Multi-combined 3D-QSAR, docking molecular and ADMET prediction of 5-azaindazole derivatives as LRRK2 tyrosine kinase inhibitors

In silico discovery of potent and selective Janus kinase 3 (JAK3) inhibitors through 3D-QSAR, covalent docking, ADMET analysis, molecular dynamics simulations, and binding free energy of pyrazolopyrimidine derivatives

Rheumatoid arthritis is a prevalent and debilitating chronic disease worldwide. Targeting Janus kinase 3 (JAK3) has emerged as a crucial molecular strategy to treat this condition. In this study, we employed a comprehensive theoretical approach that included 3D-QSAR, covalent docking, ADMET, and molecular dynamics to propose and optimize new anti-JAK3 compounds. We investigated a series of 28 1H-pyrazolo[3.4-d]pyrimidin-4-amino inhibitors and developed a highly accurate 3D-QSAR model using comparative molecular similarity index analysis (COMSIA). The model predicted with Q2 = 0.59, R2 = 0.96, and R2(Pred) = 0.89, was validated using Y-randomization and external validation methods. Our covalent docking studies identified T3 and T5 as highly potent inhibitors of JAK3 compared to the reference ligand 17. Additionally, we evaluated the ADMET properties and drug similarity of our newly developed compounds and reference ligand, providing critical insights for further optimization of anti-JAK3 medications. Furthermore, MM-GBSA analysis showed promising results for the designed compounds. Finally, we validated our docking results using molecular dynamics simulations, which confirmed the stability of hydrogen bonding contacts with key residues required to block JAK3 activity. Our findings offer new chemical scaffolds and insights that could lead to the development of novel and effective JAK3 therapeutic targets for treating rheumatoid arthritis.Communicated by Ramaswamy H. Sarma.

Antioxidant and antibacterial activities of 5-mercapto(substitutedthio)-4-substituted-1,2,4-triazol based on nalidixic acid: A comprehensive study on its synthesis, characterization, and In silico evaluation

This study introduces a series of novel Alkyl thio-1,2,4-triazole (4a-p) and mercapto-1,2,4-triazole (3a-d) compounds derived from nalidixic acid. The synthesis was streamlined, involving interactions between nalidixic acid hydrazide and various isothiocyanates to yield cyclic and alkyl(aryl) sulfide compounds, characterized using 1H NMR, 13C NMR, IR, and elemental analysis. Antioxidant capabilities were quantified through DPPH and ABTS assays, highlighting significant potential, especially for compound 3d, which demonstrated an ABTS IC50 value of 0.397 μM, on par with ascorbic acid (IC50 = 0.87 μM). Antibacterial efficacy was established through MIC assessments against a broad spectrum of Gram-positive and Gram-negative bacteria, including Candida albicans. Compounds 3b, 4e, 4h, 4j, 4i, 4m, and 4o showed broad-spectrum activity, with 4k and 4m exhibiting pronounced potency against E. coli. Molecular docking studies validated the antibacterial potential, with compounds 4f and 4h showing high binding affinities (docking scores of -9.8 and -9.6 kcal/mol, respectively), indicating robust interactions with the bacterial enzyme targets. These scores underscore the compounds' mechanistic basis for their antibacterial action and support their therapeutic promise. Furthermore, compounds 3b, 4i, and 4m, identified through drug-likeness and toxicity predictions, were highlighted for their favorable profiles, suggesting their suitability for oral antibiotic therapies. This comprehensive study, blending synthetic, in vitro, and in silico approaches, emphasizes the triazole derivatives' potential as future candidates for antibiotic and antioxidant applications, particularly spotlighting compounds 3b, 4i, and 4m due to their promising efficacy and safety profiles.

