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<p>The ongoing opioid crisis has highlighted the urgent need for novel analgesics with low addiction potential that provide effective pain relief. In this study, we present an integrated computational framework that combines quantum chemical descriptors with classical molecular descriptors to predict the bioactivity of synthetic opioid analogs across multiple opioid receptor subtypes. Using a curated dataset of over 7000 compounds with experimentally measured activities against the delta opioid receptor (DOR), kappa opioid receptor (KOR), and mu opioid receptor (MOR), we generated several classical and quantum chemical descriptors. These high-dimensional descriptors were used to train and validate several supervised models, including random forest (RF), gradient boosting (GB) with XGBoost, support vector regression (SVR), and linear regression. The RF achieved the highest predictive performance, outperforming GB, SVR, and linear regression across all metrics. Among the descriptors, dipole moment and TPSA were the most effective, significantly enhancing model interpretability and mechanistic insight into ligand–receptor interactions. Clustering and chemical space visualization were used to identify ligand subpopulations with receptor subtype preferences, guiding rational drug design. The integration of quantum-informed descriptors, classical physicochemical features, structural clustering, and interpretable machine learning—particularly RF represents a novel contribution to computational opioid ligand design and advances the field by enabling large-scale, mechanistic analysis of opioid bioactivity. Our approach uniquely combines quantum-informed GNN descriptors with interpretable ML and classical molecular descriptors, enabling mechanistic insights beyond classical QSAR. Unlike prior works relying on empirical fingerprints or small-scale docking, this quantum–ML fusion reveals mechanistic insights into stereo electronic features driving biased agonism, offering a scalable platform for GPCR drug design amid the opioid crisis.</p>

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AI-driven quantum chemical exploration of multi-target synthetic opioid analogs for enhanced therapeutic profiles

  • 期刊论文

  • 发表日期：

    2026年4月25日

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  • Mohsen Sargolzaei,
  • Hossein Nikoofard

摘要

The ongoing opioid crisis has highlighted the urgent need for novel analgesics with low addiction potential that provide effective pain relief. In this study, we present an integrated computational framework that combines quantum chemical descriptors with classical molecular descriptors to predict the bioactivity of synthetic opioid analogs across multiple opioid receptor subtypes. Using a curated dataset of over 7000 compounds with experimentally measured activities against the delta opioid receptor (DOR), kappa opioid receptor (KOR), and mu opioid receptor (MOR), we generated several classical and quantum chemical descriptors. These high-dimensional descriptors were used to train and validate several supervised models, including random forest (RF), gradient boosting (GB) with XGBoost, support vector regression (SVR), and linear regression. The RF achieved the highest predictive performance, outperforming GB, SVR, and linear regression across all metrics. Among the descriptors, dipole moment and TPSA were the most effective, significantly enhancing model interpretability and mechanistic insight into ligand–receptor interactions. Clustering and chemical space visualization were used to identify ligand subpopulations with receptor subtype preferences, guiding rational drug design. The integration of quantum-informed descriptors, classical physicochemical features, structural clustering, and interpretable machine learning—particularly RF represents a novel contribution to computational opioid ligand design and advances the field by enabling large-scale, mechanistic analysis of opioid bioactivity. Our approach uniquely combines quantum-informed GNN descriptors with interpretable ML and classical molecular descriptors, enabling mechanistic insights beyond classical QSAR. Unlike prior works relying on empirical fingerprints or small-scale docking, this quantum–ML fusion reveals mechanistic insights into stereo electronic features driving biased agonism, offering a scalable platform for GPCR drug design amid the opioid crisis.