To contribute to the development of products to control Meloidogyne exigua, the bacteria Bacillus cereus and B. subtilis were cultivated in liquid medium to produce metabolites active against this plant-parasitic nematode. Fractionation of the crude dichloromethane extracts obtained from the cultures afforded uracil, 9H-purine and dihydrouracil. All compounds were active against M. exigua, the latter being the most efficient. This substance presented a LC 50 of 204 µg/mL against the nematode, while a LC 50 of 260 µg/mL was observed for the commercial nematicide carbofuran. A search for protein-ligand complexes in which the ligands were structurally similar to dihydrouracil resulted in the selection of phosphoribosyltransferases, the sequences of which were used in an in silico search in the genome of M. incognita for a similar sequence of amino acids. The resulting sequence was modelled and dihydrouracil and 9H-purine were inserted in the active site of this putative phosphoribosyltransferase resulting in protein-ligand complexes that underwent molecular dynamics simulations. Calculation of the binding free-energies of these complexes revealed that the dissociation constant of dihydrouracil and 9H-purine to this protein is around 8.3 x 10 -7 and 1.6 x 10 -6 M, respectively. Consequently, these substances and the putative phosphoribosyltransferase are promising for the development of new products to control M. exigua.

Leaf-cutting ants are considered the main agricultural and forest pest in countries such as Brazil, as they attack plants at any stage of their development, cutting their leaves, flowers, buds, and branches, which are then transported to the interior of their underground nest [9]. A colony of Atta laevigata (F. Smith) (Hymenoptera: Formicidae) can cut approximately 5 kg

A series of pyridylthiazole derivatives developed by Lawrence et al. as Rho-associated protein kinase inhibitors were subjected to four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis. The models were generated applying genetic algorithm (GA) optimization combined with partial least squares (PLS) regression. The best model presented validation values ofr2=0.773,qCV2=0.672,rpred2=0.503,Δrm2=0.197,rm test2⁡⁡=0.520,rY-rand2=0.19, andRp2=0.590. Furthermore, analyzing the descriptors it was possible to propose new compounds that predicted higher inhibitory concentration values than the most active compound of the series.

In this study, quantitative structure-activity relationship studies which make use of molecular dynamics trajectories were performed on a set of 54 glucokinase protein activators. The conformations obtained by molecular dynamics simulation were superimposed according to the twelve alignments tested in a virtual three-dimensional box comprised of 2 Å cells. The models were generated by the technique that combines genetic algorithms and partial least squares. The best alignment models generated with a determination coefficient (r(2)) between 0.674 and 0.743 and cross-validation (q(2)) between 0.509 and 0.610, indicating good predictive capacity. The 4D-QSAR models developed in this study suggest novel molecular regions to be explored in the search for better glucokinase activators.

Despite recent advances in Computer Aided Drug Discovery and High Throughput Screening, the attrition rates of drug candidates continue to be high, underscoring the inherent complexity of the drug discovery paradigm. Indeed, a compromise between several objectives is often required to obtain successful clinical drugs. The present manuscript details a multi-objective workflow that integrates the 4D-QSAR and molecular docking methods in the simultaneous modeling of the Rho Kinase inhibitory activity and acute toxicity of Benzamide derivatives. To this end, the pIC /pLD ratio is considered as the response variable, permitting the concurrent modeling of both properties and representing a shift from classical step-by-step evaluations. The 4D-QSAR strategy is used to generate the Grid Cell Occupancy Descriptors (GCODs), and Stochastic Gradient Boosting (SGB) and Partial Least Squares (PLS) methods as the model fitting techniques. While the statistical parameters for the PLS model do not meet established criteria for acceptability, the SGB model yields satisfactory performance, with correlation coefficients r =0.95 and r pred=0.65 for the training and test set, respectively. Posteriorly, the structural interpretation of the most relevant GCODs according to the SGB model is performed, allowing for the proposal of 139 novel benzamide derivatives, which are then screened using the same model. Of these 9 compounds were predicted to possess pIC /pLD ratio values higher than those for the employed dataset. Finally, in order to corroborate the results obtained with the SGB model, a docking simulation was formed to evaluate the binding affinity of the proposed molecules to the ROCK2 active site and 3 chemical structures (i. e. p6, p14 and p131) showed higher binding affinity than the most active compound in the training set, while the rest generally demonstrated comparable behavior. It may therefore be concluded that the consensus models that intertwine the 4D-QSAR and molecular docking methods contribute to more reliable virtual screening and compound optimization experiments. Additionally, the use of multi-objective modeling schemes permits the simultaneous evaluation of different chemical and biological profiles, which should contribute to the control a priori of causative factors for the high attrition rates in later drug discovery phases.