Computer-aided structure elucidation of neolignans.

This paper describes a new module of the expert system SISTEMAT used for the prediction of the skeletons of neolignans by 13C NMR, 1H NMR and botanical data obtained from the literature. SISTEMAT is composed of MACRONO, SISCONST, C13MACH, H1MACH and SISOCBOT programs, each analyzing data of the neolignan in question to predict the carbon skeleton of the compound. From these results, the global probability is computed and the most probable skeleton predicted. SISTEMAT predicted the skeletons of 75% of the 20 neolignans tested, in a rapid and simple procedure demonstrating its advantage for the structural elucidation of new compounds.

New improvements in automatic structure elucidation using the LSD (Logic for Structure Determination) and the SISTEMAT expert systems.

This article describes the integration of the LSD (Logic for Structure Determination) and SISTEMAT expert systems that were both designed for the computer-assisted structure elucidation of small organic molecules. A first step has been achieved towards the linking of the SISTEMAT database with the LSD structure generator. The skeletal descriptions found by the SISTEMAT programs are now easily transferred to LSD as substructural constraints. Examples of the synergy between these expert systems are given for recently reported natural products.

C-glycosyl flavones from Peperomia blanda.

The methanol extract from aerial parts of the Peperomia blanda (Piperaceae) yielded two C-glycosyl-flavones. Their structures were elucidated on the basis of extensive spectroscopic analysis, including 1D and 2D NMR, chemical transformation and comparison with the related known compounds. The structure of the new flavonoids were established as 4'-methoxy-vitexin 7-O-beta-D-xylopyranoside (1) (7-O-beta-D-xylopyranosyl-8-C-beta-D-glucopyranosyl-4'-methoxy-apigenin) and vicenin-2 (2). The antioxidant activity of both compounds was investigated using the DPPH assay. Both compounds showed only modest activity, with IC50 values of 357.2 microM for 1, and 90.5 microM for 2.

Use of self-organizing maps and molecular descriptors to predict the cytotoxic activity of sesquiterpene lactones.

Some sesquiterpene lactones (SLs) are the active compounds of a great number of traditionally medicinal plants from the Asteraceae family and possess considerable cytotoxic activity. Several studies in vitro have shown the inhibitory activity against cells derived from human carcinoma of the nasopharynx (KB). Chemical studies showed that the cytotoxic activity is due to the reaction of alpha,beta-unsaturated carbonyl structures of the SLs with thiols, such as cysteine. These studies support the view that SLs inhibit tumour growth by selective alkylation of growth-regulatory biological macromolecules, such as key enzymes, which control cell division, thereby inhibiting a variety of cellular functions, which directs the cells into apoptosis. In this study we investigated a set of 55 different sesquiterpene lactones, represented by 5 skeletons (22 germacranolides, 6 elemanolides, 2 eudesmanolides, 16 guaianolides and nor-derivatives and 9 pseudoguaianolides), in respect to their cytotoxic properties. The experimental results and 3D molecular descriptors were submitted to Kohonen self-organizing map (SOM) to classify (training set) and predict (test set) the cytotoxic activity. From the obtained results, it was concluded that only the geometrical descriptors showed satisfactory values. The Kohonen map obtained after training set using 25 geometrical descriptors shows a very significant match, mainly among the inactive compounds (approximately 84%). Analyzing both groups, the percentage seen is high (83%). The test set shows the highest match, where 89% of the substances had their cytotoxic activity correctly predicted. From these results, important properties for the inhibition potency are discussed for the whole dataset and for subsets of the different structural skeletons.

Quantitative structure-activity relationship of sesquiterpene lactones with cytotoxic activity.

Some sesquiterpene lactones (SLs) are the active compounds of a great number of traditionally medicinal plants from the Asteraceae family and possess considerable cytotoxic activity. Several studies in vitro have shown the inhibitory activity against cells derived from human carcinoma of the nasopharynx (KB). In this study, we investigated a set of 37 different sesquiterpene lactones, represented by 4 skeletons (14 germacranolides, 6 elemanolides, 9 guaianolides and nor-derivatives, and 8 pseudoguaianolides), in what it says respect of their cytotoxic properties. The experimental results were submitted to a QSAR study. A single model for the entire data set was described using 3D molecular descriptors and genetic algorithms establishing structure-activity relationships among the compounds. Important properties for the inhibition potency are discussed for the whole data set and for subsets of the different structural skeletons.

Unusual chromenes from Peperomia blanda.

