Reduced estrogenic risks of a sunscreen additive: Theoretical design and evaluation of functionally improved salicylates

Salicylic esters (SEs), the widely used ultraviolet (UV) absorbers in sunscreen products, have been found to have health risks such as skin sensitization and estrogenic effects. This study aims to design SE substitutes that maintain high UV absorbance while reducing estrogenicity. Using molecular docking and Gaussian09 software for initial assessments and further application of a combination of two-dimensional and three-dimensional quantitative structure-activity relationships (2D-QSAR and 3D-QSAR, respectively) models, we designed 73 substitutes. The best-performing molecules, ethylhexyl salicylate (EHS)-5 and EHS-15, significantly reduced estrogenicity (44.54 % and 17.60 %, respectively) and enhanced UV absorbance (249.56 % and 46.94 %, respectively). Through screening for human health risks, we found that EHS-5 and EHS-15 were free from skin sensitivity and eye irritation and exhibited reduced skin permeability compared with EHS. Furthermore, the photolysis and synthetic pathways of EHS-5 and EHS-15 were deduced, demonstrating their good photodegradability and potential synthesizability. In addition, we analyzed the mechanisms underlying the changes in estrogenic effects and UV absorption properties. We identified covalent hydrogen bond basicity and acidity Propgen value for atomic molecular properties and the highest occupied molecular orbital eigenvalue as the main factors affecting the estrogenic effect and UV absorbance of SEs, respectively. This study focuses on the design and screening of SEs, exhibiting enhanced functionality, reduced health risks, and synthetic feasibility.

Design of vilazodone-donepezil chimeric derivatives as acetylcholinesterase inhibitors by QSAR, molecular docking and molecular dynamics simulations

Alzheimer's disease (AD) is a disease that affects the cognitive abilities of older adults, and it is one of the biggest global medical challenges of the 21st century. Acetylcholinesterase (AChE) can increase acetylcholine concentrations and improve cognitive function in patients, and is a potential target to develop small molecule inhibitors for the treatment of Alzheimer's disease (AD). In this study, 29 vilazodone-donepezil chimeric derivatives are systematically studied using 3D-QSAR modeling, and a robust and reliable Topomer CoMFA model was obtained with: q2 = 0.720, r2 = 0.991, F = 287.234, N = 6, and SEE = 0.098. Based on the established model and combined with the ZINC20 database, 33 new compounds with ideal inhibitory activity are successfully designed. Molecular docking and ADMET property prediction also show that these newly designed compounds have a good binding ability to the target protein and can meet the medicinal conditions. Subsequently, four new compounds with good comprehensive ability are selected for molecular dynamics simulation, and the simulation results confirm that the newly designed compounds have a certain degree of reliability and stability. This study provides guidance for vilazodone-donepezil chimeric derivatives as a potential AChE inhibitor and has certain theoretical value.

Scaffold-Hopping Strategies in Aurone Optimization: A Comprehensive Review of Synthetic Procedures and Biological Activities of Nitrogen and Sulfur Analogues

Aurones, particular polyphenolic compounds belonging to the class of minor flavonoids and overlooked for a long time, have gained significative attention in medicinal chemistry in recent years. Indeed, considering their unique and outstanding biological properties, they stand out as an intriguing reservoir of new potential lead compounds in the drug discovery context. Nevertheless, several physicochemical, pharmacokinetic, and pharmacodynamic (P3) issues hinder their progression in more advanced phases of the drug discovery pipeline, making lead optimization campaigns necessary. In this context, scaffold hopping has proven to be a valuable approach in the optimization of natural products. This review provides a comprehensive and updated picture of the scaffold-hopping approaches directed at the optimization of natural and synthetic aurones. In the literature analysis, a particular focus is given to nitrogen and sulfur analogues. For each class presented, general synthetic procedures are summarized, highlighting the key advantages and potential issues. Furthermore, the biological activities of the most representative scaffold-hopped compounds are presented, emphasizing the improvements achieved and the potential for further optimization compared to the aurone class.

In silico screening of a series of 1,6-disubstituted 1H-pyrazolo[3,4-d]pyrimidines as potential selective inhibitors of the Janus kinase 3

Rheumatoid arthritis is a common chronic disabling inflammatory disease that is characterized by inflammation of the synovial membrane and leads to discomfort. In the current study, twenty-seven 1,6-disubstituted 1H-pyrazolo[3,4-d]pyrimidines were tested as potential selective inhibitors of the tyrosine-protein kinase JAK3 using a number of molecular modeling methods. The activity of the screened derivatives was statistically quantified using multiple linear regression and artificial neural networks. To assess the quality, robustness, and predictability of the generated models, the leave-one-out cross-validation method was applied with favorable results (Q2 = 0.75) and Y-randomization. In addition, the evaluation of the predictive ability of the established model was confirmed by means of an external validation using a composite test set and an applicability domain approach. The covalent docking indicated that the tested 1H-pyrazolo[3,4-d]pyrimidines containing the acrylic aldehyde moiety had irreversible interaction with the residue Cys909 in the active sites of the tyrosine-protein kinase JAK3 by Michael addition. The molecular dynamics for three selected derivatives (compounds 9, 12, and 18) were used to verify the covalent docking by determining the stability of hydrogen bonding interactions with active sites, which are needed to stop tyrosine-protein kinase JAK3. The results obtained showed that the tested compounds containing acrylic aldehyde moiety had favorable binding free energies, indicating a strong affinity for the JAK3 enzyme. Overall, this current study suggests that the tested compounds containing the acrylic aldehyde moiety have the potential to act as anti-JAK3 inhibitors. They could be explored further to be used as treatment options for rheumatoid arthritis.Communicated by Ramaswamy H. Sarma.

Blueberry extract and its bioactive compounds mitigate oxidative stress and suppress human lung cancer cell (A549) growth by modulating the expression of p53/EGFR/STAT3/IL6-mediated signaling molecules

Bioactive phytocompounds are crucial components in all plants. Since the time of traditional medicine, the utilization of plants has been grounded in the potential of these bioactive compounds to treat or manage specific illnesses. These natural bioactive compounds have sparked growing interest in employing medicinal plants for addressing various conditions, such as inflammatory diseases, diabetes, and cancer. This study focuses on assessing the qualitative phytochemical composition, antioxidant potential, and cytotoxic effects of blueberry (Vaccinium sect. Cyanococcus) extract using three different solvents, namely water, ethanol, and methanol. The extract exhibited notable antioxidant activities, as evidenced by DPPH and H2O2 free radical scavenging assays. The cell viability assay also demonstrated cell growth inhibition in A549 cells. Furthermore, nine specific phytocompounds sourced from existing literature were selected for molecular docking studies against CDK6 and, AMPK key protein kinases which enhance the cancer progression. The molecular docking results also revealed favorable binding scores, with a high score of -9.5 kcal/mol in CDK6 protein and a maximum score of AMPK with targets of -8.8 kcal/mol. The selected phytocompounds' pharmacodynamic properties such as ADMET also supported the study. Furthermore, rutin stated that pre-dominantly present in blueberry plants shows a potent cytotoxicity effect in A549 cells. Functional annotations by bioinformatic analysis for rutin also revealed the strong enrichment in the involvement of PI3K/AKT1/STAT, and p53 signaling pathways. Based on this analysis, the identified rutin and other compounds hold a promising anticancer activity. Overall, the comprehensive evaluation of both in vitro and in silico data suggests that the Vaccinium sect. Cyanococcus extract could serve as a valuable source of pharmaceutical agents and may prove effective in future therapeutic applications.

