Spec2D:  A Structure Elucidation System Based on ¹H NMR and H−H COSY Spectra in Organic Chemistry

Abstract

A system for structure elucidation based on proton NMR spectra has been developed. The system, named Spec2D (system for spectra from 2D-NMR), incorporates 1H NMR and H-H correlation spectroscopy (COSY) spectral information obtained from 2D-NMR experiments. 2D-NMR is important for the structure elucidation because it provides information about the relationships among differently situated protons in the structures of unknown compounds. The system uses the concepts of molecular graphs. The improved representation of substructures as well as several novel algorithms for structure generation have been devised to solve the combinatorial problem and to reduce the processing time. Spec2D consists of a knowledge base, an analysis module, and a candidate structure generator module. Spec2D proposes candidate structures from only 1H NMR and H-H COSY spectral information of an unknown compound without any 13C NMR spectral or structural information, such as molecular formulas. Spec2D has the capability to propose the "new" structure of an unknown compound, if the corresponding substructures are included in the knowledge base.

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

“…Prediction confidence has been proposed as one of the metrics to measure performance of predictive models developed in the FDA’s endocrine disruptors knowledge based project6465666768697071 using variety of machine learning methods such as decision tree72, Decision Forest models737475767778 based on molecular descriptors79 that are calculated using the algorithm developed for the expert systems of structure elucidation808182838485, support vector machine8687 and principal component analysis based algorithm8889. The continuous value output from sNebula for prediction of binding between an HLA and a peptide is the measure of likelihood of the peptide is a binder or non-binder of the HLA and indicates the confidence for the prediction.…”

Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products

“…Mold2 is a free software package that calculates molecular descriptors based on two-dimensional structures of chemicals. Mold2 is utmost fast in computation because it utilizes the extremely fast ring structure recognition algorithm [11] and adopted the efficient system of representation of chemical structures [12,13] that were initially developed in a structure elucidation expert system based on infrared [14] and nuclear magnetic resonance (NMR) spectra [15,16,17]. Mold2 molecular descriptors have been used in the development of various successful QSAR models [18,19,20,21,22].…”

Homology Model and Ligand Binding Interactions of the Extracellular Domain of the Human <i>α</i>4<i>β</i>2 Nicotinic Acetylcholine Receptor

“…There are many structure-activity relationship techniques such as pharmacophore modeling [27]- [29], comparative molecular field analysis [30] [31], machine learning methods [32] such as classification tree model [33] and Decision Forest models [34]- [41] based on molecular descriptors [42] that are calculated using the algorithm developed for the expert systems of structure elucidation [43]- [49], and molecular docking [15] [50] [51]. Molecular docking was used in our previous studies of interactions between ligands and estrogen receptors [52] [53] for prediction of estrogenic activity [54] and endocrine disruptions [53] [55].…”