| Crystallographic Insights into Urea Solvates: Data-Driven Approach Using the Cambridge Structural Database and Artificial Intelligence |
| کد مقاله : 1345-ICOC |
| نویسندگان |
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غزاله خورشیدی *1، وحید فرزانه2 1دانشگاه تربیت مدرس 2شرکت داروسازی سیناژن |
| چکیده مقاله |
| Understanding solvent effects on urea-based molecular systems is crucial for rational design in organic and supramolecular chemistry, as well as for controlling solid-state properties relevant to pharmaceutical development [1,2]. This study analyzed crystallographic data from the Cambridge Structural Database (CSD, version 5.45, 2024) for urea and its derivatives solvated in three polar aprotic solvents: dimethyl sulfoxide (DMSO), N,N-dimethylformamide (DMF), and N,N-dimethylacetamide (DMA). We examined more than 500 crystal structures and selected 60 representative cases for an in-depth analysis of their structural and statistical characteristics. Key parameters like space groups, cell dimensions, density, and R-factors were extracted and analyzed using data-driven and AI-assisted clustering and correlation tools. The analysis revealed clear solvent-driven patterns in hydrogen-bonding motifs and symmetry; for instance, DMSO solvates often form low-symmetry networks packed with hydrogen bonds, while DMF and DMA exhibit more orderly, symmetric arrangements. Principal Component Analysis (PCA) and hierarchical clustering revealed distinct solvent-dependent structural domains. Integrating CSD data mining with AI-assisted pattern recognition provided new insights into the solid-state behavior of organic compounds (Scheme 1). |
| کلیدواژه ها |
| Urea solvates, Cambridge Structural Database, Artificial Intelligence (AI). |
| وضعیت: پذیرفته شده |