Drug-like annotation and duplicate analysis of a 23-supplier chemical database totalling 2.7 million compounds.

We have implemented five drug-like filters, based on 1D and 2D molecular descriptors, and applied them to characterize the drug-like properties of commercially available chemical compounds. In addition to previously published filters (Lipinski and Veber), we implemented a filter for medicinal chemistry tractability based on lists of chemical features drawn up by a panel of medicinal chemists. A filter based on the modeling of aqueous solubility (>1 microM) was derived in-house, as well as another based on the modeling of Caco-2 passive membrane permeability (>10 nm/s). A library of 2.7 million compounds was collated from the 23 compound suppliers and analyzed with these filters, highlighting a tendency toward highly lipophilic compounds. The library contains 1.6 M unique structures, of which 37% (607,223) passed all five drug-like filters. None of the 23 suppliers provides all the members of the drug-like subset, emphasizing the benefit of considering compounds from various compound suppliers as a source of diversity for drug discovery.

Preliminary screening of some tropical plants for anti-tyrosinase activity.

In our efforts to find new active tyrosinase inhibitors for skin-whitening or antibrowning preparations, we investigated 67 tropical plants belonging to 38 families, which were evaluated for their anti-tyrosinase activity. The results of our investigation show that extracts of 5 plants, Stryphnodendron barbatimao, Portulaca pilosa, Cariniana brasiliensis, Entada africana and Prosopis africana present interesting in vitro mushroom tyrosinase inhibition (over 90%), similar to a positive control: Morus alba. These 5 plants will be studied in order to isolate and identify phytochemical compounds, involved in this biological activity.

Three-dimensional quantitative structure-activity relationships of cyclo-oxygenase-2 (COX-2) inhibitors: a comparative molecular field analysis.

The three-dimensional quantitative structure-activity relationship (3D-QSAR) approach using comparative molecular field analysis (CoMFA) was applied to an extensive series of 305 varied diarylheterocyclic derivatives known as COX-2 selective inhibitors. X-ray crystal structure of COX-2 bound with SC-558, a selective COX-2 inhibitor, was used to derive the putative bioactive conformation of these inhibitors. Five statistically significant models were obtained from the randomly constituted training sets (229 compounds) and subsequently validated with the corresponding test sets (76 compounds). The best predictive model (n = 229, q(2) = 0.714, N = 8, r(2) = 0.905, s = 0.291, F = 261.545) was selected for further comparison of the CoMFA contour maps obtained for steric, electrostatic, and lipophilic fields with the enzyme structure. The high level of compatibility with the COX-2 enzyme topology shows the great accuracy of this model that can predict inhibitory activities for a wide range of compounds and offers important structural insight into designing novel antiinflammatory drugs prior to their synthesis.

3D-QSAR CoMFA study on imidazolinergic I(2) ligands: a significant model through a combined exploration of structural diversity and methodology.

Displaying an unprecedented structural diversity, 119 I(2) ligands, and their pK(i) values, were collected and submitted to a comparative molecular fields analysis (CoMFA) study. They were discerned into three structural subsets (A, B, C), to explore the I(2) 3D-QSARs from finite structural systems (A, B, C) to more complex ones (AB, AC, BC, ABC). In addition, various key steps of the CoMFA methology were explored. The applied method used two pharmacophore templates and seven molecular field combinations (electrostatic, lipophilic, steric), as well as eight alignment methods (two point-by-point and six similarity-based variations). That way, 644 CoMFA models were obtained and further selected according to their predictive ability through two filters. The first filter was mainly based on the q(2), which internally evaluates the predictive ability from the training set. For the second filter, the predictive ability was externally evaluated through the prediction of test sets. Finally, one model was extracted from the whole data as the best. Indeed, it combines three features of upmost importance for the further design of ligands endowed with high I(2) affinity: structural diversity (n = 73), robustness (N = 9, r(2) = 0.96, s = 0. 28, F = 148), and a great fully assessed predictive ability (q(2) = 0.50, r(2)(test set) = 0.81, n(test set) = 46). On the basis of structural data and CoMFA isocontours, some elements of the I(2) tridimensional pharmacophore are also suggested.