Towards new bioactive fluorine-containing 1,3,4-oxadiazole-amide derivatives: synthesis, antibacterial activity, molecular docking and molecular dynamics simulation study

Through the approach of molecular hybridization, this study rationally designed and synthesized new trifluoromethyl-1,3,4-oxadiazole amide derivatives, denoted as 1a-1n. The findings reveal that these novel molecules exhibit potent inhibitory effects against various bacterial strains. Thereinto, compounds 1c, 1d, 1i, 1j and 1n, demonstrate relatively superior antimicrobial performance against B. cereus FM314, with a minimum inhibitory concentration (MIC) of 0.03907 μg/mL. Molecular docking analysis suggests the potential importance of the Ser57 and Thr125 amino acid residues (PDB ID: 4EI9) in contributing to the inhibitory activity against B. cereus. The consistency of these results was further corroborated through subsequent molecular dynamics simulations and MMPBSA validations. The insights gained from this study serve to facilitate the rational design and efficient development of novel eco-friendly antimicrobial inhibitors based on the trifluoromethyl-1,3,4-oxadiazole amide scaffold.

Synthesis and Antifungal Properties of 1,2,4-Triazole Schiff Base Agents Based on a 3D-QSAR Model

Based on our previous research, a 3D-QSAR model (q2=0.51, ONC=5, r2=0.982, F=271.887, SEE=0.052) was established to predict the inhibitory effects of triazole Schiff base compounds on Fusarium graminearum, and its predictive ability was also confirmed through the statistical parameters. According to the results of the model design, 30 compounds with superior bioactivity compared to the template molecule 4 were obtained. Seven of these compounds (DES2-6, DES9-10) with improved biological activity and readily available raw materials were successfully synthesized. Their structures were confirmed through HRMS, NMR, and single crystal X-ray diffraction analysis (DES-5). The bioactivity of the final products was investigated through an in vitro antifungal assay. There was little difference in the EC50 values between the experimental and predicted values of the model, demonstrating the reliability of the model. Especially, DES-3 (EC50=9.915 mg/L) and DES-5 (EC50=9.384 mg/L) exhibited better inhibitory effects on Fusarium graminearum compared to the standard drug (SD) triadimenol (EC50=10.820 mg/L). These compounds could serve as potential new fungicides for future research. The interaction between the final products and isocitrate lyase (ICL) was investigated through molecular docking. Compounds with R groups that have a higher electron-donating capacity were found to be biologically active.

Design of novel DABO derivatives as HIV-1 RT inhibitors using molecular docking, molecular dynamics simulations and ADMET properties

HIV-1 reverse transcriptase is an important target for developing effective anti-HIV-1 inhibitors. Different types of small molecules have been designed based on this target, showing different levels of inhibitory activity against various types of HIV-1 strains. The relationship between structure and activity of DABO derivatives was investigated by means of 3D-QSAR molecular model, molecular docking, molecular dynamics and ADMET properties. The statistical results of molecular models show that the CoMFA and CoMSIA models have good internal stability (CoMFA: q2 = 0.623, r2 = 0.946; CoMSIA: q2 = 0.668, r2 = 0.983) and external prediction ability (CoMFA: rpred2 = 0.961; CoMSIA: rpred2 = 0.961). In addition, molecular docking has explored the mechanism of action between small molecules and receptor proteins, and the results show that hydrogen bonding between amino acid Lys101 and small molecules can improve the affinity of ligands to receptor binding. A total of 12 novel molecules were designed and their activities were predicted based on the 3D-QSAR model and molecular docking results. The results showed that the designed molecules had higher predictive activity. Subsequently, 100 ns MD simulation and binding free energy verified the stability of molecular docking results. Finally, the pharmacokinetic properties of the novel designed molecule were verified by using ADMET to predict its properties. These results can provide reference for the design and development of novel and effective HIV-1 RT inhibitors, and provide new ideas for the design of subsequent drugs.Communicated by Ramaswamy H. Sarma.