Revealing innovative JAK1 and JAK3 inhibitors: a comprehensive study utilizing QSAR, 3D-Pharmacophore screening, molecular docking, molecular dynamics, and MM/GBSA analyses

The heterocycle compounds, with their diverse functionalities, are particularly effective in inhibiting Janus kinases (JAKs). Therefore, it is crucial to identify the correlation between their complex structures and biological activities for the development of new drugs for the treatment of rheumatoid arthritis (RA) and cancer. In this study, a diverse set of 28 heterocyclic compounds selective for JAK1 and JAK3 was employed to construct quantitative structure-activity relationship (QSAR) models using multiple linear regression (MLR). Artificial neural network (ANN) models were employed in the development of QSAR models. The robustness and stability of the models were assessed through internal and external methodologies, including the domain of applicability (DoA). The molecular descriptors incorporated into the model exhibited a satisfactory correlation with the receptor-ligand complex structures of JAKs observed in X-ray crystallography, making the model interpretable and predictive. Furthermore, pharmacophore models ADRRR and ADHRR were designed for each JAK1 and JAK3, proving effective in discriminating between active compounds and decoys. Both models demonstrated good performance in identifying new compounds, with an ROC of 0.83 for the ADRRR model and an ROC of 0.75 for the ADHRR model. Using a pharmacophore model, the most promising compounds were selected based on their strong affinity compared to the most active compounds in the studied series each JAK1 and JAK3. Notably, the pharmacokinetic, physicochemical properties, and biological activities of the selected compounds (As compounds ZINC79189223 and ZINC66252348) were found to be consistent with their therapeutic effects in RA, owing to their non-toxic, cholinergic nature, absence of P-glycoprotein, high gastrointestinal absorption, and ability to penetrate the blood-brain barrier. Furthermore, ADMET properties were assessed, and molecular dynamics and MM/GBSA analysis revealed stability in these molecules.

QSAR-driven screening uncovers and designs novel pyrimidine-4,6-diamine derivatives as potent JAK3 inhibitors

This study presents a robust and integrated methodology that harnesses a range of computational techniques to facilitate the design and prediction of new inhibitors targeting the JAK3/STAT pathway. This methodology encompasses several strategies, including QSAR analysis, pharmacophore modeling, ADMET prediction, covalent docking, molecular dynamics (MD) simulations, and the calculation of binding free energies (MM/GBSA). An efficacious QSAR model was meticulously crafted through the employment of multiple linear regression (MLR). The initial MLR model underwent further refinement employing an artificial neural network (ANN) methodology aimed at minimizing predictive errors. Notably, both MLR and ANN exhibited commendable performance, showcasing R2 values of 0.89 and 0.95, respectively. The model's precision was assessed via leave-one-out cross-validation (CV) yielding a Q2 value of 0.65, supplemented by rigorous Y-randomization. , The pharmacophore model effectively differentiated between active and inactive drugs, identifying potential JAK3 inhibitors, and demonstrated validity with an ROC value of 0.86. The newly discovered and designed inhibitors exhibited high inhibitory potency, ranging from 6 to 8, as accurately predicted by the QSAR models. Comparative analysis with FDA-approved Tofacitinib revealed that the new compounds exhibited promising ADMET properties and strong covalent docking (CovDock) interactions. The stability of the new discovered and designed inhibitors within the JAK3 binding site was confirmed through 500 ns MD simulations, while MM/GBSA calculations supported their binding affinity. Additionally, a retrosynthetic study was conducted to facilitate the synthesis of these potential JAK3/STAT inhibitors. The overall integrated approach demonstrates the feasibility of designing novel JAK3/STAT inhibitors with robust efficacy and excellent ADMET characteristics that surpass Tofacitinib by a significant margin.Communicated by Ramaswamy H. Sarma.

Molecular modelling studies of substituted indole derivatives as novel influenza a virus inhibitors

The emergence of drug-resistant strains motivate researchers to find new innovative anti-IAV candidates with a different mode of action. In this work, molecular modelling strategies, such as 2D-QSAR, 3D-QSAR, molecular docking, molecular dynamics, FMOs, and ADMET were applied to some substituted indoles as IAV inhibitors. The best-developed 2D-QSAR models, MLR (Q2 = 0.7634, R2train = 0.8666) and ANN[4-3-1] (Q2 = 0.8699, R2train = 0.8705) revealed good statistical validation for the inhibitory response predictions. The 3D-QSAR models, CoMFA (Q2 = 0.504, R2train = 0.805) and CoMSIA/SEDHA (Q2 = 0.619, R2train = 0.813) are selected as the best 3D models following the global thresholds. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrate the relationship between the molecular fields and the inhibitory effects of the studied molecules. The results of the studies led to the design of five new molecules (24a-e) with enhanced anti-IAV activities and binding potentials using the most active molecule (24) as the template scaffold. The conformational stability of the best-designed molecules with the NA protein showed hydrophobic and H-bonds with the key residues from the molecular dynamics simulations of 100 ns. Furthermore, the global reactivity indices from the DFT calculations portrayed the relevance of 24c in view of its smaller band gap as also justified by our QSAR and molecular simulation studies.Communicated by Ramaswamy H. Sarma.

