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Comprehensive Overview of 3-Bromo-4-(difluoromethyl)pyridine (CAS No. 114468-05-2): Properties, Applications, and Industry Insights

3-Bromo-4-(difluoromethyl)pyridine (CAS No. 114468-05-2) is a halogenated pyridine derivative that has garnered significant attention in pharmaceutical and agrochemical research due to its unique structural features. The compound's molecular formula (C₆H₄BrF₂N) combines a pyridine core with bromine and difluoromethyl substituents, making it a versatile intermediate for synthesizing complex molecules. Researchers frequently search for "3-Bromo-4-(difluoromethyl)pyridine synthesis" or "CAS 114468-05-2 suppliers," reflecting its commercial and scientific relevance.

In recent years, the demand for fluorinated pyridines like 3-Bromo-4-(difluoromethyl)pyridine has surged, driven by their role in drug discovery. Fluorine incorporation often enhances metabolic stability and bioavailability, a hot topic in "medicinal chemistry optimization." The compound's bromine atom also facilitates cross-coupling reactions, such as Suzuki-Miyaura or Buchwald-Hartwig couplings, which are pivotal in constructing heterocyclic scaffolds for kinase inhibitors or antimicrobial agents.

From an industrial perspective, CAS 114468-05-2 is often discussed alongside "high-value chemical intermediates" and "custom synthesis services." Its applications extend to materials science, where fluorinated pyridines contribute to liquid crystals or OLED materials. Environmental considerations, such as "green chemistry in halogenation," are increasingly shaping synthesis protocols for this compound, aligning with global sustainability trends.

Analytical data for 3-Bromo-4-(difluoromethyl)pyridine typically includes NMR spectra (1H, 13C, 1?F), HPLC purity, and melting point characterization. These metrics are critical for quality control, as highlighted in searches like "pyridine derivative analytical methods." The compound's stability under various conditions (pH, temperature) is another focal point for formulators.

Emerging trends link 114468-05-2 to "AI-assisted molecular design," where machine learning models predict optimal substitution patterns for target activity. This synergy between experimental chemistry and computational tools underscores the compound's future potential in accelerating hit-to-lead optimization pipelines.

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