Biological activity Model

The first stage in data acquisition is the identification of the task that is, we have to know what kind of physical properties/biological activities we are going to model. 

Systems can possess different extents of complexity. To measure complexity, the information content of the system can be used. Application of information theory is increasingly finitful for modeling biological activities with regard to the symmetry of molecules. 

The most important task of modeling is prediction. The model itself is needed for evaluating the biological activities (and/or physical properties) of compounds, where it is either difficult or costly to measure the activities experimentally. 

The HYBOT descriptors were successfully applied to the prediction of the partition coefficient log P (>i--octanol/water) for small organic componnds with one acceptor group from their calculated polarizabilities and the free energy acceptor factor C, as well as properties like solubility log S, the permeability of drugs (Caco-2, human skin), and for the modeling of biological activities. 

The possibilities for the application for neural networks in chemistry arc huge [10. They can be used for various tasks for the classification of structures or reactions, for establishing spcctra-strncturc correlations, for modeling and predicting biological activities, or to map the electrostatic potential on molecular surfaces. 

GAs or other methods from evolutionary computation are applied in various fields of chemistry Its tasks include the geometry optimization of conformations of small molecules, the elaboration of models for the prediction of properties or biological activities, the design of molecules de novo, the analysis of the interaction of proteins and their ligands, or the selection of descriptors [18]. The last application is explained briefly in Section 9.7.6. 

The fundamental assumption of SAR and QSAR (Structure-Activity Relationships and Quantitative Structure-Activity Relationships) is that the activity of a compound is related to its structural and/or physicochemical properties. In a classic article Corwin Hansch formulated Eq. (15) as a linear frcc-cncrgy related model for the biological activity (e.g.. toxicity) of a group of congeneric chemicals [37, in which the inverse of C, the concentration effect of the toxicant, is related to a hy-drophobidty term, FI, an electronic term, a (the Hammett substituent constant). Stcric terms can be added to this equation (typically Taft s steric parameter, E,). 

Quantitative Structure—Activity Relationships (QSAR). Quantitative Stmcture—Activity Relationships (QSAR) is the name given to a broad spectmm of modeling methods which attempt to relate the biological activities of molecules to specific stmctural features, and do so in a quantitative manner (see Enzyme INHIBITORS). The method has been extensively appHed. The concepts involved in QSAR studies and a brief overview of the methodology and appHcations are given here. 

Conformational analysis has been used to find and predict conformations which maximize antibiotic activity, using x-ray crystal stmctures coupled with nmr and cd spectra. An early approach utilized the Dale diamond lattice conformational model (480), which was extended to other diamond lattice models (472,481—483). Other studies have reHed on nmr data (225,484—491). However, extensive correlations between conformation and biological activity have not been successful (486,492). 

Much of the experimental work in chemistry deals with predicting or inferring properties of objects from measurements that are only indirectly related to the properties. For example, spectroscopic methods do not provide a measure of molecular stmcture directly, but, rather, indirecdy as a result of the effect of the relative location of atoms on the electronic environment in the molecule. That is, stmctural information is inferred from frequency shifts, band intensities, and fine stmcture. Many other types of properties are also studied by this indirect observation, eg, reactivity, elasticity, and permeabiHty, for which a priori theoretical models are unknown, imperfect, or too compHcated for practical use. Also, it is often desirable to predict a property even though that property is actually measurable. Examples are predicting the performance of a mechanical part by means of nondestmctive testing (qv) methods and predicting the biological activity of a pharmaceutical before it is synthesized. 

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