Discovery of anti-colon cancer agents targeting wild-type and mutant p53 using computer-aided drug design

Mutations in the p53 gene are common and occur in over 50% of all cancers, as it is involved in DNA damage repair, cell cycle regulation and apoptosis. Moreover, the p53 gene is mutated in 70% of colon cancers. Therefore, the development of drugs to combat this mutation requires urgent attention. With this in mind, in silico drug design approaches were applied on quinoline derivatives with anticancer activity. In 3D-QSAR study, steric, electrostatic, hydrophobic and H-bond acceptor fields (SEHA) play an important role in prediction and design of new colon cancer compounds. Indeed, the two best CoMSIA/SEHA models with (Q2 = 0.737, R2 = 0.914, R pred 2 = 0.720) and (Q2 = 0.738, R2 = 0.919, R pred 2 = 0.739) show good prediction of human colon carcinoma HCT 116 (p53+/+) and (p53-/-) activities, respectively. Furthermore, the predictive ability and robustness of these models were tested by several validation methods. Molecular docking analyses reveal crucial interactions with the active sites of the p53 protein in both wild type and mutant. Based on these theoretical studies, we designed 10 new compounds with good anticancer activity potential, which were evaluated using ADMET properties. Molecular dynamics simulations were performed to confirm the detailed binding mode of the docking results. Finally, the MM-GBSA based on molecular dynamics simulation confirmed that the designed compounds were able to form stable hydrogen bonding interactions with the crucial residues, which are essential to overcome the p53 mutation in colon cancer.Communicated by Ramaswamy H. Sarma.

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.

Exploration of 2D and 3D-QSAR analysis and docking studies for novel dihydropteridone derivatives as promising therapeutic agents targeting glioblastoma

Background: Dihydropteridone derivatives represent a novel class of PLK1 inhibitors, exhibiting promising anticancer activity and potential as chemotherapeutic drugs for glioblastoma. Objective: The aim of this study is to develop 2D and 3D-QSAR models to validate the anticancer activity of dihydropteridone derivatives and identify optimal structural characteristics for the design of new therapeutic agents. Methods: The Heuristic method (HM) was employed to construct a 2D-linear QSAR model, while the gene expression programming (GEP) algorithm was utilized to develop a 2D-nonlinear QSAR model. Additionally, the CoMSIA approach was introduced to investigate the impact of drug structure on activity. A total of 200 novel anti-glioma dihydropteridone compounds were designed, and their activity levels were predicted using chemical descriptors and molecular field maps. The compounds with the highest activity were subjected to molecular docking to confirm their binding affinity. Results: Within the analytical purview, the coefficient of determination (R2) for the HM linear model is elucidated at 0.6682, accompanied by an R2 cv of 0.5669 and a residual sum of squares (S2) of 0.0199. The GEP nonlinear model delineates coefficients of determination for the training and validation sets at 0.79 and 0.76, respectively. Empirical modeling outcomes underscore the preeminence of the 3D-QSAR model, succeeded by the GEP nonlinear model, whilst the HM linear model manifested suboptimal efficacy. The 3D paradigm evinced an exemplary fit, characterized by formidable Q2 (0.628) and R2 (0.928) values, complemented by an impressive F-value (12.194) and a minimized standard error of estimate (SEE) at 0.160. The most significant molecular descriptor in the 2D model, which included six descriptors, was identified as "Min exchange energy for a C-N bond" (MECN). By combining the MECN descriptor with the hydrophobic field, suggestions for the creation of novel medications were generated. This led to the identification of compound 21E.153, a novel dihydropteridone derivative, which exhibited outstanding antitumor properties and docking capabilities. Conclusion: The development of 2D and 3D-QSAR models, along with the innovative integration of contour maps and molecular descriptors, offer novel concepts and techniques for the design of glioblastoma chemotherapeutic agents.

In silico design of novel CDK2 inhibitors through QSAR, ADMET, molecular docking and molecular dynamics simulation studies

The present study aims to investigate about the quantitative structure-activity relationship (QSAR) of a series of Thiazole derivatives reported as anticancer agents (hepatocellular carcinoma), using principally the electronic descriptors calculated by the DFT method and by applying the multiple linear regression method. The developed model showed good statistical parameters (R2 = 0.725, R2adj = 0.653, MSE = 0.060, R2test = 0.827, Q2cv = 0.536). The energy EHOMO orbital, electronic energy (TE), shape coefficient (I), number of rotatable bonds (NROT), and index of refraction (n) were revealed to be the main descriptors influencing the anti-cancer activity. Further, new Thiazole derivatives have been designed and their activities and pharmacokinetic properties have been predicted using the validated QSAR model. The designed molecules were then assessed to molecular docking (MD), and molecular dynamic (MDs) simulation accompanied by the calculation of the binding affinity using MMPBSA script according to 100 ns a simulation trajectory, to study both their affinity and their stability towards CDK2 as a target protein for the cancer disease treatment. This research concluded with the identification of four new CDK2 inhibitors which are A1, A3, A5, and A6 showing good pharmacokinetic properties. The MDs results revealed that the newly designed compound A5 remained stable in the active center of the discovered CDK2 protein, indicating its potential as a novel inhibitor for the treatment of hepatocellular carcinoma. The current findings may eventually contribute to the development of robust CDK2 inhibitors in the future.Communicated by Ramaswamy H. Sarma.

Designing of 2,3-dihydrobenzofuran derivatives as inhibitors of PDE1B using pharmacophore screening, ensemble docking and molecular dynamics approach

In recent years, the PDE1B enzyme has become a desirable drug target for the treatment of psychological and neurological disorders, particularly schizophrenia disorder, due to the expression of PDE1B in brain regions involved in volitional behaviour, learning and memory. Although several inhibitors of PDE1 have been identified using different methods, none of these inhibitors has reached the market yet. Thus, searching for novel PDE1B inhibitors is considered a major scientific challenge. In this study, pharmacophore-based screening, ensemble docking and molecular dynamics simulations have been performed to identify a lead inhibitor of PDE1B with a new chemical scaffold. Five PDE1B crystal structures have been utilised in the docking study to improve the possibility of identifying an active compound compared to the use of a single crystal structure. Finally, the structure-activity- relationship was studied, and the structure of the lead molecule was modified to design novel inhibitors with a high affinity for PDE1B. As a result, two novel compounds have been designed that exhibited a higher affinity to PDE1B compared to the lead compound and the other designed compounds.

3D- and 2D-QSAR models’ study and molecular docking of novel nitrogen-mustard compounds for osteosarcoma

Background: The dipeptide-alkylated nitrogen-mustard compound is a new kind of nitrogen-mustard derivative with a strong anti-tumor activity, which can be used as a potential anti-osteosarcoma chemotherapy drug.

Objective: 2D- and 3D-QSAR (structure–activity relationship quantification) models were established to predict the anti-tumor activity of dipeptide-alkylated nitrogen-mustard compounds.

Method: In this study, a linear model was established using a heuristic method (HM) and a non-linear model was established using the gene expression programming (GEP) algorithm, but there were more limitations in the 2D model, so a 3D-QSAR model was introduced and established through the CoMSIA method. Finally, a series of new dipeptide-alkylated nitrogen-mustard compounds were redesigned using the 3D-QSAR model; docking experiments were carried out on several compounds with the highest activity against tumors.

