3D-QSAR Models Research Articles

Inactivating Activities and Mechanism of Imidazo[1,2-c]pyrimidin-5(6H)-one Nucleoside Derivatives Incorporating a Sulfonamide Scaffold.

Twenty-eight imidazo[1,2-c]pyrimidin-5(6H)-one nucleoside derivatives incorporating a sulfonamide scaffold with preferable inactivating activities on pepper mild mottle virus (PMMoV) were designed and synthesized. Then, compound B29 with illustrious inactivating activity against PMMoV was received on the basis of the three-dimensional quantitative structure-activity relationship (3D-QSAR) model, with the EC50 of 11.4 μg/mL, which was superior to ningnanmycin (65.8 μg/mL) and template molecule B16 (15.3 μg/mL). Furthermore, (1) transmission electron microscopy (TEM) indicated that B29 could cause severe fracture of virions; (2) microscale thermophoresis (MST) and molecular docking further demonstrated that B29 had faintish binding affinities with PMMoV CPR62A (Kd = 202.84 μM), PMMoV CPL144A (Kd = 141.57 μM), and PMMoV CPR62A,L144A (Kd = 332.06 μM) compared to PMMoV CP (Kd = 4.76 μM); and (3) western blot and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) results of pCB-GFP-PMMoV CPR62A, pCB-GFP-PMMoV CPL144A, and pCB-GFP-PMMoV CPR62A,L144A were consistent with MST and confocal. In brief, the above results indicated that the amino acids at positions 62 and 144 of PMMoV CP might be the key amino acid sites of B29 acted on.

Discerning of isatin-based monoamine oxidase (MAO) inhibitors for neurodegenerative disorders by exploiting 2D, 3D-QSAR modelling and molecular dynamics simulation

Almost a billion people worldwide suffer from neurological disorders, which pose public health challenges. An important enzyme that is well-known for many neurodegenerative illnesses is monoamine oxidase (MAO). Although several promising drugs for the treatment of MAO inhibition have recently been examined, it is still necessary to identify the precise structural requirements for robust efficacy. Atom-based, field-based, and GA-MLR (genetic algorithm multiple linear regression) models were created for this investigation. All of the models have strong statistical (R 2 and Q 2) foundations because of both internal and external validation. Our dataset’s molecule has a higher docking score than safinamide, a well-known and co-crystallized MAO-B inhibitor, as we also noticed. Using the SwissSimilarity platform, we further inquired which of our docked molecules would be the best for screening. We chose ZINC000016952895 as the screen molecule with the best binding docking score (XP score = −13.3613). Finally, the 100 ns for the ZINC000016952895-MAO-B complex in our MD investigations is stable. For compounds that we hit, also anticipate ADME properties. Our research revealed that the successful compound ZINC000016952895 might pave the way for the future development of MAO inhibitors for the treatment of neurological disease. Communicated by Ramaswamy H. Sarma

3D-QSAR, docking and molecular dynamics simulations of novel Pyrazolo-pyridazinone derivatives as covalent inhibitors of FGFR1: a scientific approach for possible anticancer agents

Developing highly potent covalent inhibitors of Fibroblast growth factor receptors 1 (FGFR1) has always been a challenging task. In the current study, various computational techniques, such as 3D-QSAR, covalent docking, fingerprinting analysis, MD simulation followed by MMGB/PBSA, and per-residue energy decomposition analysis were used to explore the binding mechanism of pyrazolo[3,4-d]pyridazinone derivatives to FGFR1. The high q2 and r2 values for the CoMFA and CoMSIA models, suggest that the constructed 3D-QSAR models could reliably predict the bioactivities of FGFR1 inhibitors. The structural requirements revealed by the model’s contour maps were strategically used to computationally create an in-house library of more than 100 new FGFR1 inhibitors using the R-group exploration technique implemented in the SparkTM software. The compounds from the in-house library were also mapped in the 3D-QSAR model that predicts comparable pIC50 values with the experimental values. A comparison between 3D-QSAR generated contours and molecular docking conformation of ligands was performed to reveal the fundamentals to design potent FGFR1 covalent inhibitors. The estimated binding free energies (MMGB/PBSA) for the selected compounds were in agreement with the experimental value ranking of their binding affinities towards FGFR1. Furthermore, per-residue energy decomposition analysis has identified Arg627 and Glu531 to contribute significantly in improved binding affinity of compound W16. During ADME analysis, the majority of in-house library compounds exhibited pharmacokinetic properties superior to those of experimentally produced compounds. These new compounds may help researchers better understand FGFR1 inhibition and lead to the creation of novel, potent FGFR1 inhibitors. Communicated by Ramaswamy H. Sarma

