Antitumor activity of 3,4-ethylenedioxythiophene derivatives and quantitative structure-activity relationship analysis

Efficient cocrystal coformer screening based on a Machine learning Strategy: A case study for the preparation of imatinib cocrystal with enhanced physicochemical properties

Cocrystal engineering, which involves the self-assembly of two or more components into a solid-state supramolecular structure through non-covalent interactions, has emerged as a promising approach to tailor the physicochemical properties of active pharmaceutical ingredient (API). Efficient coformer screening for cocrystal remains a challenge. Herein, a prediction strategy based on machine learning algorithms was employed to predict cocrystal formation and seven reliable models with accuracy over 0.890 were successfully constructed. Imatinib was selected as the model drug and the models established were applied to screen 31 potential coformers. Experimental verification results indicated RF-8 is the optimal model among seven models with an accuracy of 0.839. When the seven models were combined for coformer screening of Imatinib, the combinational model achieved an accuracy of 0.903, and eight new solid forms were observed and characterized. Benefiting from intermolecular interactions, the obtained multicomponent crystals displayed enhanced physicochemical properties. Dissolution and solubility experiments showed the prepared multicomponent crystals had higher cumulative dissolution rate and remarkably improved the solubility of imatinib, and IM-MC exhibited comparable solubility to Imatinib mesylate α form. Stability test and cytotoxicity results showed that multicomponent crystals exhibited excellent stability and the drug-drug cocrystal IM-5F exhibited higher cytotoxicity than pure API.

HATS5m as an Example of GETAWAY Molecular Descriptor in Assessing the Similarity/Diversity of the Structural Features of 4-Thiazolidinone

Among the various methods for drug design, the approach using molecular descriptors for quantitative structure-activity relationships (QSAR) bears promise for the prediction of innovative molecular structures with bespoke pharmacological activity. Despite the growing number of successful potential applications, the QSAR models often remain hard to interpret. The difficulty arises from the use of advanced chemometric or machine learning methods on the one hand, and the complexity of molecular descriptors on the other hand. Thus, there is a need to interpret molecular descriptors for identifying the features of molecules crucial for desirable activity. For example, the development of structure-activity modeling of different molecule endpoints confirmed the usefulness of H-GETAWAY (H-GEometry, Topology, and Atom-Weights AssemblY) descriptors in molecular sciences. However, compared with other 3D molecular descriptors, H-GETAWAY interpretation is much more complicated. The present study provides insights into the interpretation of the HATS5m descriptor (H-GETAWAY) concerning the molecular structures of the 4-thiazolidinone derivatives with antitrypanosomal activity. According to the published study, an increase in antitrypanosomal activity is associated with both a decrease and an increase in HATS5m (leverage-weighted autocorrelation with lag 5, weighted by atomic masses) values. The substructure-based method explored how the changes in molecular features affect the HATS5m value. Based on this approach, we proposed substituents that translate into low and high HATS5m. The detailed interpretation of H-GETAWAY descriptors requires the consideration of three elements: weighting scheme, leverages, and the Dirac delta function. Particular attention should be paid to the impact of chemical compounds' size and shape and the leverage values of individual atoms.

A simple and robust model to predict the inhibitory activity of α-glucosidase inhibitors through combined QSAR modeling and molecular docking techniques

Quantitative structure-activity relationships (QSAR) and molecular docking studies have been performed on a series of 35 α-glucosidase inhibitory derivatives. The QSAR models have been developed by genetic algorithm-multiple linear regression (GA-MLR) and least squares-support vector machine (LS-SVM) methods to correlate the conformational descriptors to the inhibitory activity. The obtained models with 5 descriptors were validated and illustrated to be statistically significant. They had desirable prediction based on squared correlation coefficient (R²), cross-validated correlation coefficient (Q²), root-mean-squares error (RMSE) and Fisher (F) parameters (R² = 0.951, Q² = 0.931, RMSE = 0.121, and F = 114.629 for GA-MLR model, and R² = 0.989, Q² = 0.987, RMSE = 0.056 and F = 543.754 for LS-SVM model). The crucial descriptor named DELS was explored to have the highest correlation with the inhibitory activity and thus has been chosen to build a simple model. The QSAR model developed with this mono-descriptor showed appropriate results of the predicted model using LS-SVM method (R² = 0.888, Q² = 0.872, RMSE = 0.185 and F = 221.459). Also, molecular docking which focuses on the interaction between ligands and α-glucosidase in the protein active site considered different binding positions to find the best binding mode. It helped the QSAR study to propose more comprehensive details of the compounds structures and was used to design more active compounds. The most active designed compound had a high inhibitory activity of 9.22 that can be proposed for the treatment of diabetes type 2.

