molekululegra

Molekululegra is a cross-disciplinary concept that focuses on the representation and analysis of molecular structures using graph-like models and associated energy landscapes. Proponents describe it as an approach that combines experimental data with computational methods to map how atoms connect and interact within a molecule, and how these connections influence properties and reactivity. The term is a neologism that blends the word molecule with graph (and related notions of topology). It has appeared in specialized publications and online discussions since the early 2020s, but it is not yet standardized or widely adopted in formal curricula.

Methods typically involve integrating spectroscopic and imaging data (for example Raman or infrared spectra, and nanoscale imaging such as atomic force microscopy or electron microscopy) with chemoinformatics, graph theory, and molecular simulations. The goal is to produce graph representations—nodes as atoms, edges as bonds or interactions—that can be analyzed to infer conformations, reaction pathways, or material properties. Advanced workflows may include energy landscape sampling, topology-based descriptors, and visualization tools that highlight connectivity and clusters within large molecules or molecular ensembles.

Applications span structural elucidation, mechanism studies in catalysis, materials science, drug design, and molecular electronics. Limitations include variability in how graphs are defined for three-dimensional structures, sensitivity to data quality, computational demands, and the lack of universally accepted standards for notation and interpretation. See also chemoinformatics, molecular graph theory, and topological data analysis.