PubMed · 5286382
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R A Elton, J J Greenwood. 1970. Exploring apostatic selection.. https://doi.org/10.1038/hdy.1970.67
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At first sight, the QSAR issue might appear to be a mere pattern recognition problem; however, a purely "surface" approach to QSAR as a pattern recognition problem, not involving the profound plausibility of the solutions, has often been demonstrated to be devoid of scientific value and of predictive strength. The requirement for such a lateral validation should imply the recognition of the basic differences between the two terms of the QSAR issue: biology and chemistry. In particular, the difficulty to derive strong quantitative theories for the biological aspect of QSAR procedures should be taken into serious consideration. Within this conceptual framework, this paper examines the different families of mathematical models (classical regression, multivariate methods, neural networks) used in the QSAR research.
By considering an exciplex system consisting of collective molecules in interaction with both the 'pumping' fields and the biophoton fields, the two-level exciplex model and the three-level exciplex model are presented. They are useful for the investigation of the quasi-stationary behaviour of biophoton emission, and biophoton emission as a dynamic process in the presence of external perturbations. Our theoretical results predict a series of nonlinear effects, such as chaos, fractal behaviour, and non-equilibrium phase transition. These effects characterize the coherence nature of living systems. In our approaches, there are two important quantities f and x, which can be used to mark the working points of the two-level and three-level exciplex systems. All the influences of external perturbations on the exciplex systems, e.g. change of temperature, the addition of agents, exposure to light, etc., can be interpreted as shifts of the working points of the systems, leading to a diversity of nonlinear response of biophoton emission. In addition, the agreements of the theoretical results and the corresponding experimental observations on biophoton emission from biological systems in the presence of external perturbations are demonstrated.
The relationship between the fractions of protein secondary structural components as determined from X-ray crystallographic data by the procedures of Kabsch and Sander (KS) and of Levitt and Greer (LG) is analyzed by neural network analysis of these two tabulations of literature data. A linear relationship between the KS and LG reductions of X-ray data to secondary structure descriptors is demonstrated by a regression analysis of the relationships between these sets of structural parameters. Back-propagation neural network analysis was then used to derive equations for determination of the most probable fractions of beta-sheet, bend, turn, and "other" conformations given the fraction of alpha-helix in a globular protein. The deviation of the X-ray values for beta-sheet from that determined with these equations was shown to have a variance that exponentially decreased with increasing fraction of alpha-helix. A second neural network analysis showed that knowledge of both the alpha-helical and beta-sheet fractions in a protein significantly reduces the uncertainty in prediction of the other components of the secondary structure. These analyses provide insight into the nature of the data sets derived from crystal structures. Since these complications of crystal structure data are commonly used as reference information for quantitative evaluation of spectra (for example, FTIR, Raman, and electronic or vibrational circular dichroism) in terms of secondary structure, such internal correlations in the reference sets may have significant effects on the stability of spectroscopic analyses derived from them.