Steric Fields Research Articles

3D QSAR - Comparative Molecular Similarity Indices Analysis of 2-Heteroarylquinoline-4-amines as Possible Anti-HIV-1 Agents

Three dimensional quantitative structure activity relationships (3D-QSAR), based upon Comparative Molecular Similarity Indices Analysis (CoMSIA) and partial least square analysis (PLS) with dipole moment and shape descriptors for thirty seven 2-heteroarylquinolines as anti-HIV-1 agents were investigated. The training set and test set consisted of 30 and 7 heteroarylquinoline-4-amines respectively. The combination of steric, donor and acceptor fields in CoMSIA gave the best results with cross-validated and conventional correlation coefficients of 0.55 and 0.92 respectively. The predictive ability of CoMSIA was determined using a test set of seven quinoline-4-amine derivatives giving predictive correlation coefficients of 0.67. These results provided further understanding of the relationship between the structural features of quinoline-4-amines and its activities, which should be applicable to design and find new potential anti-HIV-1 agents.

Computational Integration of TRPV4 Antagonists: 3D QSAR, Molecular Docking, Molecular Dynamics Simulations, ADME/Tox, and Retrosynthesis studies

AbstractTRPV4 antagonists that could be potential drugs for treatment and management of neuropathic pain. This study was conducted to systematically design several amino derivatives of 2,4′‐dimethyl‐[4,5′‐bithiazol]‐2‐yl as antagonists. In silico computational methods, such as 3D quantitative structure‐activity relationship modeling (3D‐QSAR), molecular docking, and pharmacokinetic property assessment (ADMET), were used to discover new compounds with strong antagonistic activity. The best‐performing 3D‐QSAR model was developed using a partial least squares approach and comparative molecular similarity analysis (CoMSIA). The developed model demonstrated an excellent ability to correlate and predict properties (R2 = 0.932, Q2 = 0.620, and SEE = 0.109). It was observed that variations in biological activity were significantly influenced by interactions with steric, electrostatic, and hydrophobic fields. Molecular docking was used to validate the 3D‐QSAR methods and to explain the binding site and interactions between the most active ligands and the receptor. Based on these results, a new series of compounds was predicted. The best‐anchored molecules underwent MD simulation to confirm their dynamic behavior and stability, and they also underwent retrosynthetic analysis to assist in their synthesis.

Unveiling structural determinants for FXR antagonism in 1,3,4-trisubstituted-Pyrazol amide derivatives: A multi-scale in silico modelling approach

Non-alcoholic fatty liver disease (NAFLD) is a growing global health concern due to its potential to progress into severe liver diseases. Targeting the bile acid receptor FXR has emerged as a promising strategy for managing NAFLD. Building upon our previous research on FXR partial agonism, the present study investigates a series of 1,3,4-trisubstituted-pyrazol amide derivatives as FXR antagonists, aiming to delineate the structural features for antagonism. By means of 2D-QSAR (quantitative structure-activity relationships) modelling techniques, we elucidated the key structural elements responsible for the antagonistic properties of these derivatives. We then employed QPhAR, an open-access software, to identify key molecular features within the compounds that enhance their antagonistic activity. Additionally, 3D-QSAR modelling allowed us to analyse the steric and electrostatic fields of aligned 3D structures, further refining our understanding of structure-activity relationships. Subsequent molecular dynamics simulations provided insights into the binding mode interactions between the compounds and FXR, with varying potencies, confirming and complementing the findings from 2D-QSAR, pharmacophore, and 3D-QSAR modelling. Particularly, our study highlighted the significance of hydrophobic interactions in conferring potent antagonism by the 1,3,4-trisubstituted-pyrazol amide derivatives against FXR. Overall, this work underscores the potential of 1,3,4-trisubstituted-pyrazol amides as FXR antagonists for NAFLD treatment. Notably, our reliance on open-access software fosters reproducibility and broadens the accessibility of our findings.

Structure-Activity Relationship of Natural Dihydrochalcones and Chalcones, and Their Respective Oxyalkylated Derivatives as Anti-Saprolegnia Agents.