Result: The 2D- and 3D-QSAR models obtained in this experiment were satisfactory. A linear model with six descriptors was obtained in this experiment using the HM through CODESSA software, where the descriptor “Min electroph react index for a C atom” has the greatest effect on the compound activity; a reliable non-linear model was obtained using the GEP algorithm model (the best model was generated in the 89th generation cycle, with a correlation coefficient of 0.95 and 0.87 for the training and test set, respectively, and a mean error of 0.02 and 0.06, respectively). Finally, 200 new compounds were designed by combining the contour plots of the CoMSIA model with each other, together with the descriptors in the 2D-QSAR, among which compound I1.10 had a high anti-tumor and docking ability.

Conclusion: Through the model established in this study, the factors influencing the anti-tumor activity of dipeptide-alkylated nitrogen-thaliana compounds were revealed, providing direction and guidance for the further design of efficient chemotherapy drugs against osteosarcoma.

Development of reliable quantitative structure-toxicity relationship models for toxicity prediction of benzene derivatives using semiempirical descriptors

The Health and environmental hazards of benzene and nitrobenzene (NB) derivatives have remained a topic of interest of researchers. In silico methods for prediction of toxicity of chemicals have proved their worth in accurate forecast of environmental as well as health toxicity and are strongly recommended by regulatory authorities. Two quantitative structure-toxicity relationship (QSTR) models explaining Scenedesmus obliquus toxicity trends among 39 benzene derivatives and Tetrahymena pyriformis toxicity of 103 NB and 392 benzene derivatives are developed using semiempirical quantum chemical parameters. The best constructed QSTR models have good fitting ability (R² = 0.8053, 0.7591, and 0.8283) and robustness (Q²_LOO = 0.7507, 0.7227, and 0.8194; Q²_LMO = 0.7338, 0.7153, and 0.8172). The external predictivity of all the models are quite good (R²_EXT = 0.8256, 0.9349, and 0.8698). Electronegativity, Cosmo volume, total energy, and molecular weight are responsible for the increase and decrease of toxicity of benzene derivatives against S. obliquus while electronegativity, electrophilicity index, the heat of formation, total energy, hydrophobicity, and cosmo volume are responsible for modulation of toxicity of NB and benzene derivatives toward T. pyriformis. These models fulfill the requirements of all the five OECD principles.

Exposure to high-performance benzotriazole ultraviolet stabilizers: Advance in toxicological effects, environmental behaviors and remediation mechanism using in-silica methods

Benzotriazole ultraviolet stabilizers (BUVSs), as light stabilizers, have attracted widespread attention because of their easy migration in the environment and their acute toxicity and biological toxicity effects, such as immunotoxicity and hepatotoxicity. Accordingly, the treatment and remediation mechanisms of high-performance, environmentally friendly, and low human health risk BUVS substitutes were analyzed. Firstly, the weights and the comprehensive effect (CE) values of migration and toxicity of BUVSs were determined by Topsis assisted by the coefficient of variation (CV) method. From this, a three-dimensional quantitative structure activity relationship (3D-QSAR) model based on the CE values of the 13 BUVSs was constructed. Secondly, EPI software was used to predict the functionality and environmental friendliness of BUVS substitutes, and a partial least squares regression machine learning (ML-PLSR) model was used to analyze the mechanism. Then, ADMET (absorption, distribution, metabolism, excretion, toxicity), TOPKAT, and exposure dose models were used to evaluate the ecological and human health risks of BUVSs and their substitutes. Finally, the key charge information affecting the UV-326 substitutes was deduced by time dependent density functional theory (TDDFT). Using UV-326 as an example, 15 UV-326 substitutes with reduced CE values were designed (reductions of 2.61%-23.18%). Compared with ML-PLSR models of acute toxicity, immunotoxicity, and hepatotoxicity, it was found that the decrease of DM and Q_yy values and the increase of Q_zz value could further decrease the toxicity of the UV-326 substitutes. Ecological and human health risk assessment showed that the exposure risks of the six UV-326 substitutes were within acceptable limits. TDDFT showed that the change of electron distribution and electron excitation type were the key factors affecting the performance of the UV-326 substitutes, and a charge transfer excitation type was more conducive to obtaining high-performance, environmentally friendly UV-326 substitutes. This study aims to alleviate the toxic damage to the ecological environment and human health caused by BUVS exposure.

3D-QSAR, molecular docking, DFT and ADMET studies on quinazoline derivatives to explore novel DHFR inhibitors

Resistance to folate antagonists is caused by mutations in the dihydrofolate reductase (DHFR) genes. These mutations affect the amino acids at positions 51, 59, 108 and 164 of DHFR, which appear to play a major role in malaria treatment failure. Therefore, the design of new drugs able to overcome the problem of antifolate drug resistance should receive urgent attention. In this study, a three-dimensional quantitative structure-activity relationship (3 D-QSAR) and molecular docking studies have been performed on antimalarial quinazoline derivatives. The CoMFA (Q² = 0.63, R² = 0.83 and Rpred2 = 0.70) and the CoMSIA (Q² = 0.584, R² = 0.816, and Rpred2= 0.73) models show a good prediction of antimalarial activity. The reliability and robustness of the proposed models have been tested using several validation methods, which showed that the steric, electrostatic, hydrophobic and H-bond acceptor fields of the CoMSIA model play a key role in the prediction of antimalarial activity. Molecular docking studies reveal important interactions between two isomeric compounds (meta and para) and the DHFR receptor in its wild and mutant forms. The obtained outcomes of molecular docking studies have been validated using a new method based on visual inspection. The DFT study of the two isomeric compounds confirms clearly the trends of 3 D-QSAR and molecular docking for the design of new compounds. Moreover, the consistency between theoretical, 3 D-QSAR and molecular docking analysis provides guidance for the design of new drug candidates, which have been tested using ADMET properties and drug likeness analysis.Communicated by Ramaswamy H. Sarma.

3D,2D-QSAR study and docking of novel quinazolines as potential target drugs for osteosarcoma

Background: Quinazolines are an important class of benzopyrimidine heterocyclic compounds with a promising antitumor activity that can be used for the design and development of osteosarcoma target compounds. Objective: To predict the compound activity of quinazoline compounds by constructing 2D- and 3D-QSAR models, and to design new compounds according to the main influencing factors of compound activity in the two models. Methods: First, heuristic method and GEP (gene expression programming) algorithm were used to construct linear and non-linear 2D-QSAR models. Then a 3D-QSAR model was constructed using CoMSIA method in SYBYL software package. Finally, new compounds were designed according to molecular descriptors of 2D-QSAR model and contour maps of 3D-QSAR model. Several compounds with optimal activity were used for docking experiments with osteosarcoma related targets (FGFR4). Results: The non-linear model constructed by GEP algorithm was more stable and predictive than the linear model constructed by heuristic method. A 3D-QSAR model with high Q² (0.63) and R ² (0.987) values and low error values (0.05) was obtained in this study. The success of the model fully passed the external validation formula, proving that the model is very stable and has strong predictive power. 200 quinazoline derivatives were designed according to molecular descriptors and contour maps, and docking experiments were carried out for the most active compounds. Compound 19g.10 has the best compound activity with good target binding capability. Conclusion: To sum up, the two novel QSAR models constructed were very reliable. The combination of descriptors in 2D-QSAR with COMSIA contour maps provides new design ideas for future compound design in osteosarcoma.

