Structure-driven approaches

Structure-driven approaches can be aimed toward structures with known, interesting properties or toward novel structural features, with unknown properties. 

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A recent report presented Continuous Composition Spread (CCS) approaches (37) to identify thin film dielectrics with varying composition among the system Zr-Sn-Ti-O (78). A structural-driven approach was applied to the discovery of a high dielectric perovskite polymorphic material (79). 

We have used a highly structure-driven approach composed of fragment-based NMR screening, X-ray crystallography, and structure-assisted chemistry to develop a first-in-class clinical candidate as a potential proof-of-concept for the inhibition of BACE-1 in AD. Crucial to this achievement was the initial identification of a ligand-efficient isothiourea fragment and its X-ray crystal structure, which revealed an extensive H-bond network with the two active site aspartates. This interaction was unprecedented in the aspartic acid protease field when we discovered it several... 

Structure-driven Approaches Toward Unchartered Territory... 

The synthesis-driven approach towards material science can be applied to create oligomers and polymers with optimized properties, e.g. maximized carrier mobilities and electrical conductivities or high photo- and electroluminescence quantum yields. It becomes obvious, however, that the ability to synthesize structurally defined -architectures is the key to these high performance materials. 

A consequence of looking for novel secondary metabolites with bioassay driven approaches is that novel natural compounds are generally structurally complex and are often bound only in very small amounts. The first three groups of compounds that we identified were extremely potent at preventing barnacle settlement. However, they occurred in mg/kilogram dry weight of starting material amounts, and each had approximately seven chiral centers.5194 Compounds with this abundance and complexity have little practical commercial potential. 

Whereas hard filters can be considered to be knowledge-driven, soft filters are the result of a data-driven approach. A quantitative structure-activity or structure-property relationship (QSAR/QSPR) is established to predict a property from a set of molecular descriptors. Examples are the above-mentioned in-silico prediction tools for frequent hitters [27] and drug-likeness [41,42] additional models for ADM E properties are described below. 

The two examples above show that the structure of a problem determines what type of search to use. In the first example, there is only a limited number of components which could be broken, and a large number of possible symptoms. A data-driven approach to this problem would be to ask the scientist for all the symptoms, some of which may require disassembling the HPLC to determine. The problem, therefore, demands a goal-directed approach. 

The effectiveness of his efforts continues to be based on a foundation of multifaceted discoveries. The Crews group emphasizes a field-driven approach to explore and discover inspirational chemical structures often accompanied by significant biological activity derived from marine sponges. Several years ago the lab expanded investigations to encompass the study of marine-derived fungi. 

The availability of the three-dimensional structure of the protein complex allows structure-driven drug discovery approaches. In this case, a pharmacophore model is first established. This corresponds to identifying the interactions that take place at the interface and which contribute most to AG. The importance of these interactions can be validated by site-directed mutagenesis or, when possible, by the use of peptides. Once these interactions are validated, molecules containing chemical groups mimicking these key interactions are selected from compound libraries and tested. Very often these initial molecules are not optimal (e.g., they do not make all the key contacts) and they must be modified to enhance their potency. This is done, for example. 

Rohrschneider s approach is able to predict retention index values for solute s with known solute constants (a, through e) [283,288]. These are determined from AI values for the solute on at least five phases of known phase constants and solving the series of linear equations. The retention index of the solute on any phase of known phase constants (X through S ) can then be calculated from Eq. (2.8). The theoretical defects of the method for assigning intermolecular interactions do not apply to the prediction of retention index values. A mean error of about 6 index units was indicated in some calculations. The retention or retention index values for thousands of compounds can be calculated from literature compilations of solute descriptors and the system constants summarized in Tables 2.6 and 2.8 using the solvation parameter model [103]. The field of structure-driven prediction of retention in gas chromatography is not well developed at present and new approaches will likely emerge in the future. 

One of the most challenging membrane protein structures determined to a high resolution via an NOE-driven approach is the pSRII GPCR (Fig. 6d), which required additional sensitivity-enhancing approaches to be employed during data... 

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