Saprolegnia sp. is a pathogenic oomycete responsible for severe economic losses in aquaculture. To date, there is no treatment for its control that is effective and does not pose a threat to the environment and human health. In this research, two dihydrochalcones 1 and 2, and three chalcones 3-5, isolated from the resinous plant Adesmia balsamica, as well as their synthesized oxyalkylated derivatives 6-29 already reported and a new synthesized series of oxyalkylchalcones 30-35, were evaluated for their anti-saprolegnia activity and structure-activity relationship as potential control and treatment agents for strains of Saprolegnia parasitica and S. australis. Among the molecules tested, natural 2',4'-dihydroxychalcone (3) and new oxyalkylchalcone 34 were the most potent antisaprolegnia agents against both strains, even with better results than the commercial control bronopol. On the other hand, the structure-activity relationship study indicates that the contributions of steric and electrostatic fields are important to enhance the activity of the compounds, thus the presence of bulky substituents favors the activity.

AI-based 3D-QSAR model of FDA-approved repurposed drugs for inhibiting sclerostin

BACKGROUND: Wnt activation promotes bone formation and prevents bone loss. The Wnt pathway antagonist sclerostin and additional anti-sclerostin antibodies were discovered as a result of the development of the monoclonal antibody romosozumab. These monoclonal antibodies greatly increase the risk of cardiac arrest. Three-dimensional quantitative structure-activity relationships (3D-QSAR) predicts biological activities of ligands based on their three-dimensional features by employing powerful chemometric investigations such as artificial neural networks (ANNs) and partial least squares (PLS). OBJECTIVE: In this study, ligand-receptor interactions were investigated using 3D-QSAR Comparative molecular field analysis (CoMFA). Estimates of steric and electrostatic characteristics in CoMFA are made using Lennard-Jones and Coulomb potentials. METHODS: To identify the conditions necessary for the activity of these molecules, fifty Food and Drug Administration (FDA)-approved medications were chosen for 3D-QSAR investigations and done by CoMFA. For QSAR analysis, there are numerous tools available. This study employed Open 3D-QSAR for analysis due to its simplicity of use and capacity to produce trustworthy results. Four tools were used for the analysis on this platform: Py-MolEdit, Py-ConfSearch, and Py-CoMFA. RESULTS: Maps that were generated were used to determine the screen’s r2 (Coefficient of Multiple Determinations) value and q2 (correlation coefficient). These numbers must be fewer than 1, suggesting a good, trustworthy model. Cross-validated (q2) 0.532 and conventional (r2) correlation values of 0.969 made the CoMFA model statistically significant. The model showed that hydroxamic acid inhibitors are significantly more sensitive to the steric field than the electrostatic field (70%) (30%). This hypothesis states that steric (43.1%), electrostatic (26.4%), and hydrophobic (20.3%) qualities were important in the design of sclerostin inhibitors. CONCLUSION: With 3D-QSAR and CoMFA, statistically meaningful models were constructed to predict ligand inhibitory effects. The test set demonstrated the model’s robustness. This research may aid in the development of more effective sclerostin inhibitors that are synthesised using FDA-approved medications.

Study on β-glucosidase activators by 3D-QSAR, molecular docking and molecular dynamics simulation

To investigate the conformational relationship and mechanism of action of β-glucosidase (BGL) activators, a theoretical study of three-dimensional quantitative structure activity relationship, molecular docking, molecular dynamics (MD) simulations, and binding free energy analysis were performed. The results indicate that comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models had good stability and predictive ability, with r2cv/ r2pred values of 0.540/0.998 and 0.654/0.993, respectively. The derived contour maps of steric, electrostatic, and hydrogen bond donor field further displayed the modified information of these activators. Molecular docking and MD were performed on calcium diascorbate (CaAS) and L-Ascorbyl 2,6-Dipalmitate (L-ADP), which have good activating ability. The study indicates a positive interaction between the activators and the enzyme, and this interaction does not cause irreversible negative effects on the conformation of BGL. The MD results show that the total binding free energies of CaAS and L-ADP with BGL were −19.837 kJ/mol and −37.798 kJ/mol, respectively, and the binding force of the activators with BGL was mainly derived from hydrophobic interaction and hydrogen bonding. This study offers important guidance for the efficient use of BGL and for the research and development of effective activators.