Structure-activity relationships of dihydropyrimidone inhibitors against native and auto-processed human neutrophil elastase

BackgroundHuman neutrophil elastase (HNE) is a key driver of systemic and cardiopulmonary inflammation. Recent studies have established the existence of a pathologically active auto-processed form of HNE with reduced binding affinity against small molecule inhibitors. MethodAutoDock Vina v1.2.0 and Cresset Forge v10 software were used to develop a 3D-QSAR model for a series of 47 DHPI inhibitors. Molecular Dynamics (MD) simulations were carried out using AMBER v18 to study the structure and dynamics of sc (single-chain HNE) and tcHNE (two-chain HNE). MMPBSA binding free energies of the previously reported clinical candidate BAY 85–8501 and the highly active BAY-8040 were calculated with sc and tcHNE. ResultsThe DHPI inhibitors occupy the S1 and S2 subsites of scHNE. The robust 3D-QSAR model showed acceptable predictive and descriptive capability with regression coefficient of r2 = 0.995 and cross-validation regression coefficient q2 = 0.579 for the training set. The key descriptors of shape, hydrophobics and electrostatics were mapped to the inhibitory activity. In auto-processed tcHNE, the S1 subsite undergoes widening and disruption. All the DHPI inhibitors docked with the broadened S1′-S2′ subsites of tcHNE with lower AutoDock binding affinities. The MMPBSA binding free energy of BAY-8040 with tcHNE reduced in comparison with scHNE while the clinical candidate BAY 85–8501 dissociated during MD. Thus, BAY-8040 may have lower inhibitory activity against tcHNE whereas the clinical candidate BAY 85–8501 is likely to be inactive. ConclusionSAR insights gained from this study will aid the future development of inhibitors active against both forms of HNE.

Discovery of Novel Isoxazoline Derivatives Containing Diaryl Ether against Fall Armyworms

With the continuous evolution of insect resistance, it is a tremendous challenge to control the fall armyworm (Spodoptera frugiperda) with traditional insecticides. To solve this pending issue, a series of novel isoxazoline derivatives containing diaryl ether structures were designed and synthesized, and most of the target compounds exhibited excellent insecticidal activity. Based on the three-dimensional quantitative structure-activity relationship (3D-QSAR) model analysis, we further optimized the molecular structure with compound L35 obtained and tested for its activity. Compound L35 (LC50 = 1.69 mg/L) exhibited excellent insecticidal activity against S. frugiperda, which was better than those of commercial fipronil (LC50 = 70.78 mg/L) and indoxacarb (LC50 = 5.37 mg/L). The enzyme-linked immunosorbent assay showed that L35 could upregulate the levels of GABA in insects. In addition, molecular docking and transcriptomic results also indicated that compound L35 may affect the nervous system of S. frugiperda by acting on GABA receptors. Notably, through high-performance liquid chromatography (HPLC), we were able to obtain the two enantiomers of compound L35, and the insecticidal activity test revealed that S-(+)-L35 was 44 times more active than R-(-)-L35 against S. frugiperda. This study established the chemistry basis and mechanistic foundations for the future development of pesticide candidates against fall armyworms.

