Correlation between the predicted and the observed biological activity of the symmetric and nonsymmetric cyclic urea derivatives used as HIV-1 protease inhibitors. A 3D-QSAR-CoMFA method for new antiviral drug design

Therapeutic Potentials of Aconite-like Alkaloids: Bioinformatics and Experimental Approaches

Compounds from plants that are used in traditional medicine may have medicinal properties. It is well known that plants belonging to the genus Aconitum are highly poisonous. Utilizing substances derived from Aconitum sp. has been linked to negative effects. In addition to their toxicity, the natural substances derived from Aconitum species may have a range of biological effects on humans, such as analgesic, anti-inflammatory, and anti-cancer characteristics. Multiple in silico, in vitro, and in vivo studies have demonstrated the effectiveness of their therapeutic effects. In this review, the clinical effects of natural compounds extracted from Aconitum sp., focusing on aconitelike alkaloids, are investigated particularly by bioinformatics tools, such as the quantitative structure- activity relationship method, molecular docking, and predicted pharmacokinetic and pharmacodynamic profiles. The experimental and bioinformatics aspects of aconitine's pharmacogenomic profile are discussed. Our review could help shed light on the molecular mechanisms of Aconitum sp. compounds. The effects of several aconite-like alkaloids, such as aconitine, methyllycacintine, or hypaconitine, on specific molecular targets, including voltage-gated sodium channels, CAMK2A and CAMK2G during anesthesia, or BCL2, BCL-XP, and PARP-1 receptors during cancer therapy, are evaluated. According to the reviewed literature, aconite and aconite derivatives have a high affinity for the PARP-1 receptor. The toxicity estimations for aconitine indicate hepatotoxicity and hERG II inhibitor activity; however, this compound is not predicted to be AMES toxic or an hERG I inhibitor. The efficacy of aconitine and its derivatives in treating many illnesses has been proven experimentally. Toxicity occurs as a result of the high ingested dose; however, the usage of this drug in future research is based on the small quantity of an active compound that fulfills a therapeutic role.

Novel (+)-Neoisopulegol-Based O-Benzyl Derivatives as Antimicrobial Agents

Discovery of novel antibacterial agents with new structures, which combat pathogens is an urgent task. In this study, a new library of (+)-neoisopulegol-based O-benzyl derivatives of aminodiols and aminotriols was designed and synthesized, and their antimicrobial activity against different bacterial and fungal strains were evaluated. The results showed that this new series of synthetic O-benzyl compounds exhibit potent antimicrobial activity. Di-O-benzyl derivatives showed high activity against Gram-positive bacteria and fungi, but moderate activity against Gram-negative bacteria. Therefore, these compounds may serve a good basis for antibacterial and antifungal drug discovery. Structure-activity relationships were also studied from the aspects of stereochemistry of the O-benzyl group on cyclohexane ring and the substituent effects on the ring system.

Identifying MurI uncompetitive inhibitors by correlating decomposed binding energies with bioactivity

MurI uncompetitive inhibitors can be virtually identified by a new method that correlates decomposed binding free energies with the bioactivity.

Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks

Artificial neural networks (ANNs) were used to model both inhibition of HIV-1 protease (K(i)) and inhibition of HIV replication (IC90) for 55 cyclic urea derivatives using constitutional and 2D descriptors. As a preliminary step, linear dependences were established by multiple linear regression (MLR) approaches, selecting the relevant descriptors by genetic algorithm (GA) feature selection. For ANN models non-linear GA feature selection was also applied. Non-linear modeling of K(i) overcame the results of the linear one using four properties, keeping in mind standard Pearson R correlation coefficients (0.931 vs. 0.862) and leave one out (LOO) cross-validation analysis (Q(LOO)2 = 0.703 vs. 0.510). On the other hand, IC90 modeling was insoluble by a linear approach: no predictive model was achieved; however, a non-linear relation was encountered according to statistic results (R = 0.891; Q(LOO)2 = 0.568). The best non-linear models suggested the influence of the presence of nitrogen atoms and the molecular volume distribution in the inhibitor structures on the HIV-1 protease inhibition as well as that the inhibition of HIV replication was dependent on the occurrence of five-member rings. Finally, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map built using the input variables of the best non-linear models.

Evaluation of a neural networks QSAR method based on ligand representation using substituent descriptors. Application to HIV-1 protease inhibitors

We present here a neural networks method designed to predict biological activity based on a local representation of the ligand. The compounds of the series are represented by a vector mapping for each of four substituent properties: volume, log P, dipole moment and a simple 'steric' parameter relating to its shape. This ligand representation was tested using neural networks on a set of 42 cyclic-urea derivatives, inhibiting HIV-1 protease. The leave-one-out cross-validation using all descriptors in the input gave a correlation factor between prediction and experiment of 0.76 for the overall set and 0.88 when three outliers were left out. To rank the significance of the four descriptors, we further tested all combinations of two and three parameters for each substituent, using two disjunctive testing sets of five inhibitors. In these sets, vectors with extreme descriptor values were used either in the training or the testing set (sets A and B, respectively). The method is a very good interpolator (set A, 95+/-2% accuracy) but a less effective extrapolator (set B, 85+/-2% accuracy). Generally, the combinations including the 'steric' parameter predict better than average, while those containing the volume are less effective. The best prediction, 98.8+/-1.2%, was obtained when log P, the dipole and the steric parameter were used on set A. At the opposite end, the lowest ranked descriptor set was obtained when replacing log P with the volume, giving 92.3+/-6.7% accuracy over the set A.

Quantitative structure-activity relationship by CoMFA for cyclic urea and nonpeptide-cyclic cyanoguanidine derivatives on wild type and mutant HIV-1 protease

3D-QSAR studies using the Comparative Molecular Field Analysis (CoMFA) methodology were conducted to predict the inhibition constants, K(i), and the inhibitor concentrations, IC90 of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups such as: benzyl, isopropyl, 4-hydroxybenzyl, ketone, oxime, pyrazole, imidazole, triazole and having anti-HIV-1 protease activities. A significant cross-validated correlation coefficient (q2) of 0.63 and a fitted correlation coefficient r2 of 0.70 were obtained, indicating that the models can predict the anti-protease activity from poorly to highly active compounds reliably. The best predictions were obtained for: XV643 (predicted log 1/K(i) = 9.86), a 3,5-dimethoxy-benzyl cyclic urea derivate (molec60, predicted log 1/K(i) = 8.57) and a benzyl cyclic urea derivate (molec 61, predicted log 1/IC90 = 6.87). Using the CoMFA method, we also predicted the biological activity of 14 cyclic urea derivatives that inhibit the HIV-1 protease mutants V82A, V82I and V82F. The predicted biological activities of the: (i) XNO63 (inhibitory activity on the mutant HIV-1 PR V82A), (ii) SB570 (inhibiting the mutant HIV-1 PR V82I) and also (iii) XV652 (during the interaction with the mutant HIV-1 PR V82F) were in good agreement with the experimental values.