Breaking linear scaling relationships in NRR with hydrogen bonding in confined space: A case study of g-C3N4 covered Ru(001) system

The electrochemical nitrogen reduction reaction (NRR) is a very efficient method for sustainable NH3 production under mild conditions, but it requires effective catalysts to expedite the NRR kinetics. However, the linear scaling relationships between the energies of elementary steps in NRR severely limit the rates of thermally catalysis reactions. Using density functional theory calculations, we demonstrate that these scaling relations can be circumvented by introducing hydrogen bonding and confinement field in g-C3N4/Ru(Rh) catalysis. Specially, the adsorption strength of key intermediate *N2H can be enhanced on the g-C3N4/Ru(Rh) own to the formation of H···N hydrogen bonding, while the adsorption of N2 is weakened due to the steric confinement field of the covered g-C3N4, resulting a significantly decrease of the energy barrier for the potential-limiting step in g-C3N4/Ru(Rh) system compared that in pure Ru(Rh) surface. Moreover, the hydrogen bonding strength between g-C3N4 and *NxHy increases with increasing of H atoms, which lower the energy barrier for the following protonation steps. Our results provide guidance for optimizing catalysts for application with hydrogen bonding and steric confinement.

Design, Synthesis, Antifungal Activity, and 3D-QASR of Novel Oxime Ether-Containing Coumarin Derivatives as Potential Fungicides.

Structural modification of natural products is an effective approach for improving antifungal activity and has, therefore, been used extensively in the development of new agrochemical products. In this work, a series of novel coumarin derivatives containing oxime ether structures were designed, synthesized, and evaluated for antifungal activity. Some of the designed compounds exhibited promising antifungal activities against tested fungi, and compounds 4a, 4c, 5a, and 6b had EC50 values equivalent to those of commercial fungicides. Compound 6b was the most promising candidate fungicide against Rhizoctonia solani (EC50 = 0.46 μg/mL). In vivo antifungal bioassays suggested that compounds 5a and 6b could serve as novel agricultural antifungals. Furthermore, microscopy demonstrated that compound 6b induced the sprawling growth of hyphae, distorted the outline of cell walls, and reduced mitochondrial numbers. Additionally, the effects of the substituent steric, electrostatic, hydrophobic, and hydrogen-bond fields were elucidated using an accurate and reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) model. The results presented here will guide the discovery of potential novel fungicides for plant disease control in agriculture.

A Potential Fungicide against Pseudoperonospora Cubensis: Design, Synthesis and 3D-QSAR of New Pyrazole-5-Carboxamide Derivatives.

Downy mildew is one of the major fungi causing significant economic losses to crops. The resistance of this fungus to current fungicides is increasing and new fungicides with a unique mode of action are needed. To find a novel pyrazole amide derivative as a potential fungicide. A series of pyrazole-5-carboxamide derivatives containing a diaryl ether were designed and synthesized by the Intermediate derivatization method (IDM). Their fungicidal activities against Pseudoperonospora cubensis (P. cubensis, cucumber downy mildew) were evaluated in the greenhouse. Bioassays indicated that several compounds exhibited excellent fungicidal activity against P. cubensis in vivo. In particular, T24 (EC50 = 0.88 mg L-1) had the highest activity compared with Dimethomorph and Fluazinam and other analogues. The relationship between the activity and the structure of these derivatives was analyzed, and an accurate and reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) model was established to determine that electrostatic and steric fields had important effects on the improvement of fungicidal activity. The novel pyrazole-5-carboxamide derivative T24 can be considered a potential fungicide for P. cubensis control.

Ligand-Based Design of Novel Quinoline Derivatives as Potential Anticancer Agents: An In-Silico Virtual Screening Approach.

