Compound selection structure-activity relationship models

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. 

Although the above methodologies proved to be very successful in identifying active kinase inhibitors, they utilized "generic" kinase models and did not address selectivity issues. An interesting recent report has attempted to create quantitative structure-activity relationship (QSAR) models based on data sets of compounds tested against multiple kinases [33]. 

A series of pyrazolo[3,4-, pyridazinones 430 and analogues, potentially useful as peripheral vasodilators, were synthesized and evaluated as inhibitors of PDE5 extracted from human platelets. Several of them showed ICso values in the range 0.14-1.4 pM. A good activity and selectivity profile versus PDE6 was found for compound 430 (6-benzyl-3-methyl-l-isopropyl-4-phenylpyrazolo[3,4-r/]pyridazin-7(6/7)-one). Structure-activity relationship studies demonstrated the essential role played by the benzyl group at position 6 of the pyrazolopyridazine system. Other types of pyridazinones fused with five- and six-membered heterocycles (pyrrole, isoxazole, pyridine, and dihydropyridine), as well as some open-chain models were prepared and evaluated. Besides the pyrazole, the best of the fused systems proved to be isoxazole and pyridine <2002MI227>. 

Since one of the main aims of green chemistry is to reduce the use and/or production of toxic chemicals, it is important for practitioners to be able to make informed decisions about the inherent toxicity of a compound. Where sufficient ecotoxicological data have been generated and risk assessments performed, this can allow for the selection of less toxic options, such as in the case of some surfactants and solvents [94, 95]. When toxicological data are limited, for example, in the development of new pharmaceuticals (see Section 15.4.3) or other consumer products, there are several ways in which information available from other chemicals may be helpful to estimate effect measures for a compound where data are lacking. Of these, the most likely to be used are the structure-activity relationships (SARs, or QSARs when they are quantitative). These relationships are also used to predict chemical properties and behavior (see Chapter 16). There often are similarities in toxicity between chemicals that have related structures and/or functional subunits. Such relationships can be seen in the progressive change in toxicity and are described in QSARs. When several chemicals with similar structures have been tested, the measured effects can be mathematically related to chemical structure [96-98] and QSAR models used to predict the toxicity of substances with similar structure. Any new chemicals that have similar structures can then be assumed to elicit similar responses. 

One may think of an iterative model for the preclinical discovery screening cycle. A large number of compounds are to be mined for compounds that are active for example, that bind to a particular target. The compounds may come from different sources such as vendor catalogues, corporate collections, or combinatorial chemistry projects. In fact, the compounds need only to exist in a virtual sense, because in silico predictions in the form of a model can be made in a virtual screen (Section 8) which can then be used to decide which compounds should be physically made and tested. A mapping from the structure space of compounds to the descriptor space or property space provides covariates or explanatory variables that can be used to build predictive models. These models can help in the selection process, where a subset of available molecules is chosen for the biological screen. The experimental results of the biological screen (actives and inactives, or numeric potency values) are then used to learn more about the structure-activity relationship (SAR) which leads to new models and a new selection of compounds as the cycle renews. 

When compounds are selected according to SMD, this necessitates the adequate description of their structures by means of quantitative variables, "structure descriptors". This description can then be used after the compound selection, synthesis, and biological testing to formulate quantitative models between structural variation and activity variation, so called Quantitative Structure Activity Relationships (QSARs). For extensive reviews, see references 3 and 4. With multiple structure descriptors and multiple biological activity variables (responses), these models are necessarily multivariate (M-QSAR) in their nature, making the Partial Least Squares Projections to Latent Structures (PLS) approach suitable for the data analysis. PLS is a statistical method, which relates a multivariate descriptor data set (X) to a multivariate response data set Y. PLS is well described elsewhere and will not be described any further here [42, 43]. 

The modification of these natural polyhydroxylated compounds via acylation of the hydroxyl functions with aliphatic molecules not only increases their structural diversity, producing analogs that may be useful models for the study of structure-activity relationships, but also changes their physicochemical properties, increasing their solubility in lipophilic media. Moreover, the selective acylation of these natural compounds with various acyl donors could enhance their biological activities, such as their antioxidant and antimicrobial activity, as well as their pharmacological properties [5, 6]. 

Another extension is the descriptor selection procedure designed to enhance the stability and predict vity of the PLSR models. Its aim is to minimize the info-noise that can dilute and distort the true structure-activity relationship. The procedure involves two phases. The first phase consists of the elimination of the low-variable descriptors that have the same value for all but a few (2-3) compounds in the training set. Such descriptors cannot provide useful statistical information and instead simply help to fit these particular compounds into a model, thus decreasing its predictivity. This filtering is performed entirely in the X-space, without regard for the aetivity values. In the optional second phase, the descriptor... 

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