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<p>Artificial intelligence–enabled structure prediction and molecular dynamics simulations have transformed G-protein-coupled receptor (GPCR) research, enabling rapid exploration of receptor conformational landscapes. While these advances have dramatically expanded predictive capability, they have also introduced a critical challenge: structural plausibility is increasingly interpreted as mechanistic certainty without adequate qualification of uncertainty, state ambiguity, or reproducibility. Here, we define <i>decision-grade GPCR modeling</i> as computational modeling explicitly qualified to support a<i> stated biological or translational decision</i>, rather than exploratory structure generation alone. This correspondence builds on recent advances published in <i>Cell Biochemistry and Biophysics</i> by proposing a practical, operational framework for evaluating whether AI-derived GPCR models are fit to support hypothesis generation, mechanistic inference,or translational prioritization. We emphasize explicit declaration of modeling intent, operational definition of activation states, uncertainty quantification, integration of orthogonal validation, global reproducibility, and ethical responsibility across the innovation pipeline. By shifting emphasis from model generation to model qualification, this framework aims to strengthen mechanistic credibility, reduce irreproducibility, and enhance global usability of AI-enabled GPCR research.</p>

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From “accurate structures” to decision-grade mechanisms: advancing AI-enabled GPCR biophysics toward globally reproducible standards

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • M Vijayasimha,
  • M Srikanth,
  • Abrar Hussain Joo

摘要

Artificial intelligence–enabled structure prediction and molecular dynamics simulations have transformed G-protein-coupled receptor (GPCR) research, enabling rapid exploration of receptor conformational landscapes. While these advances have dramatically expanded predictive capability, they have also introduced a critical challenge: structural plausibility is increasingly interpreted as mechanistic certainty without adequate qualification of uncertainty, state ambiguity, or reproducibility. Here, we define decision-grade GPCR modeling as computational modeling explicitly qualified to support a stated biological or translational decision, rather than exploratory structure generation alone. This correspondence builds on recent advances published in Cell Biochemistry and Biophysics by proposing a practical, operational framework for evaluating whether AI-derived GPCR models are fit to support hypothesis generation, mechanistic inference,or translational prioritization. We emphasize explicit declaration of modeling intent, operational definition of activation states, uncertainty quantification, integration of orthogonal validation, global reproducibility, and ethical responsibility across the innovation pipeline. By shifting emphasis from model generation to model qualification, this framework aims to strengthen mechanistic credibility, reduce irreproducibility, and enhance global usability of AI-enabled GPCR research.