Computer-encodable structure

Computer-Aided Property Estimation Computer-aided structure estimation requires the structure of the chemical compounds to be encoded in a computer-readable language. Computers most efficiently process linear strings of data, and hence linear notation systems were developed for chemical structure representation. Several such systems have been described in the literature. SMILES, the Simplified Molecular Input Line Entry System, by Weininger and collaborators [2-4], has found wide acceptance and is being used in the Toolkit. Here, only a brief summary of SMILES rules is given. A more detailed description, together with a tutorial and examples, is given in Appendix A. 

DNA and RNA each contain four monomers, called nucleotides, that differ in the structure of the bases bonded to the ribose units. Yet this deceptively simple structure encodes complex information just as the 0 and 1 bits used by a computer encode complex programs. First we consider the structure of individual nucleotides, then the bonding of these monomers into single-stranded nucleic acids, and finally the base pairing that binds two strands into the double helix of nuclear DNA. 

For many computer tasks and for the transfer of structiural information from one computer program to another, a linear representation of the chemical structure may be more suitable. " A popular linear representation is the SMILES notation. Part of its appeal is that for acyclic structures the SMILES is similar to the traditional linear diagram. For example, ethane is denoted by CC and ethylene C=C. Examples of additional SMILES are given in Figure 4. SMILES is the basis of a chemical information system, and this notation provides a convenient framework for more sophisticated computer coding of chemistry described below. For some internal computer functions, structures encoded in a linear notation may be converted to connection tables. 

Multivariate data analysis usually starts with generating a set of spectra and the corresponding chemical structures as a result of a spectrum similarity search in a spectrum database. The peak data are transformed into a set of spectral features and the chemical structures are encoded into molecular descriptors [80]. A spectral feature is a property that can be automatically computed from a mass spectrum. Typical spectral features are the peak intensity at a particular mass/charge value, or logarithmic intensity ratios. The goal of transformation of peak data into spectral features is to obtain descriptors of spectral properties that are more suitable than the original peak list data. 

Of course, with so many different final products mixed together, the problem is to identify them. What structure is linked to what bead Several approaches to this problem have been developed, all of which involve the attachment of encoding labels to each polymer bead to keep track of the chemistry each has undergone. Encoding labels used thus far have included proteins, nucleic acids, halogenated aromatic compounds, and even computer chips. 

Invent computer methods to predict the three-dimensional folded structure of a protein—and the pathway by which folding occurs—from its amino acid sequence, so information from the human genome can be translated into the encoded protein structures. 

Because the chemical structure of a molecule encodes its biological properties, structure has long served as the primary variable and determinant for the discovery of new drugs by medicinal chemists. For this reason, systematic structural modification has been the primary tool of choice to isolate and enhance a desired biologic activity. Moreover, with the relatively recent development of in vitro receptor-binding assays, combinatorial methods of chemical synthesis, and computer graphics, the overall approach to structural modification has become increasingly sophisticated. 

Infrared spectroscopical data encode a lot of structural information and can be analyzed with the help of computational methods (vide supra) aiding in the identification of the observed species. Sometimes, two different electronic states may lie very close in energy and have similar geometries. In such cases (e.g., the quinonoid radicals to be described in Section II.B.), the predicted differences in the IR spectra are too small to allow an unambiguous assignment of the ground-state multiplicity. In this respect, ESR spectroscopy provides valuable comple-... 

Computational models relating molecular structure and/or properties to biological activity are required for the design of both target-focused and target class combinatorial libraries based on known active ligands. These models are developed from descriptors, which encode information about molecular properties... 

The method Meylan et al. (1992) described also has been encoded in a computer program, PCKOCWIN. After the user enters the structure of the chemical of interest represented in SMILES notation (Anderson et al., 1987 Weininger, 1988), the program automatically calculates y and determines the appropriate fragments and correction factors. 

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