, we present Graph_RG, our top-performing model in the CASP16 small molecule track's protein-ligand affinity prediction category, achieving a N-weighted Kendall's Tau of 0.42-significantly outperforming other submissions (second-best: 0.36). Beyond accuracy, Graph_RG is noncomplex dependent, hence exhibits exceptional computational efficiency, operating > 100 000× faster than conformation-search dependent prediction methods, thus enabling billion- to 10-billion-scale screening on standard servers. We further discuss the potential improvements for Graph_RG, including dataset optimization, atomic vector representation enhancements, and model architecture upgrades. We also introduce the potential broader applications in large-scale drug screening, reverse target identification, and GPCR-specific drug discovery. We also point out the development of an interactive web platform hosting Graph_RG and its derivative models to enhance accessibility. By integrating community feedback and iterative model refinement, this initiative bridges the gap between AI-driven predictions and practical drug discovery, fostering advancements in both computational methodologies and biomedical applications.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Graph_RG: Dominating CASP16's Small Molecule Affinity Prediction Subcategory-A Pose-Free Framework for Billion-Scale Virtual Screening.\",\"authors\":\"Haiping Zhang\",\"doi\":\"10.1002/prot.70010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Protein-ligand interaction prediction is pivotal in early-stage drug development, enabling large-scale virtual screening, drug optimization, and reverse target searching. In this work, we present Graph_RG, our top-performing model in the CASP16 small molecule track's protein-ligand affinity prediction category, achieving a N-weighted Kendall's Tau of 0.42-significantly outperforming other submissions (second-best: 0.36). Beyond accuracy, Graph_RG is noncomplex dependent, hence exhibits exceptional computational efficiency, operating > 100 000× faster than conformation-search dependent prediction methods, thus enabling billion- to 10-billion-scale screening on standard servers. We further discuss the potential improvements for Graph_RG, including dataset optimization, atomic vector representation enhancements, and model architecture upgrades. We also introduce the potential broader applications in large-scale drug screening, reverse target identification, and GPCR-specific drug discovery. We also point out the development of an interactive web platform hosting Graph_RG and its derivative models to enhance accessibility. By integrating community feedback and iterative model refinement, this initiative bridges the gap between AI-driven predictions and practical drug discovery, fostering advancements in both computational methodologies and biomedical applications.</p>\",\"PeriodicalId\":56271,\"journal\":{\"name\":\"Proteins-Structure Function and Bioinformatics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proteins-Structure Function and Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1002/prot.70010\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proteins-Structure Function and Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/prot.70010","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Graph_RG: Dominating CASP16's Small Molecule Affinity Prediction Subcategory-A Pose-Free Framework for Billion-Scale Virtual Screening.
Protein-ligand interaction prediction is pivotal in early-stage drug development, enabling large-scale virtual screening, drug optimization, and reverse target searching. In this work, we present Graph_RG, our top-performing model in the CASP16 small molecule track's protein-ligand affinity prediction category, achieving a N-weighted Kendall's Tau of 0.42-significantly outperforming other submissions (second-best: 0.36). Beyond accuracy, Graph_RG is noncomplex dependent, hence exhibits exceptional computational efficiency, operating > 100 000× faster than conformation-search dependent prediction methods, thus enabling billion- to 10-billion-scale screening on standard servers. We further discuss the potential improvements for Graph_RG, including dataset optimization, atomic vector representation enhancements, and model architecture upgrades. We also introduce the potential broader applications in large-scale drug screening, reverse target identification, and GPCR-specific drug discovery. We also point out the development of an interactive web platform hosting Graph_RG and its derivative models to enhance accessibility. By integrating community feedback and iterative model refinement, this initiative bridges the gap between AI-driven predictions and practical drug discovery, fostering advancements in both computational methodologies and biomedical applications.
期刊介绍:
PROTEINS : Structure, Function, and Bioinformatics publishes original reports of significant experimental and analytic research in all areas of protein research: structure, function, computation, genetics, and design. The journal encourages reports that present new experimental or computational approaches for interpreting and understanding data from biophysical chemistry, structural studies of proteins and macromolecular assemblies, alterations of protein structure and function engineered through techniques of molecular biology and genetics, functional analyses under physiologic conditions, as well as the interactions of proteins with receptors, nucleic acids, or other specific ligands or substrates. Research in protein and peptide biochemistry directed toward synthesizing or characterizing molecules that simulate aspects of the activity of proteins, or that act as inhibitors of protein function, is also within the scope of PROTEINS. In addition to full-length reports, short communications (usually not more than 4 printed pages) and prediction reports are welcome. Reviews are typically by invitation; authors are encouraged to submit proposed topics for consideration.