Design and Prediction of ADME/Tox Properties of Novel Magnolol Derivatives as Anticancer Agents for NSCLC Using 3D-QSAR, Molecular Docking, MOLCAD and MM-GBSA Studies

Introduction: In this work, we used several molecular modeling techniques to design new molecules for the treatment of non-small cell lung cancer (NSCLC). Methods: For this purpose, we applied 3D-QSAR, molecular docking, MOLCAD, ADMET, and MMGBSA studies to a series of 51 natural derivatives of magnolol. Results: The developed models showed excellent statistical results (R² = 0.90; Q² = 0.672; R²pred = 0.86) for CoMFA and (R² = 0.82; Q² = 0.58; R2 pred = 0.78) CoMSIA. The design of eleven new molecules was based on predictions derived from the 3D-QSAR model contour maps, molecular docking and MolCAD analyses. In silico drug-like and ADMET properties studies led to the selection of four new molecules designed as potential agents for NSCLC therapy. Molecular docking and MM-GBSA simulations of proposed structures with EGFR-TKD (PDB code: 1M17) showed that ligands X10 and 30 attained better stability in the 1M17 protein pocket compared to the Erlotinib ligand used as a reference. Conclusion: Incorporating all the molecular modelling techniques used in this work is conducive to the design of new molecules derived from the 3-(4-aminobipyridin-1-yl)methyl structure of magnolol, a candidate for drug design for the treatment of non-small cell lung cancer. Therefore, the molecular structures (X10 and 30) can be proposed as a key to designing new drugs against NSCLC.

Innovative screening for functional improved aromatic amine derivatives: Toxicokinetics, free radical oxidation pathway and carcinogenic adverse outcome pathway

Aromatic amines, one of the most widely used low-cost antioxidants in rubbers, have been regarded as pollutants with human health concerns. To overcome this problem, this study developed a systematic molecular design, screening, and performance evaluation method to design functionally improved, environmentally friendly and synthesizable aromatic amine alternatives for the first time. Nine of 33 designed aromatic amine derivatives have improved antioxidant property (lower bond dissociation energy of N-H), and their environmental and bladder carcinogenicity impacts were evaluated through toxicokinetic model and molecular dynamics simulation. The environmental fate of the designed AAs-11–8, AAs-11–16, and AAs-12–2 after antioxidation (i.e., peroxyl radicals (ROO·), hydroxyl radicals (HO·), superoxide anion radicals (O2·-) and ozonation reaction) was also analyzed. Results showed that the by-products of AAs-11–8 and AAs-12–2 have less toxicity after antioxidation. In addition, human bladder carcinogenicity of the screened alternatives was also evaluated through adverse outcome pathway. The carcinogenic mechanisms were analyzed and verified through amino acid residue distribution characteristics, 3D-QSAR and 2D-QSAR models. AAs-12–2, with high antioxidation property, low environmental impacts and carcinogenicity, was screened as the optimum alternative for 3,5-Dimethylbenzenamine. This study provided theoretical support for designing environmentally friendly and functionally improved aromatic amine alternatives from toxicity evaluation and mechanism analysis.

Design of Potent Inhibitors Targeting the Main Protease of SARS-CoV-2 Using QSAR Modeling, Molecular Docking, and Molecular Dynamics Simulations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a serious global public health threat. The evolving strains of SARS-CoV-2 have reduced the effectiveness of vaccines. Therefore, antiviral drugs against SARS-CoV-2 are urgently needed. The main protease (Mpro) of SARS-CoV-2 is an extremely potent target due to its pivotal role in virus replication and low susceptibility to mutation. In the present study, a quantitative structure-activity relationship (QSAR) study was performed to design new molecules that might have higher inhibitory activity against SARS-CoV-2 Mpro. In this context, a set of 55 dihydrophenanthrene derivatives was used to build two 2D-QSAR models using the Monte Carlo optimization method and the Genetic Algorithm Multi-Linear Regression (GA-MLR) method. From the CORAL QSAR model outputs, the promoters responsible for the increase/decrease in inhibitory activity were extracted and interpreted. The promoters responsible for an increase in activity were added to the lead compound to design new molecules. The GA-MLR QSAR model was used to ensure the inhibitory activity of the designed molecules. For further validation, the designed molecules were subjected to molecular docking analysis and molecular dynamics simulations along with an absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. The results of this study suggest that the newly designed molecules have the potential to be developed as effective drugs against SARS-CoV-2.