Design of potential anti-tumor PARP-1 inhibitors by QSAR and molecular modeling studies

Poly ADP-ribose polymerase-1 (PARP-1) inhibitors have been recognized as new agents for the treatment of patients with breast cancer type 1 (BRCA1) disorders. The quantitative structure-activity relationships (QSAR) technique was used in order to achieve the required medicines for anticancer activity easier and faster. In this study, the QSAR method was developed to predict the half-maximal inhibitory concentration (IC₅₀) of 51 1H-benzo[d]immidazole-4-carboxamide derivatives by genetic algorithm-multiple linear regression (GA-MLR) and least squares-support vector machine (LS-SVM) methods. Results in the best QSAR model represented the coefficient of leave-one-out cross-validation (Q _cv ² ) = 0.971, correlation coefficient (R²) = 0.977, Fisher parameter (F) = 259.016 and root mean square error (RMSE) = 0.095, respectively, which indicated that the LS-SVM model had a good potential to predict the pIC₅₀ (9 - log(IC₅₀ nM)) values compared with other modeling methods. Also, molecular docking evaluated interactions between ligands and enzyme and their free energy of binding were calculated and used as descriptors. Molecular docking and the QSAR study completed each other. The results represented that the final model can be useful to design some new inhibitors. So, the knowledge of the QSAR modeling and molecular docking was used in pIC₅₀ prediction and 51 new compounds were developed as PARP-1 inhibitors that 9 compounds had the best-proposed values for pIC₅₀. The maximum enhancement of the inhibitory activity of compounds was 33.394%.

Design and Synthesis of Benzimidazole-Chalcone Derivatives as Potential Anticancer Agents

Numerous reports have shown that conjugated benzimidazole derivatives possess various kinds of biological activities, including anticancer properties. In this report, we designed and synthesized 24 new molecules comprising a benzimidazole ring, arene, and alkyl chain-bearing cyclic moieties. The results showed that the N-substituted benzimidazole derivatives bearing an alkyl chain and a nitrogen-containing 5- or 6-membered ring enhanced the cytotoxic effects on human breast adenocarcinoma (MCF-7) and human ovarian carcinoma (OVCAR-3) cell lines. Among the 24 synthesized compounds, (2E)-1-(1-(3-morpholinopropyl)-1H-benzimidazol-2 -yl)-3-phenyl-2-propen-1-one) (23a) reduced the proliferation of MCF-7 and OVCAR-3 cell lines demonstrating superior outcomes to those of cisplatin.

Synthesis of dibenzothiazepine analogues by one-pot S-arylation and intramolecular cyclization of diaryl sulfides and evaluation of antibacterial properties

Dibenzothiazepine analogues containing lactam, amidine and imine moieties were prepared from 2-aminophenyl disulfides via one-pot S-arylation. The S-arylation involved cleavage of an S-S bond of disulfides and SNAr reaction in aqueous ammonia solution of L-cysteine to afford diaryl sulfides. Dibenzothiazepine analogues having lactam and amidine moieties were obtained by cyclization of the corresponding diaryl sulfides under acidic conditions. One-pot S-arylation of 2-bromo-5-nitrobenzaldehyde gave dibenzothiazepine analogues with an imine moiety in one step through intramolecular cyclization. Compounds with antibacterial activities against Staphylococcus aureus and Escherichia coli were obtained.

SnCl2-catalyzed synthesis of dihydro-5H-benzo[f]pyrazolo[3,4-b]quinoline and dihydroindeno[2,1-b]pyrazolo[4,3-e]pyridine with high fluorescence and their photophysical properties

A series of pyrazoloquinoline and pyrazolopyridine based derivatives bearing donor–acceptor (D–A) substituent groups on the phenyl ring, was synthesized by a mild reaction condition.

Deep Eutectic Solvents as Convenient Media for Synthesis of Novel Coumarinyl Schiff Bases and Their QSAR Studies

Deep eutectic solvents, as green and environmentally friendly media, were utilized in the synthesis of novel coumarinyl Schiff bases. Novel derivatives were synthesized from 2-((4-methyl-2-oxo-2H-chromen-7-yl)oxy)acetohydrazide and corresponding aldehyde in choline chloride:malonic acid (1:1) based deep eutectic solvent. In these reactions, deep eutectic solvent acted as a solvent and catalyst as well. Novel Schiff bases were synthesized in high yields (65–75%) with no need for further purification, and their structures were confirmed by mass spectra, ¹H and ¹³C NMR. Furthermore, their antioxidant activity was determined and compared to antioxidant activity of previously synthesized derivatives, thus investigating their structure–activity relationship utilizing quantitative structure-activity relationship QSAR studies. Calculation of molecular descriptors has been performed by DRAGON software. The best QSAR model (R_tr = 0.636; R_ext = 0.709) obtained with three descriptors (MATS3m, Mor22u, Hy) implies that the pairs of atoms higher mass at the path length 3, three-dimensional arrangement of atoms at scattering parameter s = 21 Å⁻¹, and higher number of hydrophilic groups (-OH, -NH) enhanced antioxidant activity. Electrostatic potential surface of the most active compounds showed possible regions for donation of electrons to 1,1-diphenyl-2-picryhydrazyl (DPPH) radicals.