Chemical descriptors, QSAR

Liu W, Yi P, Tang Z (2006) QSPR Models for various proeprties of polymethacrylates based on quantum chemical descriptors. QSAR Comb Sci 25 936-943... 

Thakur, A., Thakur, M., Kakani, N., Joshi, A., Thakur, S. and Gupta, A. (2004a) Application of topological and physico-chemical descriptors QSAR study of phenylamino-acridine descriptors derivatives. ARKIVOC, (xiv), 36- 3. 

Quantum chemical descriptors such as atomic charges, HOMO and LUMO energies, HOMO and LUMO orbital energy differences, atom-atom polarizabilities, super-delocalizabilities, molecular polarizabilities, dipole moments, and energies sucb as the beat of formation, ionization potential, electron affinity, and energy of protonation are applicable in QSAR/QSPR studies. A review is given by Karelson et al. [45]. 

The correlations between chemical descriptors of molecular properties and biological activity, especially the activity of herbicides and/or plant growth regulators has been described (12). Several alternatives or improvements on the Hansch-Fujita QSAR system have been developed (13—15). 

With the development of accurate computational methods for generating 3D conformations of chemical structures, QSAR approaches that employ 3D descriptors have been developed to address the problems of 2D QSAR techniques, e.g., their inability to distinguish stereoisomers. The examples of 3D QSAR include molecular shape analysis (MSA) [34], distance geometry [35,36], and Voronoi techniques [37]. 

In the last decades not only thousands of chemical descriptors but also many advanced, powerful modeling algorithms have been made available, The older QSAR models were linear equations with one or a few parameters. Then, other tools have been introduced, such as artificial neural network, fuzzy logic, and data mining algorithms, making possible non linear models and automatic generation of mathematical solutions. 

More typically the process of building up the QSAR models requires more complex chemical information. For a set of compounds, with known property value, the descriptors are calculated. The process of model building proceeds through a reduction of the molecular descriptors, in order to indentify the most important ones. Then, using these selected chemical descriptors and a suitable algorithm, the model is developed. Finally, the model so obtained has to be validated. 

Karelson, M. and Lobanov, V.S. (1996). Quantum-chemical descriptors in QSAR/ QSPR studies. Chemical Reviews 96 1027-1043. 

Dearden, J. C. (1990) Physico-chemical descriptors. In Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology. Karcher, W. and Devillers, J., Eds., pp. 25-60. Kluwer Academic Publisher, Dordrecht, The Netherlands. 

PLS does not appear to have been applied to QSAR of flavours, and although much process has been made in the field of flavour chemistry, a greater insight into odour quality could be derived by the concept of applying many physico-chemical descriptors to the appropriate molecules. 

There are several properties of a chemical that are related to exposure potential or overall reactivity for which structure-based predictive models are available. The relevant properties discussed here are bioaccumulation, oral, dermal, and inhalation bioavailability and reactivity. These prediction methods are based on a combination of in vitro assays and quantitative structure-activity relationships (QSARs) [3]. QSARs are simple, usually linear, mathematical models that use chemical structure descriptors to predict first-order physicochemical properties, such as water solubility. Other, similar models can then be constructed that use the first-order physicochemical properties to predict more complex properties, including those of interest here. Chemical descriptors are properties that can be calculated directly from a chemical structure graph and can include abstract quantities, such as connectivity indices, or more intuitive properties, such as dipole moment or total surface area. QSAR models are parameterized using training data from sets of chemicals for which both structure and chemical properties are known, and are validated against other (independent) sets of chemicals. 

Morall, S.W., M.J. Rosen, Y.P. Zhu, D.J. Versteeg, and S.D. Dyer. 1997. Physical chemical descriptors for the development of aquatic toxicity QSARs for surfactants, Proceedings of the 7th Internatl. Workshop on QSARs in Environmental Science, SETAC Press, USA, in press. 

Chemical information can be expressed in a number of ways. Chemical descriptors are commonly used in QSAR. There are thousands of chemical descriptors possibly used in QSAR. Indeed, programs such as DRAGON and CODESSA, just to name a couple, can calculate thousands of parameters [7, 8]. Chemical descriptors have different complexities. Some of them do not require any information on the structure, such as molecular weight. Others refer to the bidimensional structure, such as the number of double bonds. While some others require tridimensional information, such as molecular volume. Chemical descriptors can be geometrical, topological, quantum-mechanical, electrostatical, etc. There are a few books describing chemical descriptors [7, 8],... 

Chemical descriptors can be easily obtained from the SMILES structure, which is another simple way to get chemical information useful for QSAR modeling purposes [9]. 

The third fundamental component in the QSAR model is the mathematical algorithms. Many methods have been used, and in the last years, there has been an increase of the methods, and hence, quite probably this trend will continue, introducing many other methods [4—6]. Classical QSAR methods, used decades ago, were simple linear relationships. Corwin Hansch has been a pioneer of these methods [2]. An example can be the linear relationship between the fish toxicity and the partition coefficient between octanol and water, called Kow [3]. Kow, and its logarithm, called log P, is still the most popular chemical descriptor used in QSAR models for fish toxicity, and it is the base of software programs used by the US Environmental Protection Agency for fish toxicity [11]. The theoretical assumptions for the use of log P are that (1) octanol mimics the lipophylic component of the fish cell, and (2) the toxic effect is due to the adsorption of the chemical substance into the cell. 

Other chemical descriptors have been used to model other properties, or to improve the QSAR models with log P. The attempt has been to avoid the errors of the QSAR models. Indeed, some chemicals were not correctly modeled, and other descriptors have been introduced, producing multilinear relationships. The theoretical assumptions were modeled keeping into account other physico-chemical parameters, such as chemical reactivity, through chemical descriptors, such as the energy of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO). 

This shift of the use forced a different evaluation of the model, toward a more statistical evaluation. The first QSAR models were evaluated in their capability to fit the property data with one or more chemical descriptors, but no proof was given about the predictivity of the model. Today, a number of criteria are requested to check if a model is predictive or not [13-15],... 

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). 

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