Exploring the Efficacy of Noncovalent SARS-CoV-2 Main Protease Inhibitors: A Computational Simulation Analysis Study

The SARS-CoV-2 main protease, as a key target for antiviral therapeutics, is instrumental in maintaining virus stability, facilitating translation, and enabling the virus to evade innate immunity. Our research focused on designing non-covalent inhibitors to counteract the action of this protease. Utilizing a 3D-QSAR model and contour map, we successfully engineered eight novel non-covalent inhibitors. Further evaluation and comparison of these novel compounds through methodologies including molecular docking, ADMET analysis, frontier molecular orbital studies, molecular dynamics simulations, and binding free energy revealed that the inhibitors N02 and N03 demonstrated superior research performance (N02 ΔGbind=-206.648 kJ/mol, N03 ΔGbind=-185.602 kJ/mol). These findings offer insightful guidance for the further refinement of molecular structures and the development of more efficacious inhibitors. Consequently, future investigations can draw upon these findings to unearth more potent inhibitors, thereby amplifying their impact in the treatment and prevention of associated diseases.

3D-QSAR, molecular docking, and molecular dynamics analysis of novel biphenyl-substituted pyridone derivatives as potent HIV-1 NNRTIs

When designing new medications targeting HIV-1, drug designers concentrate on reverse transcriptase (RT), the central enzyme of their concern. This is due to its vital role in converting single-stranded RNA into double-stranded DNA throughout the life cycle of HIV-1. In recent reports, a series of newly discovered pyridone derivatives with biphenyl substitutions have emerged as highly potent HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs), displaying impressive antiviral activity. To analyse the three-dimensional quantitative structure-activity relationship (3D-QSAR) of pyridone inhibitors with biphenyl substitutions, we employed CoMFA and CoMSIA methods in this study. The dataset comprises a total of 51 compounds. The findings of this research demonstrate that both the CoMFA (q2=0.688, r2=0.976, rpred2=0.831) and CoMSIA/SHE (q2=0.758, r2=0.968, rpred2=0.828) models exhibit excellent predictive capability and reliable estimation stability. According to the findings of the model, we designed a collection of eleven molecules that exhibit the potential for significantly improved predictive activity. We proceeded to investigate the binding patterns of these compounds to receptor proteins utilizing the molecular docking technique. To ensure the reliability of the docking results, we went on to validate them by conducting molecular dynamics simulations and performing accurate calculations of the binding free energy. Moreover, based on initial ADMET predictions, the results consistently indicate that the newly created molecule possesses favourable pharmacokinetic properties. This study will help to facilitate the development of efficient novel inhibitors that specifically target HIV-1's non-nucleoside reverse transcriptase (NNRTIs).Communicated by Ramaswamy H. Sarma.

Fragment-pair based drug molecule solubility prediction through attention mechanism

The purpose of drug discovery is to identify new drugs, and the solubility of drug molecules is an important physicochemical property in medicinal chemistry, that plays a crucial role in drug discovery. In solubility prediction, high-precision computational methods can significantly reduce the experimental costs and time associated with drug development. Therefore, artificial intelligence technologies have been widely used for solubility prediction. This study utilized the attention layer in mechanism in the deep learning model to consider the atomic-level features of the molecules, and used gated recurrent neural networks to aggregate vectors between layers. It also utilized molecular fragment technology to divide the complete molecule into pairs of fragments, extracted characteristics from each fragment pair, and finally fused the characteristics to predict the solubility of drug molecules. We compared and evaluated our method with five existing models using two performance evaluation indicators, demonstrating that our method has better performance and greater robustness.

Computational study of quinoline-based thiadiazole compounds as potential antileishmanial inhibitors

Leishmaniasis is a severe disease caused by protozoan parasites of the genus Leishmania and it is accountable for sizable morbidity and mortality worldwide.

Evaluation of novel HIV-1 protease inhibitors with DRV-resistance by utilizing 3D-QSAR molecular docking and molecular dynamics simulation

Molecular dynamics simulations were performed to explore the interaction mode of DRV derivatives binding to target proteins and to identify new potential HIV-1 PR inhibitors with stronger activity.