Discovery of novel bromodomain-containing protein 4 (BRD4-BD1) inhibitors combined with 3d-QSAR, molecular docking and molecular dynamics in silico

Bromine-containing domain protein 4 (BRD4) plays a crucial role in regulating transcription and genome stability. Selective inhibitors of BRD4-BD1 can specifically target specific bromine domains to affect cell proliferation, apoptosis, and differentiation. In this work, 43 selective benzoazepinone BRD4-BD1 inhibitors were studied using molecular simulations and three-dimensional quantitative conformation relationships (3D-QSAR). A reliable 3D-QSAR model was established based on COMFA (Q2 = 0.532, R2 = 0.981) and COMSIA (S + E + H (Q2 = 0.536, R2 = 0.979) two different analysis methods. Through 3D-QSAR model prediction and quantum chemical analysis, 15 small molecules with stronger inhibitory activity than the template compounds were constructed, and 5 new compounds with higher predictive activity and binding affinity were screened by molecular docking and ADMET methods. According to the molecular dynamics simulation, the key residues that can interact with BRD4-BD1 protein and molecular docking results are consistent, including ASN140, MET132, GLN85, MET105, ASN135 and TYR97. From the MD trajectory, we calculated and analyzed RMSD, RMSF, free binding energy, FECM, DCCM and PCA, the loop region formed by amino acids VAL45∼PRO62 showed α-helix, β-folding and clustering towards the active center with the extension of simulation time. Further optimization of the structure of active candidate compounds A6, A11, A14, and A15 will provide the necessary theoretical basis for the synthesis and activity evaluation of novel BRD4-BD1 inhibitors.Communicated by Ramaswamy H. Sarma.

Mechanistic QSAR modeling derived virtual screening, drug repurposing, ADMET and in-vitro evaluation to identify anticancer lead as lysine-specific demethylase 5a inhibitor

A lysine-specific demethylase is an enzyme that selectively eliminates methyl groups from lysine residues. KDM5A, also known as JARID1A or RBP2, belongs to the KDM5 Jumonji histone demethylase subfamily. To identify novel molecules that interact with the LSD5A receptor, we created a quantitative structure-activity relationship (QSAR) model. A group of 435 compounds was used in a study of the quantitative relationship between structure and activity to guess the IC50 values for blocking LASD5A. We used a genetic algorithm-multilinear regression-based quantitative structure-activity connection model to forecast the bioactivity (PIC50) of 1615 food and drug administration pharmaceuticals from the zinc database with the goal of repurposing clinically used medications. We used molecular docking, molecular dynamic simulation modelling, and molecular mechanics generalised surface area analysis to investigate the molecule's binding mechanism. A genetic algorithm and multi-linear regression method were used to make six variable-based quantitative structure-activity relationship models that worked well (R2 = 0.8521, Q2LOO = 0.8438, and Q2LMO = 0.8414). ZINC000000538621 was found to be a new hit against LSD5A after a quantitative structure-activity relationship-based virtual screening of 1615 zinc food and drug administration compounds. The docking analysis revealed that the hit molecule 11 in the KDM5A binding pocket adopted a conformation similar to the pdb-6bh1 ligand (docking score: -8.61 kcal/mol). The results from molecular docking and the quantitative structure-activity relationship were complementary and consistent. The most active lead molecule 11, which has shown encouraging results, has good absorption, distribution, metabolism, and excretion (ADME) properties, and its toxicity has been shown to be minimal. In addition, the MTT assay of ZINC000000538621 with MCF-7 cell lines backs up the in silico studies. We used molecular mechanics generalise borne surface area analysis and a 200-ns molecular dynamics simulation to find structural motifs for KDM5A enzyme interactions. Thus, our strategy will likely expand food and drug administration molecule repurposing research to find better anticancer drugs and therapies.Communicated by Ramaswamy H. Sarma.