Structure- and ligand-based drug design methods for the modeling of antimalarial agents: a review of updates from 2012 onwards

Malaria still persists as one of the deadliest infectious disease having a huge morbidity and mortality affecting the higher population of the world. Structure and ligand-based drug design methods like molecular docking and MD simulations, pharmacophore modeling, QSAR and virtual screening are widely used to perceive the accordant correlation between the antimalarial activity and property of the compounds to design novel dominant and discriminant molecules. These modeling methods will speed-up antimalarial drug discovery, selection of better drug candidates for synthesis and to achieve potent and safer drugs. In this work, we have extensively reviewed the literature pertaining to the use and applications of various ligand and structure-based computational methods for the design of antimalarial agents. Different classes of molecules are discussed along with their target interactions pattern, which is responsible for antimalarial activity. Communicated by Ramaswamy H. Sarma.

Efficient Antibacterial Dimeric Nitro Imidazolium Type of Ionic Liquids from a Simple Synthetic Approach

Synthesis of dimeric nitro-substituted imidazolium salts under the conventional/solvent-free method is reported. The solvent-free method is more important than the conventional one because of its shorter reaction time, higher yield from easily available starting material, environmental safety, and so forth. Counter anion exchange is carried out using inorganic salt, which is dissolved in deionized water at room temperature. In antibacterial studies, dimeric nitro-substituted imidazolium cations with bromide counter anions showed excellent inhibition against E. coli and P. aeruginosa bacteria. These experimental results were further supported by molecular docking studies. All the compounds (3-6) (a-d) showed excellent antibacterial activity than the standard drugs (gentamycin, nalidixic acid, oflaxacin, ciproflaxacin, and amikacin). Molecular docking studies showed strong hydrogen bonding, polar and hydrophobic interactions between the dimeric imidazolium salts, and Escherichia coli/Pseudomonas aeruginosa/Proteus vulgaris/Staphylococcus aureus receptors.

A new perspective to improve the treatment of Lianhuaqingwen on COVID-19 and prevent the environmental health risk of medication

Lianhuaqingwen (LH), one traditional Chinese medicine (TCM), has been used to treat the coronavirus disease 2019 (COVID-19), but its ecotoxicity with potential human health security has not been well investigated. To overcome such adverse effects and improve its medication efficacy, an intelligent multi-method integrated dietary scheme, screening, and performance evaluation approach was developed. Thirteen LH compounds were selected, and the main protease (Mpro) was used as the potential drug target. Resulted information showed that the more compounds of LH added, the higher medication efficacy obtained using multi-method integrated screening system, expert consultation method, and molecular dynamics simulation. Pharmacodynamic mechanism analysis showed that low total energy and polar surface area of LH active compound (i.e., β-sitosterol) will contribute to the best therapeutic effect on COVID-19 using quantitative structure-activity relationships (QSAR) and sensitivity models. Additionally, when mild COVID-19 patients take LH with the optimum dietary scheme (i.e., β-lactoglobulin, α-lactalbumin, vitamin A, vitamin B, vitamin C, carotene, and vitamin E), the medication efficacy were significantly improved (23.58%). Pharmacokinetics and toxicokinetics results showed that LH had certain human health risks and ecotoxicity. This study revealed the multi-compound interaction mechanism of LH treatment on COVID-19, and provided theoretical guidance for improving therapeutic effect, evaluating TCM safety, and preventing human health risk.

Evaluation of flavonoids as potential inhibitors of the SARS-CoV-2 main protease and spike RBD: Molecular docking, ADMET evaluation and molecular dynamics simulations

The 3CLpro main protease and the RDB spike (s) protein of SARS-CoV-2 are critical targets in the treatment of coronavirus 19 disease (COVID-19), as they are responsible for the COVID-19 replication and infection. With this in mind, Molecular docking of 26 natural compounds belonging to the flavonoid family with the 3CLpro and RBD sites of SARS-CoV-2 has been performed. The docking results revealed that the ligands Silibinin, Tomentin A, Tomentin B, 4′-O-methyldiplacone, Hesperidin Amentoflavone and Bilobetin act as a potential inhibitor of SARS-CoV-2 3CLpro, and that the ligands Herbacetin, Morin, Silibinin, Tomentin E, Amentoflavone, Bilobetin, Baicalein and Quercetin can be potential inhibitors of SARS-CoV-2 RBD. It has been noticed that three ligands can inhibit both sites of SARS-CoV-2, indicating a great potential of these compounds to combat COVID-19. Moreover, molecular docking has been validated by a new validation method based on visual inspiration. Evaluation of ADMET pharmacokinetic properties and the drug likeness in silico revealed that six compounds could be effective drugs against COVID-19. Finally, the docking results were verified by molecular dynamics simulations and MM-GBSA calculation to confirm the stability of hydrogen bonding interactions with crucial residues, which are essential to overcome SARS-CoV-2. These results could direct researchers toward plant-derived compounds that could be further investigated as therapeutic targets against COVID-19 replication and infection.

Bioactive Aurones, Indanones, and Other Hemiindigoid Scaffolds: Medicinal Chemistry and Photopharmacology Perspectives

Hemiindigoids comprise a range of natural and synthetic scaffolds that share the same aromatic hydrocarbon backbone as well as promising biological and optical properties. The encouraging therapeutic potential of these scaffolds has been unraveled by many studies over the past years and uncovered representants with inspiring pharmacophoric features such as the acetylcholinesterase inhibitor donezepil and the tubulin polymerization inhibitor indanocine. In this review, we summarize the last advances in the medicinal potential of hemiindigoids, with a special attention to molecular design, structure-activity relationship, ligand-target interactions, and mechanistic explanations covering their effects. As their strong fluorogenic potential and photoswitch behavior recently started to be highlighted and explored in biology, giving rise to the development of novel fluorescent probes and photopharmacological agents, we also discuss these properties in a medicinal chemistry perspective.