Reconnoitering imidazopyridazines as anticancer agents based on virtual modelling approach: quantitative structure activity relationship, molecular docking and molecular dynamics

Cancer is an unimpeded growth of cells leading to metathesis of cancer and eventually spread throughout the body. PIM kinases are the members of the serine threonine kinase playing role in cancer progression, differentiation and proliferation. Till date there is no single drug targeting PIM-1 kinase in the market, that has made itself a target in limelight for the discover of new anticancer agents. The contemporary research focusses on the development of new inhibitors of PIM-1 kinase by application of ligand-based and structure-based perspective of drug discovery namely 3D-QSAR, molecular docking and dynamics. The following study stated the correlation amid structural and biological activity of the compounds employing 3D-QSAR analysis. Three 3D-QSAR models were generated using 33 molecules from which the excellent model stated an encouraging conventional correlation coefficient (r2 ) 0.8651, cross validation coefficient (q2) 0.7609. Furthermore, the predicted correlation coefficient (r2 pred ) 0.6274, respectively. Molecular docking studies revealed that the most active compound 26 resided in the active pocket of PIM-1 kinase establishing hydrogen bond interactions with Asp186 in the DFG motif; similarly, all other molecules were engaged within the active site of the PIM-1 kinase. Moreover, molecular dynamics simulation study stated the stability of the ligand in the active site of PIM-1 kinase protein by developing two hydrogen bonds throughout the trajectory of 100 ns. In nutshell, the output stated the successful application of ligand and structure-based strategy for the development of novel PIM-1 kinase inhibitors as anticancer agents. Communicated by Ramaswamy H. Sarma

A QSAR Study for Antileishmanial 2-Phenyl-2,3-dihydrobenzofurans †.

Leishmaniasis, a parasitic disease that represents a threat to the life of millions of people around the globe, is currently lacking effective treatments. We have previously reported on the antileishmanial activity of a series of synthetic 2-phenyl-2,3-dihydrobenzofurans and some qualitative structure-activity relationships within this set of neolignan analogues. Therefore, in the present study, various quantitative structure-activity relationship (QSAR) models were created to explain and predict the antileishmanial activity of these compounds. Comparing the performance of QSAR models based on molecular descriptors and multiple linear regression, random forest, and support vector regression with models based on 3D molecular structures and their interaction fields (MIFs) with partial least squares regression, it turned out that the latter (i.e., 3D-QSAR models) were clearly superior to the former. MIF analysis for the best-performing and statistically most robust 3D-QSAR model revealed the most important structural features required for antileishmanial activity. Thus, this model can guide decision-making during further development by predicting the activity of potentially new leishmanicidal dihydrobenzofurans before synthesis.

2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma.

Background: Retinoblastoma is currently the most common malignant tumor seen in newborns and children's eyes worldwide, posing a life-threatening hazard. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents used in clinics often lead to drug resistance. Thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumour-growth and could be used to develop novel retinoblastoma-targeted agents. Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models. Method: First, linear and non-linear QSAR models were built using heuristic methods and gene expression programming (GEP). The comparative molecular similarity indices analysis (COMISA) was then used to construct 3D-QSAR models through the SYBYL software. New drugs were designed by changing drug activity factors in both models, and molecular docking experiments were performed. Result: The best linear model created using HM had an R2, S2, and R2cv of 0.82, 0.02, and 0.77, respectively. For the training and test sets, the best non-linear model created using GEP had correlation coefficients of 0.83 and 0.72 with mean errors of 0.02 and 0.04. The 3D model designed using SYBYL passed external validation due to its high Q2 (0.503), R2 (0.805), and F-value (76.52), as well as its low standard error of SEE value (0.172). This demonstrates the model's reliability and excellent predictive ability. Based on the molecular descriptors of the 2D model and the contour plots of the 3D model, we designed 100 new compounds using the best active compound 14 as a template. We performed activity prediction and molecular docking experiments on them, in which compound 14.d performed best regarding combined drug activity and docking ability. Conclusion: The non-linear model created using GEP was more stable and had a more substantial predictive power than the linear model built using the heuristic technique (HM). The compound 14.d designed in this experiment has the potential for anti-retinoblastoma treatment, which provides new design ideas and directions for retinoblastoma-targeted drugs.

