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。

IF 2.8 4区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Haiping Zhang
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引用次数: 0

摘要

蛋白质-配体相互作用预测在早期药物开发中至关重要,可以实现大规模的虚拟筛选、药物优化和反向靶标搜索。在这项工作中,我们提出了Graph_RG,这是我们在CASP16小分子轨道的蛋白质配体亲和预测类别中表现最好的模型,实现了0.42的n加权Kendall's Tau,显著优于其他提交的模型(第二好:0.36)。除了准确性之外,Graph_RG是非复杂依赖的,因此表现出卓越的计算效率,运行速度比构象搜索依赖的预测方法快100万倍,因此可以在标准服务器上进行十亿到100亿规模的筛选。我们进一步讨论了Graph_RG的潜在改进,包括数据集优化、原子向量表示增强和模型架构升级。我们还介绍了在大规模药物筛选,反向靶标鉴定和gpcr特异性药物发现方面的潜在更广泛的应用。我们还指出了托管Graph_RG及其衍生模型的交互式web平台的开发,以增强可访问性。通过整合社区反馈和迭代模型改进,该计划弥合了人工智能驱动的预测与实际药物发现之间的差距,促进了计算方法和生物医学应用的进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Proteins-Structure Function and Bioinformatics
Proteins-Structure Function and Bioinformatics 生物-生化与分子生物学
CiteScore
5.90
自引率
3.40%
发文量
172
审稿时长
3 months
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.
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