Virtual screening and molecular dynamic simulations of the antimalarial derivatives of 2-anilino 4-amino substituted quinazolines docked against a Pf-DHODH protein target

Background

The processes of drug development and validation are too expensive to be subjected to experimental trial and errors. Hence, the use of the insilico approach becomes imperative. To this effect, the drug-likeness and pharmacokinetic properties of the ten (10) previously designed derivatives of 2-anilino 4-amino substituted quinazolines were carried out. Their predicted ligand binding interactions were also carried out by docking them against the Plasmodium falciparum dihydroorotate dehydrogenase (Pf-DHODH) protein target, and the stability of the complex was determined through dynamic simulations. The drug-likeness and pharmacokinetic characteristics were estimated using the online SwissADME software, while the Molegro Virtual Docker (MVD) software was used for molecular docking. And the dynamic simulation was performed for the duration of 100 ns to verify the stability of the docked complex, with the aid of a Schrödinger program, Desmond.

Results

The designed derivatives were all found to pass the Lipinski test of drug likeness, while the pharmacokinetic studies result that the skin permeability and molar refractivity values of the derivatives are both within the limits. In addition, except for derivative C-01, most of the derivatives have strong gastrointestinal absorptions and lack Pgp substrate. Furthermore, no derivative inhibited CYP1A2, CYP2C9, or CYP2C19. The docking studies show the better binding affinities between the ligands and Pf-DHODH than those between the atovaquone or chloroquine standards. The derivative C-02, {5-((6,7-dimethoxy-4-((3-nitrobenzyl)amino)quinazolin-2-yl)amino)-2-fluorobenzaldehyde} was found to be the most stable derivative, with a re-rank docking score of - 173.528 kcal/mol and interaction energy of - 225.112 kcal/mol. The dynamic simulation analysis shows that the derivative C-02 forms a stable complex with the protein target over the simulation time.

Conclusions

The ability of these ligands to form hydrogen bonds, as well as various other interactions, was cited as a factor responsible for their better binding affinity. These findings could aid further the development of enhanced antimalarial drugs.

Multi-Step In Silico Discovery of Natural Drugs against COVID-19 Targeting Main Protease

In continuation of our antecedent work against COVID-19, three natural compounds, namely, Luteoside C (130), Kahalalide E (184), and Streptovaricin B (278) were determined as the most promising SARS-CoV-2 main protease (Mpro) inhibitors among 310 naturally originated antiviral compounds. This was performed via a multi-step in silico method. At first, a molecular structure similarity study was done with PRD_002214, the co-crystallized ligand of Mpro (PDB ID: 6LU7), and favored thirty compounds. Subsequently, the fingerprint study performed with respect to PRD_002214 resulted in the election of sixteen compounds (7, 128, 130, 156, 157, 158, 180, 184, 203, 204, 210, 237, 264, 276, 277, and 278). Then, results of molecular docking versus Mpro PDB ID: 6LU7 favored eight compounds (128, 130, 156, 180, 184, 203, 204, and 278) based on their binding affinities. Then, in silico toxicity studies were performed for the promising compounds and revealed that all of them have good toxicity profiles. Finally, molecular dynamic (MD) simulation experiments were carried out for compounds 130, 184, and 278, which exhibited the best binding modes against Mpro. MD tests revealed that luteoside C (130) has the greatest potential to inhibit SARS-CoV-2 main protease.

Targeting SARS-CoV-2 non-structural protein 13 via helicase-inhibitor-repurposing and non-structural protein 16 through pharmacophore-based screening

Novel drug compound hunting was carried out for SARS-CoV-2 proteins with low mutation susceptibility. The probability of escape mutation and drug resistance is lower if conserved microbial proteins are targeted by therapeutic drugs. Mutation rate of all SARS-CoV-2 proteins were analyzed via multiple sequence alignment Non-Structural Protein 13 and Non-Structural Protein 16 were selected for the current study due to low mutation rate among viral strains and significant functionality. Cross-species mutation rate analysis for NSP13 and NSP16 showed these are well-conserved proteins among four coronaviral species. Viral helicase inhibitors, identified using literature-mining, were docked against NSP13. Pharmacophore-based screening of 11,375 natural compounds was conducted for NSP16. Stabilities of top compounds inside human body were confirmed via molecular dynamic simulation. ADME properties and LD₅₀ values of the helicase inhibitors and Ambinter natural compounds were analyzed. Compounds against NSP13 showed binding affinities between −10 and −5.9 kcal/mol whereby ivermectin and scutellarein showed highest binding energies of −10 and −9.9 kcal/mol. Docking of 18 hit compounds against NSP16 yielded binding affinities between −8.9 and −4.1 kcal/mol. Hamamelitannin and deacyltunicamycin were the top compounds with binding affinities of −8.9 kcal/mol and −8.4 kcal/mol. The top compounds showed stable ligand–protein interactions in molecular dynamics simulation. The analyses revealed two hit compounds against each targeted protein displaying stable behavior, high binding affinity and molecular interactions. Conversion of these compounds into drugs after in vitro experimentation can become better treatment options to elevate COVID management.

Virtual screening of PEBP1 inhibitors by combining 2D/3D-QSAR analysis, hologram QSAR, homology modeling, molecular docking analysis, and molecular dynamic simulations

Human phosphatidylethanolamine binding protein 1 (hPEBP1) is a novel target affecting many cellular signaling pathways involved in the formation of metastases. It can be used in the treatment of many cases of cancer. For these reasons, pharmaceutical companies use computational approaches, including multi-QSAR (2D, 3D, and hologram QSAR) analysis, homology modeling, molecular docking analysis, and molecular dynamic simulations, to speed up the drug discovery process. In this paper, QSAR modeling was conducted using two quantum chemistry optimization methods (AM1 and DFT levels). As per PLS results, we found that the DFT/B3LYP method presents high predictability according to 2D-QSAR, CoMFA, CoMSIA, and hologram QSAR studies, with Q² of 0.81, 0.67, 0.79, and 0.67, and external power with R²_pred of 0.78, 0.58, 0.66, and 0.56, respectively. This result has been validated by CoMFA/CoMSIA graphics, which suggests that electrostatic fields combined with hydrogen bond donor/acceptor fields are beneficial to the antiproliferative activity. While the hologram QSAR models show the contributions of each fragment in improving the activity. The results from QSAR analyses revealed that ursolic acids with heterocyclic rings could improve the activities. Ramachandran plot validated the modeled PEBP1 protein. Molecular docking and MD simulations revealed that the hydrophobic and hydrogen bond interactions are dominant in the PEBP1's pocket. These results were used to predict in silico structures of three new compounds with potential anticancer activity. Similar molecular docking stability studies and molecular dynamics simulations were conducted.

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.

