Structures Systems Notation-Based

Access to all this information is provided by the Index Chemicus Registry System (ICRS), which contains records of new compounds and associated data on magnetic tape (6). Structures are described by the Wiswesser Line Notation (7), a system for describing chemical formulas in terms of a linear groups of symbols. A recent development is the ICRS Substructure Index (G5), which enables manual searches to be made for new chemical information, and is based on the occurrence of fragments of structures, the most common of which are illustrated as conventional structural diagrams. 

SMILES (Simplified Molecular Input Line Entry Systems) is a line notation system based on principles of molecular graph theory for entering and representing molecules and reactions in computer (10-13). It uses a set of simple specification rules to derive a SMILES string for a given molecular structure (or more precisely, a molecular graph). A simplified set of rules is as follows ... 

Eakin [13] describes the chemical structure information system at Imperial Chemical Industries Ltd., where registration is based on Wiswesser Line Notation. For connection tables, the unique, unambiguous representation is derived automatically, i.e., a single, invariant numbering of the connection table is algorithmically derived. 

Drefahl and Reinhard [68] have designed an unambiguous notation system aimed to a compact, unique description of A (see also Section 1.7). Based on these notations for A, automatic estimation of Kow is possible for compounds for which structurally similar compounds with known Kov/ are available in database. This approach has been implemented in the Toolkit using the database of 600 compounds from the compilation of Sangster [1] as candidates. The GIM approach is demonstrated for 1-bromooctane in Figure 13.5.3. The recommended log Kov/ value is 4.89 0.35 [1]. 

There are two competing and equivalent nomenclature systems encountered in the chemical literature. The description of data in terms of ways is derived from the statistical literature. Here a way is constituted by each independent, nontrivial factor that is manipulated with the data collection system. To continue with the example of excitation-emission matrix fluorescence spectra, the three-way data is constructed by manipulating the excitation-way, emission-way, and the sample-way for multiple samples. Implicit in this definition is a fully blocked experimental design where the collected data forms a cube with no missing values. Equivalently, hyphenated data is often referred to in terms of orders as derived from the mathematical literature. In tensor notation, a scalar is a zeroth-order tensor, a vector is first order, a matrix is second order, a cube is third order, etc. Hence, the collection of excitation-emission data discussed previously would form a third-order tensor. However, it should be mentioned that the way-based and order-based nomenclature are not directly interchangeable. By convention, order notation is based on the structure of the data collected from each sample. Analysis of collected excitation-emission fluorescence, forming a second-order tensor of data per sample, is referred to as second-order analysis, as compared with the three-way analysis just described. In this chapter, the way-based notation will be arbitrarily adopted to be consistent with previous work. 

The software now uses structurally intrinsic parameters for only one QSAR model (LSER) and the results are used to predict one property (acute toxicity) to four aquatic species by one mechanism (nonreactive, non-polar narcosis) however, we intend to continue to refine our equations as databases grow, incorporate other models, predict other properties, and include other organisms. We will attempt to differentiate between modes of toxic action and improve our estimates accordingly. For the widely divergent classes of chemicals and types of environmental behavior, no one model will best describe every situation and no one species is the optimal organism to monitor. As the software evolves, the expert system should choose the best model based on the contaminant, the species, and the property to be predicted (e.g., toxicity or bioaccumulation). In addition, we envision an interactive screen system for data entry that will bypass the SMILES notation and allow the user to describe the molecule by posing a series of questions about the compound s backbone and functional groups. The responses will translate directly into values of LSER variables. 

---