Structure generation by reduction: a new strategy for computer-assisted structure elucidation

Abstract

A problem common to computer programs for structure elucidation is the efficient and prospective use of the input information to constrain the structure generation process. The input may consist of potentially overlapping substructure requirements and alternative substructure interpretations of spectral data. Other useful information may be structural features that must not be present in the output structures. All of these may interact in a complex manner that is impossible to determine by use of a bond-by-bond structure assembly algorithm. A new method is described called structure reduction. In contrast to structure assembly, this method begins with a set of all bonds and removes inconsistent bonds as structure generation progresses. This results in a more efficient use of the input information and the ability to use potentially overlapping required substructures. Several examples illustrate the application of our computer program COCOA, which uses this method to solve real-world structure elucidation problems.

Algorithm for Exhaustive and Nonredundant Organic Stereoisomer Generation

“…Tree processing of this molecule allows for the recognition of two stereocenters: double bonds at atom pairs (2,3) and (4,5), respectively, and accordingly there will be four Z/E stereoisomers with binary configurations: [0,0]; [0,1]; [1,0]; and [1,1]. Note that stereocenter atom number 2 has the same configuration as stereocenter atom number 3; the same applies for atoms 4 and 5.…”

“…[15][16][17] For solving some stereoisomer generation problem we have used molecular graphs processing of increasing complexity, first for acyclic structures 44,45 and then for cyclic ones with isolated and spiro cycles. 46 The approach now is to solve stereoisomer generation for molecular structures containing cycles of whatever type (isolated, spiro, condensed, and nested).…”

“…P1:{(1,1);(2,8);(3,7);(4,6);(5,5);(9,9);(10,10)} G1:{1,5,7,3} 0 1 P2:{(1,1);(2,10);(3,5);(4,4);(6,9);(7,7);(8,8)} G2:{2,8,10,4,9,6} P3:{(1,1);(2,2);(3,3);(4,9);(5,7);(6,6);(8,10)8 have the same properties among them and different from atoms 2 and 5 that constitute another group. In…”

Rule-Based System To Derive Automatically Good-List and Bad-List Entries for Structure Generators from Spectra

“…Instead of directly giving the results in the output file, these rules operate on the three matrices shown in Figure 1. The hyperstructure connectivity matrix of the basic fragments, which resembles the bonding adjacency matrix (BAM) used by Christie and Munk in COCOA, 15 (for a related approach see ref 16 ), keeps track of all possible connections between the non-hydrogen atoms. A basic fragment consists of a non-hydrogen atom with its H atoms.…”

Atmospheric particulate matter characterization by Fourier Transform Infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks

“…For these applications, quantitative analysis has been conducted using various regression algorithms with standard gases or synthetic calibration spectra with absolute accuracies on 30 were reproduced, provided that relevant training samples were available in the library. Much of the work was motivated by pattern matching and classification of spectra for unknown samples (Robb and Munk, 1990;Novic and Zupan, 1995), and automated band assignment and identification of the underlying fragments typically performed by trained spectroscopists (Sasaki et al, 1968;Gribov and Elyashberg, 1970;Christie and Munk, 1988;Munk, 1998;Hemmer, 2007;Elyashberg et al, 2009). …”