Computational modeling of PET imaging agents for vesicular acetylcholine transporter (VAChT) protein binding affinity: application of 2D-QSAR modeling and molecular docking techniques.

The online version contains supplementary material available at 10.1007/s40203-023-00146-4.

3D-QSAR assisted design, synthesis and pharmacological evaluation of novel substituted benzamides as procaspase-3 activators and anticancer agents

Novel substituted benzamides were designed using 3D-QSAR analysis, synthesized and screened using in vitro procaspase-3 kinase activation and MTT assays. For the rational design of the novel compounds, we performed 3D-QSAR study and generated statistically validated CoMFA (q2 = 0.736, r2cv = 0.729) and CoMSIA (q2 = 0.751, r2cv = 0.716) models. Generated contour maps using CoMFA and CoMSIA analysis suggested the requirement of steric bulkiness (aromatic ring) along with small electron-withdrawing groups (-Cl, -diCl, -F) for the design of novel compounds. CoMSIA, HBA, and HBD contour maps also suggested the requirement of functional groups (amide and hydrazide) for hydrogen bonding interactions. A total of 40 novel substituted benzamides were designed, and generated 3D-QSAR models were used for prediction of their activity prior to synthesis. ADMET prediction study was also carried out for the selection of better compounds with no toxicity and better physicochemical parameters for the synthesis. Finally, we synthesised and characterised a total of 10 substituted benzamide derivatives. Synthesized compounds were screened for their anticancer activities, in which compound 14c showed IC50 value of 3.13 µM and 6.01 µM against HT-29 and MDA-MB-231, respectively, compound 7c exhibited excellent anticancer activity with an IC50 value of 3.76 µM and 5/63 µM against HT-29 and MDA-MB-231, respectively. Furthermore, a procaspase-3 kinase activation assay was performed, in which both these compounds (7c, EC50 = 3.12 µM, 12b, EC50 = 2.93 µM and 14C EC50 =2.93) demonstrated activation of enzymatic potency and revealed that the anticancer activity of these compounds may be due to activation of the procaspase-3 enzyme. In vitro screening assays confirmed and validated the computational design of novel benzamide derivatives. Compounds 14c and 7c are our lead compounds for further development as anticancer agents based on the induction of apoptosis through procaspase-3 activation.

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.

Machine learning and traditional QSAR modeling methods: a case study of known PXR activators

Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure–activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R 2) of 0.70 versus an R 2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures. Communicated by Ramaswamy H. Sarma

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

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

3D-QSAR, homology modelling of influenza hemagglutinin receptor (StrainA/WS/1933), molecular dynamics, DFT, and ADMET studies for newly designed inhibitors

Influenza pandemic cases are related to high morbidity and mortality rates due to the genetic variability of the influenza strains. This necessitated the need for the search and discovery of more potential anti-influenza agents to avert future outbreaks. The 3D-QSAR studies were built through comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The results showed CoMFA (Rtrain2 = 0.98, Q2 = 0.58) and CoMSIA (Rtrain2 0.96, Q2 = 0.54) models for reliable activity predictions. Using the worthy information obtained from the field contributors of the 3D-QSAR models and the molecular docking studies of the most active compound 4 (template 4), nine (9) compounds were designed (4a-i) with better potency as compared with a reference drug (arbidol). The dynamic stability of the bound ligand (4d) in the binding site of the modelled protein was further justified through molecular dynamics simulations for up to 100 ns. Moreover, the quantum chemical and drug-likeness parameters of the designed compounds predicted good chemical reactivity and pharmacokinetic profiles respectively. Hence, the outcome of this study recommends the development and bioassay testing of these newly designed compounds through in vitro and in-vivo analysis.

