Global InformationMolecular descriptor information
Molecular descriptors play a fundamental role in chemistry, pharmaceutical sciences, environmental protection policy, and health researches, as well as in quality control, being the way molecules, thought of as real bodies, are transformed into numbers, allowing some mathematical treatment of the chemical information contained in the molecule. This was defined by Todeschini and Consonni as:
"The molecular descriptor is the final result of a logic and mathematical procedure which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment."[1]
By this definition, the molecular descriptors are divided into two main categories: experimental measurements, such as log P, molar refractivity, dipole moment, polarizability, and, in general, additive physico-chemical properties, and theoretical molecular descriptors, which are derived from a symbolic representation of the molecule and can be further classified according to the different types of molecular representation.[2]
The main classes of theoretical molecular descriptors are: 1) 0D-descriptors (i.e. constitutional descriptors, count descriptors), 2) 1D-descriptors (i.e. list of structural fragments, fingerprints),3) 2D-descriptors (i.e. graph invariants),4) 3D-descriptors (such as, for example, 3D-MoRSE descriptors, WHIM descriptors, GETAWAY descriptors, quantum-chemical descriptors, size, steric, surface and volume descriptors),5) 4D-descriptors (such as those derived from GRID or CoMFA methods, Volsurf). The outspread of artificial intelligence and machine learning to computational chemistry has also lead to various attempts to uncover new descriptors or to find the most predictive ones among some sort of candidates.[3][4]
- ^ Todeschini, Roberto; Consonni, Viviana (2000). Handbook of Molecular Descriptors. Methods and Principles in Medicinal Chemistry. Wiley. doi:10.1002/9783527613106. ISBN 978-3-527-29913-3.
- ^ Mauri, Andrea; Consonni, Viviana; Todeschini, Roberto (2017). "Molecular Descriptors". Handbook of Computational Chemistry. Springer International Publishing. pp. 2065–2093. doi:10.1007/978-3-319-27282-5_51. ISBN 978-3-319-27282-5.
- ^ Mueller, Tim; Kusne, Aaron Gilad; Ramprasad, Rampi (2016-04-01). "Machine Learning in Materials Science". In Parrill, Abby L.; Lipkowitz, Kenny B. (eds.). Reviews in Computational Chemistry. Vol. 29 (1st ed.). Wiley. pp. 186–273. doi:10.1002/9781119148739.ch4. ISBN 978-1-119-10393-6.
- ^ Ghiringhelli, Luca M.; Vybiral, Jan; Levchenko, Sergey V.; Draxl, Claudia; Scheffler, Matthias (2015-03-10). "Big Data of Materials Science: Critical Role of the Descriptor". Physical Review Letters. 114 (10). 105503. arXiv:1411.7437. Bibcode:2015PhRvL.114j5503G. doi:10.1103/PhysRevLett.114.105503. PMID 25815947.