From the methanol extract of the aerial parts of Peperomia blanda (Piperaceae), two chromenes were isolated and characterized mainly through application of 2D-NMR spectroscopy. The structures were 2S-(4-methyl-3-pentenyl)-6-formyl-8-hydroxy-2,7-dimethyl-2H-chromene and 2S-(4-methyl-3-pentenyl)-5-hydroxy-6-formyl-2,7-dimethyl-2H-chromene named as blandachromenes I and II, respectively.

Automatic identification of terpenoid skeletons by feed-forward neural networks.

Feed-forward neural networks (FFNNs) were used to predict the skeletal type of molecules belonging to six classes of terpenoids. A database that contains the 13C NMR spectra of about 5000 compounds was used to train the FFNNs. An efficient representation of the spectra was designed and the constitution of the best FFNN input vector format resorted from an heuristic approach. The latter was derived from general considerations on terpenoid structures.

Simple method for identification of skeletons of aporphine alkaloids from 13C NMR data using artificial neural networks.

This paper describes the use of artificial neural networks as a theoretical tool in the structural determination of alkaloids from (13)C NMR chemical shift data, aiming to identify skeletal types of those compounds. For that, 162 aporphine alkaloids belonging to 12 different skeletons were codified with their respective (13)C NMR chemical shifts. Each skeleton pertaining to aporphine alkaloid type was used as output, and the (13)C NMR chemical shifts were used as input data of the net. Analyzing the obtained results, one can then affirm the skeleton to which each one of these compounds belongs with high degree of confidence (over 97%). The relation between the correlation coefficient and the number of epochs and the architecture of net (3-layer MLP or 4-layer MLP) were analyzed, too. The analysis showed that the results predicted by the 3-layer MLP networks trained with a number of the epochs higher than 900 epochs are the best ones. The artificial neural nets were shown to be a simple and efficient tool to solve structural elucidation problems making use of (13)C NMR chemical shift data, even when a similarity between the searched skeletons occurs, offering fast and accurate results to identification of skeletons of organic compounds.

Diterpenoids from the aerial parts of Plectranthus ornatus.

Phytochemical investigation of a hexane extract of the aerial parts of Plectranthus ornatus yielded three new neoclerodane diterpenoids (1-3), two labdane diterpenes (4 and 5) obtained for the first time as natural products, and several previously known substances. The structures and relative stereochemistry of 1-5 were established mainly on the basis of NMR spectroscopic studies and by comparison with related compounds.

Neural networks in chemosystematic studies of Asteraceae: a classification based on a dichotomic approach.

This paper describes the application of artificial neural nets as an alternative and efficient method for the classification of botanical taxa based on chemical data (chemosystematics). A total of 28,000 botanical occurrences of chemical compounds isolated from the Asteraceae family were chosen from the literature, and grouped by chemical class for each species. Four tests were carried out to differentiate and classify different botanical taxa. The qualifying capacity of the artificial neural nets was dichotomically tested at different hierarchical levels of the family, such as subfamilies and groups of Heliantheae subtribes. Furthermore, two specific subtribes of the Heliantheae and two genera of one of these subtribes were also tested. In general, the artificial neural net gave rise to good results, with multiple-correlation values R>0.90. Hence, it was possible to differentiate the dichotomic character of the botanical taxa studied.

Prediction of occurrences of diverse chemical classes in the Asteraceae through artificial neural networks.

The training and the application of a neural network system for the prediction of occurrences of secondary metabolites belonging to diverse chemical classes in the Asteraceae is described. From a database containing about 604 genera and 28,000 occurrences of secondary metabolites in the plant family, information was collected encompassing nine chemical classes and their respective occurrences for training of a multi-layer net using the back-propagation algorithm. The net supplied as output the presence or absence of the chemical classes as well as the number of compounds isolated from each taxon. The results provided by the net from the presence or absence of a chemical class showed a 89% hit rate; by excluding triterpenes from the analysis, only 5% of the genera studied exhibited errors greater than 10%.

Automatic identification by 13C NMR of substituent groups bonded in natural product skeletons.

The aim of this paper is to present a procedure that utilizes 13C NMR for identification of substituent groups which are bonded to carbon skeletons of natural products. For so much was developed a new version of the program MACRONO, that presents a database with 161 substituent types found in the most varied terpenoids. This new version was widely tested in the identification of the substituents of 60 compounds that, after removal of the signals that did not belong to the carbon skeleton, served to test the prediction of skeletons by using other programs of the expert system SISTEMAT.