In this study, using the Comparative Molecular Field Analysis (CoMFA) approach, the structure-activity relationship of 33 small quinoline-based compounds with biological anti-gastric cancer activity in vitro was analyzed in 3D space. Once the 3D geometric and energy structure of the target chemical library has been optimized and their steric and electrostatic molecular field descriptions computed, the ideal 3D-QSAR model is generated and matched using the Partial Least Squares regression (PLS) algorithm. The accuracy, statistical precision, and predictive power of the developed 3D-QSAR model were confirmed by a range of internal and external validations, which were interpreted by robust correlation coefficients (RTrain2=0.931; Qcv2=0.625; RTest2=0.875). After carefully analyzing the contour maps produced by the trained 3D-QSAR model, it was discovered that certain structural characteristics are beneficial for enhancing the anti-gastric cancer properties of Quinoline derivatives. Based on this information, a total of five new quinoline compounds were developed, with their biological activity improved and their drug-like bioavailability measured using POM calculations. To further explore the potential of these compounds, molecular docking and molecular dynamics simulations were performed in an aqueous environment for 100 nanoseconds, specifically targeting serine/threonine protein kinase. Overall, the new findings of this study can serve as a starting point for further experiments with a view to the identification and design of a potential next-generation drug for target therapy against cancer.

3D-QSAR, ADMET, and molecular docking studies of aztreonam analogs as E. colis inhibitors.

The development of multidrug resistant strains of extended-spectrum β-lactamase-producing Escherichia coli has become a global problem; therefore, the discovery of new antibacterial agents is the only available solution. To improve and propose new compounds with antibacterial activity, the three-dimensional quantitative structure-activity relationship and molecular docking studies were carried out on Aztreonam analogs as E. coli inhibitors in DNA gyrase B. This study's 3D-Quantitative structure-activity relationship model was created using on the Comparative Molecular Field Analysis and the Comparative Molecular Similarity Indices Analysis. Using the Comparative Molecular Field Analysis (Q 2 = 0.73; R 2 = 0.82), excellent predictability was achieved, and the best Comparative Molecular Similarity Indices Analysis model (Q 2 = 0.88; R 2 = 0.9). The generated model's ability to predict outcomes was assessed through external validation using a test set compound and an applicability domain technique. In this study, the steric, electrostatic, and hydrogen bond acceptor fields played a key role in antibacterial activity. The results of the molecular docking revealed that the newly generated compound A6 has the highest binding affinity with DNA gyrase B. It forms 10 hydrogen bonds with amino acid residues of Asn104, Asn274, Asn132, Ser70, Ser237, Thr105, Glu273, and 2 salt bridges with amino acid residues of Ser70 and Glu273 and one pi-pi interacting with Gys271 amino acid residue in the binding site of 5G1, and this result was validated by a new assessment method. We created some novel, highly effective DNA gyrase B inhibitors based on the earlier findings, and the most accurate model predicted their inhibitory actions. The ADMET characteristics and pharmacological similarity of these novel inhibitors were also examined. These findings would be very beneficial in guiding the optimization process for the identification of novel drugs that can address the issue of multiple drug resistance.

Design, 3D-QSAR, molecular docking, ADMET, molecular dynamics and MM-PBSA simulations for new anti-breast cancer agents

Breast cancer is the most frequent form of malignant tumor in women, and represents a major public health problem due to its high mortality rate. Although a multitude of therapeutic options exist for control of this disease, the emergence of resistance to current pharmaceutical treatments underscores the urgency of developing new anti- breast cancer drugs, with a focus on reducing the adverse effects associated with current therapeutic agents. The present study concerns a new series of (23) compounds based on 1,4-quinone and quinoline derivatives to design candidate drugs against breast cancer. For this purpose, integrated computational techniques were applied, including 3D-QSAR, molecular docking and molecular dynamics simulations (MD). CoMFA and CoMSIA were used to build a robust and highly reliable 3D-QSAR models. To validate the model's predictive capabilities, an external validation was carried out. The results of the best model (CoMSIA/SEA) revealed that electrostatic, steric and hydrogen bond acceptor fields had a significant effect on the anti-breast cancer activity of molecules studied. In addition, evaluation of ADMET properties determined whether these newly designed ligands were likely to be selected as drug-candidates. To confirm the binding stability of the selected ligands to aromatase (3S7S) and validate the molecular docking results, molecular dynamics simulations lasting 100 nanoseconds were performed by calculating RMSD, RMSF, RoG, H-bond, SASA and MM-PBSA parameters. As a result, only one designed compound (ligand 5) emerged as the most promising drug candidate for experimental in vitro and in vivo testing, due to its potential inhibition of breast cancer.