2D-QSAR, molecular docking and MD simulation based virtual screening of the herbal molecules against Alzheimer’s disorder: an approach to predict CNS activity

Acetylcholinesterase (AChE) is one of the key enzyme targets that have been used clinically for the management of Alzheimer’s Disorder (AD). Numerous reports in the literature predict and demonstrate in-vitro, and in-silico anticholinergic activity of herbal molecules, however, majority of them failed to find clinical application. To address these issues, we developed a 2D-QSAR model that could efficiently predict the AChE inhibitory activity of herbal molecules along with predicting their potential to cross the blood-brain-barrier (BBB) to exert their beneficial effects during AD. Virtual screening of the herbal molecules was performed and amentoflavone, asiaticoside, astaxanthin, bahouside, biapigenin, glycyrrhizin, hyperforin, hypericin, and tocopherol were predicted as the most promising herbal molecules for inhibiting AChE. Results were validated through molecular docking, atomistic molecular dynamics simulations and Molecular mechanics-Poisson Boltzmann surface area (MM-PBSA) studies against human AChE (PDB ID: 4EY7). To determine whether or not these molecules can cross BBB to inhibit AChE within the central nervous system (CNS) for being beneficial for the management of AD, we determined a CNS Multi-parameter Optimization (MPO) score, which was found in the range of 1 to 3.76. Overall, the best results were observed for amentoflavone and our results demonstrated a PIC50 value of 7.377 nM, molecular docking score of −11.5 kcal/mol, and CNS MPO score of 3.76. In conclusion, we successfully developed a reliable and efficient 2D-QSAR model and predicted amentoflavone to be the most promising molecule that could inhibit human AChE enzyme within the CNS and could prove beneficial for the management of AD. Communicated by Ramaswamy H. Sarma

3D-QSAR, design, docking and in silico ADME studies of indole-glyoxylamides and indolyl oxoacetamides as potential pancreatic lipase inhibitors

The versatility of indole heterocyclic led to the understanding of their structural requirements to develop new potential derivatives. The indole derivatives estimated to be active against pancreatic lipase have been chosen to develop 3D-QSAR field and atom-based models, validated using the Schrodinger suite. Designing of new agents through QSAR based predictions and performing docking on these compounds helped in defining the binding pattern and pharmacophoric features like π–π stacking interactions, hydrogen bonding, and π-cation interactions with the amino acid residues. The protein-ligand complex displayed good binding energies. In silico ADMET properties have been generated using the Quick-prop module of the Schrodinger suite. The 3D-QSAR model is found to be statistically significant and evaluated using various parameters like R2, R2CV, stability, F-value, P-value, RMSE, Q2, and Pearson-r by PLS factor of 4. The field fractions and contour maps along with their visualizations have helped in inferring the essential nature and type of substituent that should be incorporated for a compound to display potent pancreatic lipase inhibitory activity. These deductions and evaluations of the synthesized compounds through the generation of models could be further utilized for designing new molecules rationally.

Triazine Herbicides Risk Management Strategies on Environmental and Human Health Aspects Using In-Silico Methods

As an effective herbicide, 1, 3, 5-Triazine herbicides (S-THs) are used widely in the pesticide market. However, due to their chemical properties, S-THs severely threaten the environment and human health (e.g., human lung cytotoxicity). In this study, molecular docking, Analytic Hierarchy Process-Technique for Order Preference by Similarity to the Ideal Solution (AHP-TOPSIS), and a three-dimensional quantitative structure-active relationship (3D-QSAR) model were used to design S-TH substitutes with high herbicidal functionality, high microbial degradability, and low human lung cytotoxicity. We discovered a substitute, Derivative-5, with excellent overall performance. Furthermore, Taguchi orthogonal experiments, full factorial design of experiments, and the molecular dynamics method were used to identify three chemicals (namely, the coexistence of aspartic acid, alanine, and glycine) that could promote the degradation of S-THs in maize cropping fields. Finally, density functional theory (DFT), Estimation Programs Interface (EPI), pharmacokinetic, and toxicokinetic methods were used to further verify the high microbial degradability, favorable aquatic environment, and human health friendliness of Derivative 5. This study provided a new direction for further optimizations of novel pesticide chemicals.