In Silico Screening of Semi-Synthesized Compounds as Potential Inhibitors for SARS-CoV-2 Papain-like Protease: Pharmacophoric Features, Molecular Docking, ADMET, Toxicity and DFT Studies

Papain-like protease is an essential enzyme in the proteolytic processing required for the replication of SARS-CoV-2. Accordingly, such an enzyme is an important target for the development of anti-SARS-CoV-2 agents which may reduce the mortality associated with outbreaks of SARS-CoV-2. A set of 69 semi-synthesized molecules that exhibited the structural features of SARS-CoV-2 papain-like protease inhibitors (PLPI) were docked against the coronavirus papain-like protease (PLpro) enzyme (PDB ID: (4OW0). Docking studies showed that derivatives 34 and 58 were better than the co-crystallized ligand while derivatives 17, 28, 31, 40, 41, 43, 47, 54, and 65 exhibited good binding modes and binding free energies. The pharmacokinetic profiling study was conducted according to the four principles of the Lipinski rules and excluded derivative 31. Furthermore, ADMET and toxicity studies showed that derivatives 28, 34, and 47 have the potential to be drugs and have been demonstrated as safe when assessed via seven toxicity models. Finally, comparing the molecular orbital energies and the molecular electrostatic potential maps of 28, 34, and 47 against the co-crystallized ligand in a DFT study indicated that 28 is the most promising candidate to interact with the target receptor (PLpro).

Virtual screening, pharmacokinetics, and molecular dynamics simulations studies to identify potent approved drugs for Chlamydia trachomatis treatment

Background

The most frequent bacterial sexually transmitted disease is Chlamydia trachomatis (STD). In 2010, the Centers for Disease Control and Prevention (CDC) received 1.3 million reports of cases (CDC). Human chlamydial infections are linked to a variety of clinical symptoms. Inclusion (IncA) membranes are a promising drug target for the treatment of Chlamydia trachomatis. In the present study, molecular docking, ADMET, golden triangle, and molecular dynamics (MD) simulation studies were performed on a series of salicylidene acylhydrazides derivatives against Chlamydia trachomatis. Three types of docking software with different algorithms were used to screen the potential candidate against Chlamydia trachomatis.

Results

The results obtained from the docking analysis succeeded in screening nine novel hit compounds with high affinity to IncA membranes. Then, pharmacokinetics properties were calculated to spot out the drug-likeness of the selected compounds. Also, golden triangles were performed on the selected compounds. Compounds outside the golden triangle indicate that they would have clearance problems. Out of the nine novel hits drugs, four compounds pass the golden triangle screening and virtually all the quality assurance tests proposed by the model and were used for further analysis. One-ns molecular dynamics simulations on the docked complex of compound 44 (one of the highly active selected compounds of the dataset) aided in the further exploration of the binding interactions. Some crucial residues such as Ser111, Gln114, Asn107, Leu142, Gly144, Gln143, Lys104, Tyr149, Phe108, Phe145, and Arg146 were identified. Conventional and carbon–hydrogen bond interactions with amino residues Arg146, Asn107, Phe145, and Ser111 were critical for the binding of inclusion (IncA) membranes inhibitors.

Conclusion

Outcomes of the study can further be exploited to develop potent inclusion (IncA) membranes inhibitors.

Computer-aided molecular design of 2-anilino 4-amino substituted quinazolines derivatives as malarial inhibitors

Quantitative structure–activity relationship studies conducted on forty-five (45) derivatives of 2-anilino 4-amino substituted quinazolines as malaria inhibitors to determine the structures responsible for their antimalarial properties and design novel derivatives with improved activities. The molecular descriptors generated were selected to develop the theoretical model using the genetic approximation component of the material studio. The developed model found to be a function of ATSC8c, GATS8i, SpMin1_Bhi, JGI10, and TDB6u descriptors, shows excellent statistical parameters (R2 = 0.7913, R2adj = 0.7553, Q2cv = 0.7112, LOF = 0.2125, and R2pred = 0.7650). The mean effect (MF) analysis revealed the descriptor SpMin1_Bhi, as the most influential by its largest percentage contribution (54%) to the developed model. The descriptor decodes the information on the first ionization potentials and was found to have positive MF. Hence, activity increases with increases the descriptor value. Structural modifications of the template (compound 13; pEC50 = 7.387) using electron-withdrawing groups increases the descriptor value (first ionization potentials) of the template, which by extension increases the antimalarial activity lead to the design of ten (10) novel theoretical derivatives with improve antimalarial activities. Compound 3, N4-(3-bromo-5-fluorobenzyl)-N2-(4-fluorophenyl)-6,7-dimethoxyquinazoline-2,4-diamine was found to have the highest antimalarial activities among all the designed derivatives (pEC50 = 8.0515).

Pharmacokinetic predictions and docking studies of substituted aryl amine-based triazolopyrimidine designed inhibitors of Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH)

Background

The sixteen (16) designed data set of substituted aryl amine-based triazolopyrimidine were docked against Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) employing Molegro Virtual Docker (MVD) software and their pharmacokinetic property determined through SwissADME predictor.

Results

The docking studies shows compound D16, 5-((6-methoxy-5-methyl-[1,2,4]triazolo[1,5-a]pyrimidin-7-yl)amino)benzo[b]thiophen-4-ol to be the most interactive and stable derivative (re-rank score = − 114.205 kcal/mol) resulting from the hydrophobic as well as hydrogen interactions. The hydrogen interaction produced one hydrogen bond with the active residues LEU359 (H∙∙H∙∙O) at a bond distances of 2.2874 Å. All the designed derivatives were found to pass the Lipinski rule of five tests, supporting the drug-likeliness of the designed compounds.

Conclusion

The ADME analysis revealed a perfect concurrence with the Lipinski Ro5, where the derivatives were found to possess good pharmacokinetic properties such as molar refractivity (MR), number of rotatable bonds (nRotb), log of skin permeability (log Kp), blood-brain barrier (BBB). These results could a deciding factor for the optimization of novel antimalarial compounds.

2D-quantitative structure-activity relationships model using PLS method for anti-malarial activities of anti-haemozoin compounds

Background

Emergence of cross-resistance to current anti-malarial drugs has led to an urgent need for identification of potential compounds with novel modes of action and anti-malarial activity against the resistant strains. One of the most promising therapeutic targets of anti-malarial agents related to food vacuole of malaria parasite is haemozoin, a product formed by the parasite through haemoglobin degradation.

Methods

With this in mind, this study developed two-dimensional-quantitative structure–activity relationships (QSAR) models of a series of 21 haemozoin inhibitors to explore the useful physicochemical parameters of the active compounds for estimation of anti-malarial activities. The 2D-QSAR model with good statistical quality using partial least square method was generated after removing the outliers.

