Molecular modeling in the discovery of drug leads

Prediction of aqueous solubility for a diverse set of organic compounds based on atom-type electrotopological state indices

We describe robust methods for estimating the aqueous solubility of a set of 734 organic compounds from different structural classes based on multiple linear regression (MLR) and artificial neural networks (ANN) model. The structures were represented by atom-type electrotopological state (E-state) indices. The squared correlation coefficient and standard deviation for the MLR with 34 structural parameters were r(2) = 0.94 and s = 0.58 for the training set of 675 compounds. For the test set of 21 compounds, the equivalent statistics were r(2)(pred) = 0.80 and s = 0.87, respectively. Neural networks gave a significant improvement using the same set of parameters, and the standard deviations were s = 0.52 for the training set and s = 0.75 for the test set when an artificial neural network with five neurons in the hidden layer was used. The results clearly show that accurate models can be rapidly calculated for the estimation of aqueous solubility for a large and diverse set of organic compounds using easily calculated structural parameters.

Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology

An accurate and generally applicable method for estimating aqueous solubilities for a diverse set of 1297 organic compounds based on multilinear regression and artificial neural network modeling was developed. Molecular connectivity, shape, and atom-type electrotopological state (E-state) indices were used as structural parameters. The data set was divided into a training set of 884 compounds and a randomly chosen test set of 413 compounds. The structural parameters in a 30-12-1 artificial neural network included 24 atom-type E-state indices and six other topological indices, and for the test set, a predictive r2 = 0.92 and s = 0.60 were achieved. With the same parameters the statistics in the multilinear regression were r2 = 0.88 and s = 0.71, respectively.

Drug discovery: past, present and future

New drug discovery from early on involved a trial-and-error approach on naturally derived materials and substances until the end of the nineteenth century. The first half of the twentieth century witnessed systematic pharmacological evaluations of both natural and synthetic compounds. However, most new drugs until the 1970s were discovered by serendipity. With the exponential development of molecular biology on one hand and computer technology on the other, it became possible from 1980 onwards to place drug discovery on a rational basis. Cloning of genes has led to the development of methodologies for specific receptor-directed and enzyme-directed drug discoveries. Advances in recombinant DNA and transgenic technologies have enabled the production of human hormonal and other endogenous biomolecules as new drugs. As we understand more about the co-ordinating and regulating powers of the cerebral cortex during the next century, especially of the frontal lobe, man may be able to use bio-feedback training to voluntarily regulate the release of neurotransmitters, hormones, and other molecules involved in the regulation of various physiological processes in health as well as in disease.

Pharmacophores in drug design and discovery

Compounds containing a specific pharmacophore--the minimum structural features necessary for enzyme binding--can be retrieved from a database such as the National Cancer Institute repository by means of three-dimensional (3D) searching, which allows the retrieval of all compounds possessing a specified set of atoms with a given 3D geometry. The ways in which pharmacophores can be found and characterized and the details of the 3D searching methods are described. Elaboration of compounds found in such searches and their subsequent development as lead drugs is also discussed.

Potent inhibitors of human immunodeficiency virus type 1 integrase: identification of a novel four-point pharmacophore and tetracyclines as novel inhibitors

A four-point pharmacophore was constructed from energy-minimized structures of chicoric acid and dicaffeoylquinic acid. The search of 206,876 structures in the National Cancer Institute 3D database yielded 179 compounds that contain this pharmacophore. Thirty-nine of these compounds were tested in an in vitro assay specific for human immunodeficiency virus type 1 integrase (IN). Each retrieved structure was fit to the pharmacophore, and the conformation that afforded the best fit was identified. Twenty of the 39 compounds tested exhibited IC50 values of < 20 microM. Among the most potent inhibitors, tetracyclines emerged as a new class of inhibitors. Although the parent tetracycline exhibited marginal potency against purified IN, all substituted tetracyclines tested showed 5-100-fold increased potency. Disintegration assays with truncated IN mutants indicated that tetracyclines inhibit the IN catalytic core domain. To investigate whether chelation of divalent metals is implicated in differential potency of tetracyclines, enzyme assays were performed in the presence of both Mn2+ or Mg2+; no significance difference in potency was observed. Rolitetracycline inhibited IN/DNA complex formation in the presence of EDTA, which suggests that inhibition was metal independent. Rolitetracycline reversed DNA binding of IN after the complex was allowed to form before the addition of drug. Selectivity of tetracyclines was also examined in an assay specific for topoisomerase I, and none of the tetracyclines tested induced topoisomerase I-mediated cleavable complex or inhibited camptothecin-induced cleavable complex. Remarkable potency against the IN in the absence of divalent metals and the core enzyme coupled with water solubility makes tetracyclines potential candidates for X-ray crystal structure determination with IN.