Chemical descriptors types

The table shows a number of representative descriptor types (there are many more) that can be used to define chemical spaces. Each descriptor adds a dimension (with discrete or continuous value ranges) to the chemical space representation (e.g., selection of 18 descriptors defines an 18-dimensional space). Axes of chemical space are orthogonal only if the applied molecular descriptors are uncorrelated (which is, in practice, hardly ever the case). 

The primary supposition of any toxicological QSAR is that the potency of a compound is dependent upon its molecular structure, which is typically quantified by chemical properties (Schultz et al., 2002). Chemical descriptors include a variety of types, including atom, substituent, and molecular parameters. The most transparent of these are the molecular-based empirical and quantum chemical descriptors. Empirical descriptors are measured descriptors and include physicochemical properties such as hydrophobicity (Dearden, 1990). Quantum chemical properties are theoretical descriptors and include charge and energy values (Karelson et al., 1996). Physicochemical and quantum chemical descriptors are for the most part easily interpretable with regard to how that property may be related to toxicity. The classic example of this, the partitioning of a toxicant between aqueous and lipid phases, has been used as a measure of hydrophobicity for over a century (Livingstone, 2000). 

Many different types of QSAR models and chemical descriptors for a wide range of endpoints are developed and published over the years. This makes QSAR a very flexible technique to be adapted for many different situations and a quite powerful technique that can provide a wealth of information. This has also a great potential for new QSAR models with every new experimental data. 

Many thousands of descriptor types have been developed over the years of chemical research. This short review is not intended to summarize these efforts in detail. Todeschini and Consonni have already provided a reference book for this purpose. Commercial and academic software for generating molecular descriptors has proliferated over the recent years. Todeschini s book lists software packages known in the year 2000. Speed of calculation, descriptor quality and diversity should be considered in selecting software. For example, absorption, distribution, metabolism, excretion, toxicity (ADMET) Predictor (Simulations Plus, Inc.) works at a rate of 250,000 molecules per horn calculating 272 molecular and 44 atomic descriptors in the following categories ... 

Verhaar, H J., Urrestarazu R2unos, E. and Hermens, J.L. (19%). Classifying Environmental Pollutants. 2 Separation of Qass 1 (Baseline Toxicity) and Class 2 ( Polar Narcosis ) Type Com-poimds Based on Chemical Descriptors. J.Chemom., 10,149-162. 

The third chapter, Quantitative Structure-Cytotoxicity Relationship of Bioactive Heterocycles by the Semi-empirical Molecular Orbital Method with the Concept of Absolute Hardness by Mariko Ishihara, Hiroshi Sakagami, Masami Kawase, and Noboru Motohashi, presents the relationship between the cytotoxicity (defined as 50% cytotoxic concentration) of heterocycles such as phenoxazine, 5-trifluoromethyloxazoles, O-heterocycles such as 3-formylchromone and coumarins, and vitamin K2 derivatives against some tumor cell lines and 15 chemical descriptors. The results suggest the importance of selecting the most appropriate descriptors for each cell type and compound. The review is of interest as it represents the relationship of the molecular structures with the cytotoxic activity of these heterocycles. 

Lovasz-Pelikan index spectral indices (0 eigenvalues of the adjacency matrix) LOVIs = LOcal Vertex Invariants local invariants Lowdin population analysis quantum-chemical descriptors Lowest-Observed-Effect Level biological activity indices (0 toxicological indices) lowest unoccupied molecular orbital quantum-chemical descriptors lowest unoccupied molecular orbital energy quantum-chemical descriptors LUDI energy function scoring functions Lu index —> hyper-Wiener-type indices... 

Hermens, J.L.M. (1996) Classifying environmental pollutants. 2. Separation of class 1 (baseline toxicity) and class 2 ( polar narcosis ) type compounds based on chemical descriptors. 

Like the continuous physico-chemical descriptor Z variables, indicators of the presence or absence of certain substructures have also been treated by multiple regression analysis. As modified by Fujita and Ban (Seydel and Schaper, 1979), this group contribution method can be a useful alternative to the LFER approach, if only limited knowledge is available about the relevant molecular properties or no uniform physico-chemical descriptors for the various compounds in the data set are accessible. For activities and properties of compounds that may be attributed to the occurrence of certain substructures in the molecules (e.g. biodegradation section 4.8), Free-Wilson-type substructure models have their major application in environmental sciences. 

Verhaar, H. J. M., Ramos, E. U. and Mermens, J. L. M. (1995) Classifying environmental pollutants 2. Separation of class 1 (baseline toxicity) and class 2 (polar narcosis) type compounds based on chemical descriptors, in Predictive Methods in Aquatic Toxicology (by H. J. M. Verhaar). Thesis, Utrecht University, Utrecht. 

In computational chemistry, the main use of PLS is to model the relationship between, on the one hand, computed and measured variables that together characterize the structural variation of a set of N compounds and, on the other hand, biological response variables or other interesting properties measured on the same N substances. " The former comprise the matrix X-of predictor variables, and the latter the matrix Y of response variables. These matrices have dimensions N x K and N X M, respectively. We note that the X variables are usually not independent, and hence they should not be called by that name, but rather predictors or simply X variables. The example used here to illustrate PLS is of this type, where the structural variation of iV = 15 compounds is translated to iV = 15 rows of an X matrix using X = 8 measured and calculated quantum chemical descriptors. The biological response of these compounds is described by a Y matrix comprising M = S aquatic toxicity responses. 

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