Structure-based chemical descriptors

Structure-based Chemical Descriptors of Protein-Ligand Interface The EnTESS Method... 

Graphs may be represented in algebraic form as matrices [3-5]. This numerical description of the structure of chemical compounds is essential for the computer manipulation of molecules and for the calculation of various topological indices and graph descriptors [6]. The computation of the E-state indices is based on the adjacency and distance matrices. 

Key Words Biological activity cell-based partitioning chemical descriptors classification clustering distance-based design diversity selection high-throughput screening quantitative structure-activity relationship. 

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. 

Chemical descriptors are in most of the cases obtained with equations that are not known. Even if the references to certain general equations are given, in practice, it is difficult to replicate the results obtained with chemical descriptors. As we have discussed, chemical descriptors based on tridimensional structures are subject to manual optimization, and this may change the descriptor values. But even in the case of other simpler descriptors, we found that using software from two different commercial sources, the results may be different. Even the use of two different versions of the same software may provide different results for the same descriptor. Even descriptors, which seem simple, such as number of double bonds, or of aromatic rings, are critical because they depend on how tautomers and aromaticity are considered in the different software, or are sensitive to the structure format that is used. 

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). 

Structure-based drug design approaches rely on the availability of structural information about protein-ligand complexes. In contrast, ligand-based approaches rely only on the experimental structure-activity relationships for ligands only. As discussed above, QSAR methods are typically used to find correlations between ligands binding affinities and their chemical descriptors. As an innovative use of QSAR approaches, several so-called receptor-dependent quantitative structure-activity relationship (RD-QSAR) methods have been... 

S. Zhang, A. Golbraikh and A. Tropsha, Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces., J. Med. Chem.,... 

Different physical properties and molecular models have been used to define the molecular surface the most common are reported below together with the descriptors proposed as measures of surface areas and molecular volume (- volume descriptors). Molecular surface area and volume are parameters of molecules that are very important in understanding their structure and chemical behaviour such as their ability to bind ligands and other molecules. An analysis of molecular surface shape is also an important tool in QSAR and - drug design-, in particular, both - molecular shape analysis and - Mezey 3D shape analysis were developed to search for similarities among molecules, based on their molecular shape. 

Optimized Approach based on Structural Indices Set OASIS method orbital electronegativity -> quantum-chemical descriptors orbital information indices (/orb)... 

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