A combined in silico approaches of 2D-QSAR, molecular docking, molecular dynamics and ADMET prediction of anti-cancer inhibitor activity for actinonin derivatives

Inhibition of human mitochondrial peptide deformylase (HsPDF) plays a major role in reducing growth, proliferation, and cellular cancer survival. In this work, a series of 32 actinonin derivatives for HsPDF (PDB: 3G5K) inhibitor's anticancer activity was computationally analyzed for the first time, using an in silico study considering 2D-QSAR modeling, and molecular docking studies, and validated by molecular dynamics and ADMET properties. The results of multilinear regression (MLR) and artificial neural networks (ANN) statistical analysis reveal a good correlation between pIC50 activity and the seven (7) descriptors. The developed models were highly significant with cross-validation, the Y-randomization test and their applicability range. In addition, all considered data sets show that the AC30 compound, exhibits the best binding affinity (docking score = -212.074 kcal/mol and H-bonding energy = -15.879 kcal/mol). Furthermore, molecular dynamics simulations were performed at 500 ns, confirming the stability of the studied complexes under physiological conditions and validating the molecular docking results. Five selected actinonin derivatives (AC1, AC8, AC15, AC18 and AC30), exhibiting best docking score, were rationalized as potential leads for HsPDF inhibition, in well agreement with experimental outcomes. Furthermore, based on the in silico study, new six molecules (AC32, AC33, AC34, AC35, AC36 and AC37) were suggested as HsPDF inhibition candidates, which would be combined with in-vitro and in-vivo studies to perspective validation of their anticancer activity. Indeed, the ADMET predictions indicate that these six new ligands have demonstrated a fairly good drug-likeness profile.

Identification of novel curcumin derivatives against pancreatic cancer: a comprehensive approach integrating 3D-QSAR pharmacophore modeling, virtual screening, and molecular dynamics simulations

Pancreatic cancer, known as the "silent killer," poses a daunting challenge in cancer therapy. The dysregulation of the PI3Kα signaling pathway in pancreatic cancer has attracted considerable interest as a promising target for therapeutic intervention. In this regard, the use of curcumin derivatives as inhibitors of PI3Kα has emerged, providing a novel and promising avenue for developing effective treatments for this devastating disease. Computational approaches were employed to explore this potential and investigate 58 curcumin derivatives with cytotoxic activity against the Panc-1 cell line. Our approach involved ligand-based pharmacophore modeling and atom-based 3D-QSAR analysis. The resulting QSAR model derived from the best-fitted pharmacophore hypothesis (AAHRR_1) demonstrated remarkable performance with high correlation coefficients (R2) of 0.990 for the training set and 0.977 for the test set. The cross-validation coefficient (Q2) of 0.971 also validated the model's predictive power. Tropsha's recommended criteria, including the Y-randomization test, were employed to ensure its reliability. Furthermore, an enrichment study was conducted to evaluate the model's performance in identifying active compounds. AAHRR_1 was used to screen a curated PubChem database of curcumin-related compounds. Two molecules (CID156189304 and CID154728220) exhibited promising pharmacokinetic properties and higher docking scores than Alpelisib, warranting further investigation. Extensive molecular dynamics simulations provided crucial insights into the conformational dynamics within the binding site, validating their stability and behavior. These findings contribute to our understanding of the potential therapeutic effectiveness of these compounds as PI3Kα inhibitors in pancreatic cancer.Communicated by Ramaswamy H. Sarma.

Thiadiazine-thiones as inhibitors of leishmania pteridine reductase (PTR1) target: investigations and in silico approach

