Chemical structure encoding

One of the most widely used chemical structure-encoding schemas in the pharmaceutical industry is the MDL Connection Table (CT) File Format. Both Molfile and SD File are based on MDL CT File Format to represent chemical structures. A Molfile represents a single chemical structure. An SD File contains one to many records, each of which has a chemical structure and other data that are associated with the structure. MDL Connection Table File Format also supports RG File to describe a single Rgroup query, rxnfile, which contains structural information of a single reaction, RD File, which has one to many records, each of which has a reaction and data associated with the reaction, and lastly, MDL s newly developed XML representation of the above—XD File. The CT File Format definition can be downloaded from the MDL website http //www.mdl.com/downloads/public/ctfile/ctfile.jsp. 

But progress continues to be made, and more and more biologically active compounds continue to come out of DOS libraries. Another approach to the generation of synthetic compounds more closely resembling natural products, evolutionarily selected chemical structures encoding the properties required for binding to proteins, called Biology-Oriented Synthesis BIOS) was developed by Dr Herbert Waldmann at the Max Planck Institute. 

Nowadays, in science, there is a basic assumpion that molecular properties and structural characteristics are closely connected to biological functions of the compounds. It is often assumed that compounds with similar properties and structures also display similar biological responses. Chemical structure encodes a large amount of information explaining why a certain molecule is active, toxic or insoluble (Rajarshi, 2008). Thus to understand the mechanism of action of a drug it is necessary to interpret the role played by its molecular and structural properties. 

A year later, a novel method of encoding chemical structures via typewriter input (punched paper tape) was described by Feldmann [42]. The constructed typewriter had a special character set and recorded on the paper tape the character struck and the position (coordinates) of the character on the page. These input data made it possible to produce tabular representations of the structure. 

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. 

RNA RNA (ribonucleic acid) is an information encoded strand of nucleotides, similar to DNA, but with a slightly different chemical structure. In RNA, the letter U (uracil) is substituted for T in the genetic code. RNA delivers DNA s genetic message to the cytoplasm of a cell where proteins are made. 

The year 2000 marked the completion of the Human Genome Project s primary goal. Through intensive efforts of both private and public agencies, the sequence for the three billion base pairs that encodes the instructions for being human has now been determined.5 As a result of the Human Genome Project, we have determined the complete chemical structure, nucleotide by nucleotide, of the DNA within each of these chromosomes, the chemical structures that encode our lives. It is an extraordinary accomplishment in chemistry. 

Fig. 26 (a) The chemical structure of the molecular half-adder. The conformation of each N02 group encodes the logic input while the output status is encoded in the resistance between the drive and the output nano-electrodes. The complete truth table for the XOR and the AND outputs. Note the difference in magnitude between the XOR 1 and the AND 1 . (b) The T(E) spectra of the junction represented in Fig. 26 for all the logic inputs (solid line). Each inset emphasizes the modification of the conductance near the Fermi energy of the molecule. Each T(E) spectrum had been fitted in the active area to determine the minimum number of quantum levels required to reproduce it (dashed line)... 

Fig. 2.2 A. Chemical structure of the three types of lignin monomer units H, p-hydroxyphenyl- G, guaiacyl and S, syringyl. Note the methyl groups (boxed) in the methoxy moieties of the G and S monomers. B. Pie chart showing the proportions of pine xylem ESTs that putatively encode enzymes of Ci metabolism, glycolysis, and the TCA cycle.

At the low end of the hierarchy are the TS descriptors. This is the simplest of the four classes molecular structure is viewed only in terms of atom connectivity, not as a chemical entity, and thus no chemical information is encoded. Examples include path length descriptors [13], path or cluster connectivity indices [13,14], and number of circuits. The TC descriptors are more complex in that they encode chemical information, such as atom and bond type, in addition to encoding information about how the atoms are connected within the molecule. Examples of TC descriptors include neighborhood complexity indices [23], valence path connectivity indices [13], and electrotopological state indices [17]. The TS and TC are two-dimensional descriptors which are collectively referred to as TIs (Section 31.2.1). They are straightforward in their derivation, uncomplicated by conformational assumptions, and can be calculated very quickly and inexpensively. The 3-D descriptors encode 3-D aspects of molecular structure. At the upper end of the hierarchy are the QC descriptors, which encode electronic aspects of chemical structure. As was mentioned previously, QC descriptors may be obtained using either semiempirical or ab initio calculation methods. The latter can be prohibitive in terms of the time required for calculation, especially for large molecules. 

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. 

Another retrospective analysis of already known H DAC inhibitors was carried out by You et al. [68]. They generated a 3D chemical-feature-based pharmacophore model and compared the ligand-based model with the structural-functional requirements for the binding of the HDAC inhibitors. Using this model, the interactions between the benzamide MS-275 and HDAC were explored. The result showed that the type and spatial location of chemical features encoded in the pharmacophore are in full agreement with the enzyme-inhibitor interaction pattern identified from molecular docking. However, also in this study no experimental validation of the modeling results was provided. 

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. 

Other structure-encoding schemas are developed by software vendors and academia such as Daylight Smiles, CambridgeSoft ChemDraw Exchange (CDX), and Chemical Markup Language (CML), and they all have advantages and disadvantages. The MDL CT File Format is the only one that is supported by almost all chemical informatics software vendors. 

Closely related to analytical interpretations of QSAR models is the ability to visualize the SAR trends encoded in a model. The glowing molecule representation developed by Segall et al. (14) is an example of direct visualization of a predictive model in terms of the actual chemical structure. Figure 1 shows such a representation, where the shading corresponds to the influence of that sub-structural feature on the predicted property. This type of visualization allows the user to directly understand how structural modifications at specific points will affect the property or activity being optimized. 

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