Crystallography and it's Role in Molecular Structures
Crystallography has proven to be among the strongest methods of atomic precise intelligibility of the molecular structures. The spatial structure of atoms, bond lengths and conformational dynamics can be solved by examining how X-rays, neutrons, or electrons, interact with crystalline samples, by analyzing the diffraction patterns. This approach has produced revolutionary effects in chemistry, biology, materials science and drug design, leading to the discovery of the structure of DNA in two strands (the double-helix) and the structure of complex protein folding. Not only does crystallography contribute to the theoretical understanding, but also has practical uses in pharmaceuticals, nanotechnology and crystallographic informatics. Nonetheless, the industry is limited to the fact that high-quality crystals are needed, the dynamic or non-crystalline or amorphous materials are not widely applicable, and dynamic systems cannot be studied. The next steps include combining crystallography with the related technique such as the use of a cryo-electron microscope, the use of machine learning-based structure prediction, and in situ crystallography to address these limitations and expand its use in practical problem solving.
Authors
- S. Suganya (ORCID: https://orcid.org/0009-0009-0490-764X)
- Uma Varma
- Raji Thomas (ORCID: https://orcid.org/0000-0003-3146-0344)
- Santhoshkumar Bhogi (ORCID: https://orcid.org/0000-0001-5486-8292)
- Padmavathi Vustelamuri (ORCID: https://orcid.org/0000-0003-1775-1583)
- Utpal Nath (ORCID: https://orcid.org/0009-0001-5924-171X)
Institutions
- Government of Tamil Nadu (IN)
- Gandhi Medical College & Hospital (IN)
- University of Hyderabad (IN)
- Institute of Engineering (NP)
- Hindu College of Pharmacy (IN)
- Assam Medical College (IN)
Publication Details
- Journal
- Oriental Journal Of Chemistry
- Published
- 2025-10-31
- DOI
- https://doi.org/10.13005/ojc/410539
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00