Development of 2D and 3D QSAR models of pyrazole derivatives as acetylcholine esterase inhibitors

The drugs that are the most useful in all stages of Alzheimer?s disease (AD) are acetylcholinesterase (AChE) inhibitors. The objectives of this work are to generate various QSAR models for such drugs and to select a robust predictive models from the corresponding models. Studies were then focused on finding a range of pyrazole-like AChE inhibitors by 2D and 3D QSAR analysis. The genetic algorithm-based multiple linear regression (GA-MLR) provided the statistically robust 2D QSAR model that depicted the significance of the molecular volume and the number of multiple bonds along with the presence/ absence of specific atom-centred fragments and topological distance between 2D pharmacophoric features. Furthermore, these results were correlated well with the electrostatic and steric contour maps retrieved from the 3D QSAR (i.e., alignment-dependent molecular field analysis). The 2D QSAR analysis developed a highly statistical and reliable model, which was compared with the mechanistic interpretation of 3D structures and their electrostatic and steric field contributions leading to a predictive 3D QSAR model. The molecule- protein interactions, elicited by molecular docking, corroborated with the field interactions, as revealed by 2D QSAR. Thus, the developed computational models and simulation analyses in the current work provide valuable information for the future design of pyrazole and spiropyrazoline analogs, as potent AChE inhibitors.

In-silico investigations of novel tacrine derivatives potency against Alzheimer's disease

Tacrine was originally used as a palliative treatment for Alzheimer's disease (AD). But early applications soon revealed a number of its side effects on human health. That's why we decided to examine the effectiveness of novel tacrine derivatives known for their potent inhibition of GluN2B-NMDA receptors, to discover the leading candidates for treating AD in terms of safety and molecular stability towards NMDA receptors. As a result, CoMFA and CoMSIA models were generated using 3-dimensional quantitative structure-activity relationships (3D-QSARs) study, indicating that Electrostatic, Hydrophobic, and Steric fields have a vital function in the NMDAR-antagonizing activities. The predictive exactitude of the generated CoMFA model (Q2cv = 0.699, R2 training = 0.980, R2 test = 0.737) and the top model of CoMSIA (Q2cv = 0.646, R2 training = 0.867, R2 test = 0.761) has been successfully tested in both internal and external validations. The pharmacokinetics properties of in-silico ADME-Toxicity confirm the safety of C24 and C27 compounds, which were discovered to be free from any skin allergy toxicity and hepatotoxic effects, and would be able to passively break through the blood-brain barrier and ultimately penetrate the central nervous system with a very good level of absorption (HIA exceeding 95 %). The molecular docking results indicate that non-toxic inhibitors interact specifically with Gln110, Ser132, and Tyr109 amino acid residues as the main active sites of the transport protein encoded as 5EWJ.pdb. At last, the intermolecular mechanisms detected by the examined ligands labeled C24, and C27, revealed excellent levels of molecular stability towards the targeted protein along one hundred nano-seconds of molecular dynamics time.Therefore, C24 and C27 chemical compounds are strongly recommended to treat AD, due to their considerable similarity to drug candidates and high levels of molecular stability.

Interaction mechanism of okra (Abelmoschus esculentus L.) seed protein and flavonoids: Fluorescent and 3D-QSAR studies

The binding capacity of 10 flavonoids with okra seed protein (OSP) was studied by fluorescence spectroscopy. The structure of flavonoids had an obvious impact on binding performance. The binding ability of flavanone was lower than that of flavone, isoflavone and dihydrochalcone. The binding capacity of flavonoid glycoside was superior to that of the corresponding flavonoid aglycone. The binding ability was positively correlated with the number of phenolic hydroxyl groups on the B ring. The steric field and electrostatic field model constructed by 3D-QSAR method could well explain the above interaction behavior. Thermodynamic analysis suggested that the quenching mechanism of OSP caused by flavonoids was static quenching, and the binding-site number was 1. In addition, hydrogen bonding and van der Waals force dominated this interaction. The 3D and synchronous fluorescence spectra showed that there was no significant change in the polarity of the environment around tryptophan and tyrosine residues during binding.