Leishmaniasis is a widespread parasitic disease and is one of the major public health concerns in developing countries. Many drugs have been identified for leishmania as targets, but the potential toxicity and long-term treatment remain the most significant problems in terms of further development. The present study employed physicochemical investigations, structure-based virtual screening, ADMET analysis, molecular dynamics simulation, and MM-PBSA, to identify potential compounds for Leishmania. We evaluated 30,926 natural products from the NPASS database, and four potentials passed the pharmacokinetic ADMET studies and were verified using the molecular docking approach. Molecular docking results showed good binding interaction of the compounds with the active site of leishmania pteridine reductase enzyme PTR1, with compound TTC1 showing FRED and Autodock binding energies of -10.33 and -10.94, respectively, which were comparable with the antileishmania drugs of Allopurinol, Miltefosine and the original ligand, methotrexate. TTC1 compound was found to be favorable for hydrophobic interaction with PTR1. In addition, the physicochemical properties of the compounds were studied using the SwissADME web server. All compounds followed Lipinski's rule of five and can be considered as good oral candidates. The analysis of the 100 ns molecular dynamics simulation results based on the best-docked TTC1 with PTR1 receptor demonstrates stable interactions, and the complex undergoes low conformational fluctuations. The average of the calculated binding free energy of the TTC1-1e7w complex is (-68.67 kJ/mol), and the result demonstrated that the TTC1 promoted stability to the Leishmania-PTR1 complex. The potential compounds can be further explored for their antileishmanial activity.Communicated by Ramaswamy H. Sarma.

Combined molecular docking and dynamics simulations studies of natural compounds as potent inhibitors against SARS-CoV-2 main protease

Main protease (Mpro) of SARS-CoV-2 is a key CoV enzyme that plays a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-2 the new strain of coronavirus. In this study, we evaluated biologically active compounds present in medicinal plants as potential SARS-CoV-2 Mpro inhibitors, using a molecular docking study with Autodock Vina software. Top seven compounds Afzelin, Phloroglucinol, Myricetin-3-O- rutinosid Tricin 7-neohesperidoside, Silybin, Kaempferol and Silychristin among 50 molecules of natural Origin (Algerian Medicinal plants) were selected which had better and significantly low binding energy as compared to the reference molecule with binding affinities of -9.3, -9.3, -9, -8.9, -8.5, 8.3 and -8.3 kcal mol^-1 respectively. Then, we analyzed the ADME properties of the best 7 ligands using the Web server SwissADME. Two of small molecules have been shown to be the ideal candidates for further drug development. Finally, the stability of the both compounds complexed with Mpro was validated through molecular dynamics (MD) simulation, they displayed stable trajectory (RMSD, RMSF) and molecular properties with consistent interaction profile in molecular dynamics simulations, moreover, Silybin could form more stable complex with Mpro than Silychristin.Communicated by Ramaswamy H. Sarma.

Topomer CoMFA and HQSAR Study on Benzimidazole Derivative as NS5B Polymerase Inhibitor

Background:

In recent years, the number of people infected with the hepatitis C virus (HCV) is increasing rapidly. This has become a major threat to global health, therefore, new anti- HCV drugs are urgently needed. HCV NS5B polymerase is an RNA-dependent RNA polymerase (RdRp), which plays an important role in virus replication, and can effectively prevent the replication of HCV sub-genomic RNA in daughter cells. It is considered a very promising HCV therapeutic target for the design of anti-HCV drugs.

Methods:

In order to explore the relationship between the structure of benzimidazole derivative and its inhibitory activity on NS5B polymerase, holographic quantitative structure-activity relationship (HQSAR) and Topomer comparative molecular field analysis (CoMFA) were used to establish benzimidazole QSAR model of derivative inhibitors.

Results:

The results show that for the Topomer CoMFA model, the cross-validation coefficient q2 value is 0.883, and the non-cross-validation coefficient r2 value is 0.975. The model is reasonable, reliable, and has a good predictive ability. For the HQSAR model, the cross-validated q2 value is 0.922, and the uncross-validated r2 value is 0.971, indicating that the model data fit well and has a high predictive ability. Through the analysis of the contour map and color code diagram, 40 new benzimidazole inhibitor molecules were designed, and all of them have higher activity than template molecules, and the new molecules have significant interaction sites with protein 3SKE.

Conclusion:

The 3D-QSAR model established by Topomer CoMFA and HQSAR has good prediction results and the statistical verification is valid. The newly designed molecules and docking results provide theoretical guidance for the synthesis of new NS5B polymerase inhibitors and for the identification of key residues that the inhibitors bind to NS5B, which helps to better understand their inhibitory mechanism. These findings are helpful for the development of new anti-HCV drugs.