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<p>Molecular shape is a key determinant of the structural properties and intermolecular interactions of privileged scaffolds, such as biaryl and oligoaryl compounds, which are widely used in the design of drugs, catalysts, and functional materials. Despite the well-recognized importance of molecular shape diversity and increased three-dimensionality, the chemical space of biaryl compounds remains dominated by linear and disk-like structures featuring <i>para</i> and <i>meta</i> substitution patterns. In contrast, more sphere-like bis-<i>ortho</i>-substituted biaryl scaffolds have been underexplored, because of the dearth of synthetic methods for generating focused libraries of <i>ortho</i> analogs in a synthetically efficient manner. We report herein the development of regioselective nucleophile/electrophile couple-mediated cyclic sulfone ring remodeling that can streamline access to a wide range of biaryl and oligoaryl scaffolds, featuring aliphatic and (hetero)aromatic amines, phenols, (thio)ethers, and phosphines. To facilitate prediction of the regiochemistry of structurally complex biaryl products, a machine learning model based on the previously unexplored Bernoulli Naïve Bayes classifier is developed. The integrated experimental, computational, and data-driven approach charts the course for mitigating structural biases and exploring the underpopulated biaryl chemical space in medicinal chemistry, catalysis, and materials science.</p>

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Cyclic sulfone ring remodeling enables molecular shape diversity-oriented synthesis of privileged biaryl and oligoaryl motifs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月24日

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  • Viet D. Nguyen,
  • Sachchida Nand,
  • Ramon Trevino,
  • Arka Porey,
  • William T. Thompson,
  • Graham C. Haug,
  • Seth O. Fremin,
  • Shree Krishna Dhakal,
  • William B. Hughes,
  • Dylan P. Moran,
  • Johant Lakey-Beitia,
  • Chandan Kumar Giri,
  • Patrick Manning-Lorino,
  • Samuel G. Greco,
  • Hadi D. Arman,
  • Oleg V. Larionov

摘要

Molecular shape is a key determinant of the structural properties and intermolecular interactions of privileged scaffolds, such as biaryl and oligoaryl compounds, which are widely used in the design of drugs, catalysts, and functional materials. Despite the well-recognized importance of molecular shape diversity and increased three-dimensionality, the chemical space of biaryl compounds remains dominated by linear and disk-like structures featuring para and meta substitution patterns. In contrast, more sphere-like bis-ortho-substituted biaryl scaffolds have been underexplored, because of the dearth of synthetic methods for generating focused libraries of ortho analogs in a synthetically efficient manner. We report herein the development of regioselective nucleophile/electrophile couple-mediated cyclic sulfone ring remodeling that can streamline access to a wide range of biaryl and oligoaryl scaffolds, featuring aliphatic and (hetero)aromatic amines, phenols, (thio)ethers, and phosphines. To facilitate prediction of the regiochemistry of structurally complex biaryl products, a machine learning model based on the previously unexplored Bernoulli Naïve Bayes classifier is developed. The integrated experimental, computational, and data-driven approach charts the course for mitigating structural biases and exploring the underpopulated biaryl chemical space in medicinal chemistry, catalysis, and materials science.