Modelling of novel bornoel analogs as Influenza A Virus inhibitors through genetic function approximation, comparative molecular fields, molecular docking, and ADMET/Pharmacokinetic studies

Influenza A Virus (IAV) is a human respiratory pathogen prone to mutations and genome re-assortment leading to global pandemics. In this study, we applied the molecular modelling strategies such as, two-dimensional (2D), three-dimensional (3D)-quantitative structure–activity relationship (QSAR), and molecular docking simulation on a novel series of borneol compounds as influenza inhibitors. The best developed 2D-QSAR models, MLR (Q2 ​= ​0.8735, R2(train) ​= ​0.9096) and ANN [3-2-1] (Q2 ​= ​0.8987, R2(train) ​= ​0.9171) revealed good and acceptable statistical validation metrics for the inhibitory activity predictions. The 3D-QSAR models were generated using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), which showed CoMFA_S ​+ ​E (Q2 ​= ​0.559, R2(train) ​= ​0.939) and CoMSIA_S ​+ ​E (Q2 ​= ​0.577, R2(train) ​= ​0.941) as the best-observed models in accordance with the model acceptability standards. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrates the steric and electrostatic molecular field relationships with the inhibitory effects of the studied molecules. Moreover, the binding modes of the active ligands were studied through molecular docking simulation with the Human Hemagglutinin (HA) receptor of influenza A virus (A/Puerto Rico/8/34(H1N1)). The studied compounds revealed the formation of H-bonds, CH-bonds, and hydrophobic interactions with the active amino acid residues such as Asn 543, Asn 614, Asn 617, Leu 618, Ser 540, Lys 539, and Lys 621 in the HA binding cavity. The prediction of drug-likeness and ADMET properties of the compounds revealed their good bioavailability and pharmacokinetic profiling. This study may provide a valuable in-silico guideline for discovering novel potent influenza inhibitors.

Computational assessment of the reactivity and pharmaceutical potential of novel triazole derivatives: An approach combining DFT calculations, molecular dynamics simulations, and molecular docking

The worldwide prevalence of cancer and its increasing frequency make it a key research area in drug discovery programs. The current research paper describes the development of QSAR models based on the in vitro against topoisomerase II, which identified the structural origin of anticancer activity for derivatives of triazole moieties linked to mansonone E. The models PLS regression QSARs validated by LOO showed an R2 of 0.92, 0.89 and 0.99 and a Q2 of 0.75, 0.62 and 0.88 for CoMFA, CoMSIA and HQSAR respectively. External validation criteria were used to validate the reliability of the models. These results show the impact of electrostatic and steric fields and of the hydrogen bond donor on the activity of the compounds studied. Based on these results, seven novel inhibitors with high activity were designed, which successfully passed Lipinski's rule of five for oral bioavailability. The evaluations of ADME/Tox parameters and synthetic accessibility for chemical synthesis showed acceptable results. Ligand interactions in binding site protein were assessed using molecular docking. The results show the correct conformational pose of the designed compounds especially the compound T1 where it forms hydrogen and hydrophobic interactions with the main binding site residues. The stability of the complexes was confirmed by the MD study and the calculation of the free binding energy. The T1 synthesis reaction was carried out according to the 1,3 cycloaddition reaction. The study of the local and global reactivity and the energy of activation of this reaction have shown the predicted of the regioselectivity of compound T1. Also we have described the state of transition of two isomers T1,4 and T1.5. Finally, this study would be interesting to help identify and optimize avenues for early discovery of anticancer drugs.

Convergent QSAR Models for the Prediction of Cruzain Inhibitors.

