Pattern recognition with descriptors

Pattern recognition methods have been used for the description of air pollution in the industrialized region at the estuary of the river Rhine near Rotterdam. A selection of about eight chemical and physical-meteorological features offers a possibility for a description that accounts for out 70% of the information that is ccmprised in these features with two parameters only. Prediction of noxious air situations scmetimes succeeds for a period of at most four hours in advance. Seme-times, hewever, no prediction can be made. Investigations pertaining to the correlation between air conpo-sition and complaints on bad smell by inhabitants of the area show that, apart frem physical and chemical descriptors, other features are also involved that depend on human perception and bdiaviour. 

A prediction set of 19 compounds (see Table 2) was used to assess the predictive ability of the 15 molecular descriptors identified by the pattern recognition GA. We chose to map the 19 compounds directly onto the principal component plot defined by the 312 compounds and 15 descriptors. Figure 5 shows the prediction set samples projected onto the principal component map. Each projected compound lies in a region of the map with compounds that bare the same class label. Evidently, the pattern-recognition GA can identify molecular descriptors that are correlated to musk odor quality. 

Among the 153 compounds are included two pairs of diastereo-isomers. Because the descriptors cannot distinguish between pairs of diastereoisomers, these four compounds were excluded from further study. In a lengthy series of pattern recognition studies, five additional compounds were identified that had to be excluded from consideration. This leaves a data set of 143 N-nitroso compounds with 116 carcinogens and 28 noncarcinogens. 

It is obviously not possible to unravel the entire complexity of the physical, chemical and biological properties of even the simplest of molecules. However, focusing on the apparently pertinent descriptors for structures, one can, via pattern recognition, begin to equate toxicological response with structures ... 

Fluorescence spectra are collected under excitation conditions that are optimized to correlate the emission spectral features with parameters of interest. Principal components analysis (PCA) is further used to extract the desired spectral descriptors from the spectra. The PCA method is used to provide a pattern recognition model that correlates the features of fluorescence spectra with chemical properties, such as polymer molecular weight and the concentration of the formed branched side product, also known as Fries s product, that are in turn related to process conditions. The correlation of variation in these spectral descriptors with variation in the process conditions is obtained by analyzing the PCA scores. The scores are analyzed for their Euclidean distances between different process conditions as a function of catalyst concentration. Reaction variability is similarly assessed by analyzing the variability between groups of scores under identical process conditions. As a result the most appropriate process conditions are those that provide the largest differentiation between materials as a function of catalyst concentration and the smallest variability in materials between replicate polymerization reactions. 

The most difficult problem in pattern recognition applications to SAR-classifications is the formulation of meaningful descriptors that describe the molecular structure and are correlated with the classification problem. The widely used concept of a linear, binary classifier assumes a linear relationship between the structural properties (pattern components) x. and the biological activity. 

---