Results

Five two-dimensional descriptors of the training set were selected: atom count (a_ICM); adjacency and distance matrix descriptor (GCUT_SLOGP_2: the third GCUT descriptor using atomic contribution to logP); average total charge sum (h_pavgQ) in pKa prediction (pH = 7); a very low negative partial charge, including aromatic carbons which have a heteroatom-substitution in “ortho” position (PEOE_VSA-0) and molecular descriptor (rsynth: estimating the synthesizability of molecules as the fraction of heavy atoms that can be traced back to starting material fragments resulting from retrosynthetic rules), respectively. The model suggests that the anti-malarial activity of haemozoin inhibitors increases with molecules that have higher average total charge sum in pKa prediction (pH = 7). QSAR model also highlights that the descriptor using atomic contribution to logP or the distance matrix descriptor (GCUT_SLOGP_2), and structural component of the molecules, including topological descriptors does make for better anti-malarial activity.

Conclusions

The model is capable of predicting the anti-malarial activities of anti-haemozoin compounds. In addition, the selected molecular descriptors in this QSAR model are helpful in designing more efficient compounds against the P. falciparum 3D7A strain.

Compounds Identified from Marine Mangrove Plant (Avicennia alba) as Potential Antiviral Drug Candidates Against WDSV, an In-Silico Approach

Walleye dermal sarcoma virus (WDSV) is a type of retrovirus, which affects most of the adult walleye fishes during the spawning time. The virus causes multiple epithelial tumors on the fish’s skin and fins that are liable for more than 50% of the mortality rate of fish around the world. Till now, no effective antiviral drug or vaccine candidates have been developed that can block the progression of the disease caused by the pathogen. It was found that the 582-amino-acid (aa) residues long internal structural gag polyprotein of the virus plays an important role in virus budding and virion maturation outside of the cell. Inhibition of the protein can block the budding and virion maturation process and can be developed as an antiviral drug candidate against the virus. Therefore, the study aimed to identify potential natural antiviral drug candidates from the tropical mangrove marine plant Avicennia alba, which will be able to block the budding and virion maturation process by inhibiting the activity of the gag protein of the virus. Initially, a homology modeling approach was applied to identify the 3D structure, followed by refinement and validation of the protein. The refined protein structures were then utilized for molecular docking simulation. Eleven phytochemical compounds have been isolated from the marine plant and docked against the virus gag polyprotein. Three compounds, namely Friedlein (CID244297), Phytosterols (CID12303662), and 1-Triacontanol (CID68972) have been selected based on their docking score −8.5 kcal/mol, −8.0 kcal/mol and −7.9 kcal/mol, respectively, and were evaluated through ADME (Absorption, Distribution, Metabolism and Excretion), and toxicity properties. Finally, molecular dynamics (MD) simulation was applied to confirm the binding stability of the protein-ligands complex structure. The ADME and toxicity analysis reveal the efficacy and non-toxic properties of the compounds, where MD simulation confirmed the binding stability of the selected three compounds with the targeted protein. This computational study revealed the virtuous value of the selected three compounds against the targeted gag polyprotein and will be effective and promising antiviral candidates against the pathogen in a significant and worthwhile manner. Although in vitro and in vivo study is required for further evaluation of the compounds against the targeted protein.

Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein

X-linked inhibitor of apoptosis protein (XIAP) is a member of inhibitor of apoptosis protein (IAP) family responsible for neutralizing the caspases-3, caspases-7, and caspases-9. Overexpression of the protein decreased the apoptosis process in the cell and resulting development of cancer. Different types of XIAP antagonists are generally used to repair the defective apoptosis process that can eliminate carcinoma from living bodies. The chemically synthesis compounds discovered till now as XIAP inhibitors exhibiting side effects, which is making difficulties during the treatment of chemotherapy. So, the study has design to identifying new natural compounds that are able to induce apoptosis by freeing up caspases and will be low toxic. To identify natural compound, a structure-based pharmacophore model to the protein active site cavity was generated following by virtual screening, molecular docking and molecular dynamics (MD) simulation. Initially, seven hit compounds were retrieved and based on molecular docking approach four compounds has chosen for further evaluation. To confirm stability of the selected drug candidate to the target protein the MD simulation approach were employed, which confirmed stability of the three compounds. Based on the finding, three newly obtained compounds namely Caucasicoside A (ZINC77257307), Polygalaxanthone III (ZINC247950187), and MCULE-9896837409 (ZINC107434573) may serve as lead compounds to fight against the treatment of XIAP related cancer, although further evaluation through wet lab is necessary to measure the efficacy of the compounds.

Structural, QSAR, machine learning and molecular docking studies of 5-thiophen-2-yl pyrazole derivatives as potent and selective cannabinoid-1 receptor antagonists

We performed a structural study followed by theoretical analysis of the chemical descriptors and biological activity of a series of 5-thiophen-2-yl pyrazole derivatives as potent and selective cannabinoid-1 (CB1) receptor antagonists.

Structure–activity relationship analysis of a series of nonsteroidal analogues as androgen receptor antagonists

Computer-aided drug design technology was used to screen drugs in large-scale and to accelerate the progress of drug design of nonsteroidal compounds deriving from the hybridization of FDA-approved Enzalutamide and Abiraterone.

A computational approach for designing novel SARS-CoV-2 Mpro inhibitors: combined QSAR, molecular docking, and molecular dynamics simulation techniques

The promising compound T₂₁ for treating COVID-19 at the active site of SARS-CoV-2 M^pro.

In-silico Design of Aryl and Aralkyl Amine-Based Triazolopyrimidine Derivatives with Enhanced Activity Against Resistant Plasmodium falciparum

A blend of genetic algorithm with multiple linear regression (GA-MLR) method was utilized in generating a quantitative structure–activity relationship (QSAR) model on the antimalarial activity of aryl and aralkyl amine-based triazolopyrimidine derivatives. The structures of derivatives were optimized using density functional theory (DFT) DFT/B3LYP/6–31 + G* basis set to generate their molecular descriptors, where two (2) predictive models were developed with the aid of these descriptors. The model with an excellent statistical parameters; high coefficient of determination (R2) = 0.8884, cross-validated R2 (Q2cv) = 0.8317 and highest external validated R2 (R2pred) = 0.7019 was selected as the best model. The model generated was validated through internal (leave-one-out (LOO) cross-validation), external test set, and Y-randomization test. These parameters are indicators of robustness, excellent prediction, and validity of the selected model. The most relevant descriptor to the antimalarial activity in the model was found to be GATS6p (Geary autocorrelation—lag 6/weighted by polarizabilities), in the model due to its highest mean effect. The descriptor (GATS6p) was significant in the in-silico design of sixteen (16) derivatives of aryl and aralkyl amine-based triazolopyrimidine adopting compound DSM191 with the highest activity (pEC50 = 7.1805) as the design template. The design compound D8 was found to be the most active compound due to its superior hypothetical activity (pEC50 = 8.9545).