Chagas disease is a parasitosis caused by Trypanosoma cruzi. Cruzain, the major cysteine protease from T. cruzi, is an excellent therapeutic target in the search for antichagasic drugs. It is important in the role of cell invasion, replication, differentiation, and metabolism of the parasite. In this work, we developed and assessed multiple quantitative structure-activity relationship (QSAR) models for a set of 61 cruzain inhibitors. These models include two-dimensional (2D) QSAR, three-dimensional (3D) QSAR, such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and Hologram QSAR (HQSAR). In total, we generated 10 major and 114 minor model variations. Molecular docking was used to successfully align the molecules. All CoMFA and CoMSIA models, which incorporate multiple fields, demonstrated robustness in our analysis. Steric fields exhibited satisfactory convergence in the contour maps, while the electrostatic field converged into a single small region. The HQSAR model taking into consideration only Atoms and Connectivity, with fragment sizes ranging from two to five atoms, was considered the best of the HQSAR variations, despite exhibiting a higher level of deviance. In total, 78 model variations meet the minimum requirements to be considered acceptable. We found that using as few as five descriptors it is possible to obtain robust results with 2D-QSAR. Models such as Random Forest, Tree Ensemble, Linear Regression, and HQSAR are recommended for working with large data sets, while the 3D-QSAR models are intended to study the geometry of the ligands, to optimize them into new and better performing antichagasics. Virtual Screening of a set of hydrazones, guided by the top-performing models, identified promising candidates for experimental validation. Among them, dv007 and dv015 exhibited consistently high predicted pIC50 values (7.26 and 7.24, respectively), making them compelling candidates for further drug development.

Finding structural requirements of structurally diverse α-glucosidase and α-amylase inhibitors through validated and predictive 2D-QSAR and 3D-QSAR analyses

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycemic state. The α-glucosidase and α-amylase are considered two major targets for the management of Type 2 DM due to their ability of metabolizing carbohydrates into simpler sugars. In the current study, cheminformatics analyses were performed to develop validated and predictive models with a dataset of 187 α-glucosidase and α-amylase dual inhibitors. Separate linear, interpretable and statistically robust 2D-QSAR models were constructed with datasets containing the activities of α-glucosidase and α-amylase inhibitors with an aim to explain the crucial structural and physicochemical attributes responsible for higher activity towards these targets. Consequently, some descriptors of the models pointed out the importance of specific structural moieties responsible for the higher activities for these targets and on the other hand, properties such as ionization potential and mass of the compounds as well as number of hydrogen bond donors in molecules were found to be crucial in determining the binding potentials of the dataset compounds. Statistically significant 3D-QSAR models were developed with both α-glucosidase and α-amylase inhibition datapoints to estimate the importance of 3D electrostatic and steric fields for improved potentials towards these two targets. Molecular docking performed with selected compounds with homology model of α-glucosidase and X-ray crystal structure of α-amylase largely supported the interpretations obtained from the cheminformatic analyses. The current investigation should serve as important guidelines for the design of future α-glucosidase and α-amylase inhibitors. Besides, the current investigation is entirely performed by using non-commercial open-access tools to ensure easy accessibility and reproducibility of the investigation which may help researchers throughout the world to work more on drug design and discovery.

Discovery of Novel Diamides Scaffold Containing Monofluoro-acrylamides Activating the Insect Ryanodine Receptor.

The research and development of organofluorine chemistry has flourished; in particular, monofluoroalkene has aroused considerable interest from medicinal and organic chemists. It is a significant attempt to introduce monofluoroalkene into agrochemicals. In this study, monofluoroalkene was introduced into diamide molecules and inserted between the aliphatic amide and benzene ring, and 44 compounds have been successfully synthesized. The bioassay results showed that compounds with monofluoro-acrylamide moiety (Z-isomers) had excellent larvicidal activity against lepidopteran pests at 5 mg·L-1. The LC50 values of compounds B16, B18, and B21 against Mythimna separata were 1.02, 1.32, and 0.78 mg·L-1, respectively. 3D-QSAR analysis including the CoMFA model and the CoMSIA model was conducted to illustrate the contributions of steric, electrostatic, hydrophobic, and hydrogen bond fields on the bioactivity. Moreover, typical symptoms caused by chlorantraniliprole including dehydration, shrinkage, and blackening were also observed on the test larvae treated with monofluoro-acrylamide diamide compounds. M. separata central neurons calcium imaging experiment of compound B18 indicated that the monofluoro-acrylamide diamide compounds were potential insect ryanodine receptor activators. The molecular docking was performed in the CHL binding domain of Plutella xylostella RyR and revealed that the predicted binding mode of compound B21 was slightly different from that of CHL. The MM|GBSA dG Bind values of B21 and CHL with P. xylostella RyR were respectively -85.797 and -95.641 kcal·mol-1. The present work explored the insecticidal properties of a new diamide scaffold containing a monofluoro-acrylamide fragment and extended the application of monofluoroalkene in the agrochemical field.