Atom-based 3D-QSAR Research Articles

A Computational Study of Green Tea Extracts and their Derivatives as Potential Inhibitors for Squalene Monooxygenase.

According to the World Health Organisation, cardiovascular complications have been recognized as the leading course of death between 2000 and 2019. Cardiovascular complications are caused by excess LDL cholesterol in the body or arteries that can build up to form a plaque. There are drugs currently in clinical use called statins that target HMGCoA reductase. However, these drugs result in several side effects. This work investigated using computational approaches to lower cholesterol by investigating green tea extracts as an inhibitors for squalene monooxygenase (the second-rate-controlling step in cholesterol synthesis). Pharmacophore modeling was done to identify possible pharmacophoric sites based on the pIC50 values. The best hypothesis generated by pharmacophore modeling was further validated by atom-based 3D QSAR, where 70% of the data set was treated as the training set. Prior molecular docking ADMET studies were done to investigate the physiochemical properties of these molecules. Glide docking was performed, followed by molecular dynamics to evaluate the protein conformational changes. Pharmacophore results suggest that the best molecules to interact with the biological target should have at least one hydrogen acceptor (A5), two hydrogen donors (D9 and D10), and two benzene rings (R14 and R15) for green tea polyphenols and theasinensin A. ADMET result shows that all molecules in this class have low oral adsorption. Molecular docking results showed that some green tea polyphenols have good binding affinities, with most of these structures having a docking score of less than -10 kcal/mol. Molecular dynamics further illustrated that the best-docked ligands perfectly stay within the active site over a 100 ns simulation. The results obtained from this study suggest that green tea polyphenols have the potential for inhibition of squalene monooxygenase, except for theasinensin A.

A Combination of Pharmacophore Generation, Ligand-based Virtual Screening, Atom-based 3D-QSAR, and Molecular Docking Studies on Febuxostat-based Amides Analogues as Anti-inflammatory Agents.

A defence mechanism of the body includes inflammation. It is a process through which the immune system identifies, rejects, and starts to repair foreign and damaging stimuli. In the world, chronic inflammatory disorders are the leading cause of death. To obtain optimized pharmacophore, previously reported febuxostat- based anti-inflammatory amide derivatives series were subjected to pharmacophore hypothesis, ligand-based virtual screening, and 3D-QSAR studies in the present work using Schrodinger suite 2022-4. QuikProp module of Schrodinger was used for ADMET prediction, and HTVS, SP, and XP protocols of GLIDE modules were used for molecular docking on target protein (PDB ID:3LN1). Utilising 29 compounds, a five-point model of common pharmacophore hypotheses was created, having pIC50 ranging between 5.34 and 4.871. The top pharmacophore hypothesis AHHRR_ 1 model consists of one hydrogen bond acceptor, two hydrophobic groups and two ring substitution features. The hypothesis model AHHRR_1 underwent ligand-based virtual screening using the molecules from Asinex. Additionally, a 3D-QSAR study based on individual atoms was performed to assess their contributions to model development. The top QSAR model was chosen based on the values of R2 (0.9531) and Q2 (0.9424). Finally, four potential hits were obtained by molecular docking based on virtual screening. The virtual screen compounds have shown similar docking interaction with amino acid residues as shown by standard diclofenac sodium drugs. Therefore, the findings in the present study can be explored in the development of potent anti-inflammatory agents.

Multistage in silico approach to identify novel quinoline derivatives as potential c-kit kinase inhibitors

The type II-C-KIT signaling network has been extensively studied for its potential as a target for cancer treatment, leading to the investigation of quinoline derivatives as compounds with inhibitory effects on c-Kit kinase. In this study, a multistage approach was employed, including the creation of pharmacophore models, 3D QSAR analysis, virtual screening, docking investigations, and molecular dynamics stimulation. The pharmacophore evaluation included a data set of 29 ligands, which resulted in the generation of the ADDHR_1pharmacophore model as the most promising, with a survival score of 5.6812. The main objective was to utilize the atom-based 3D-QSAR approach for generating robust 3D-QSAR models aimed at identifying new TypeII-C-kit kinase inhibitors. The evaluations of these models have convincingly demonstrated their high predictive power (Q2 = 0.6547, R2 = 0.9947). Using atom-based 3D-QSAR data, a total of 7564 novel compounds were generated from R-group enumeration. Molecular docking and MM-GBSA study revealed that compound A1 exhibited the highest binding score of −9.30 kcal/mol and a Δ GBind value of −90.56 kcal/mol. The ZINC compounds were then screened using the pharmacophore model, followed by virtual screening, which identified ZINC65798256, ZINC09317958, ZINC73187176, and ZINC76176670 as potential candidates with promising docking scores. Among these, ZINC65798256 demonstrated the best binding interactions with amino acid residues, ASP810, LYS623, CYS673, and THR670 (PDB ID: 1T46). To further analyze the structural features and molecular interactions, molecular dynamics simulation was conducted for a time scale of 100 ns. Communicated by Ramaswamy H. Sarma

A comprehension on structure guided alignment dependent 3D-QSAR modelling, and molecular dynamics simulation on 2,4-thiazolidinediones as aldose reductase inhibitors for the management of diabetic complications

Aldose reductase is an oxo-reductase enzyme belonging to the aldo-keto reductase class. Compounds having thiazolidine-2,4-dione scaffold are reported as potential aldose reductase inhibitors for diabetic complications. The present work uses structure-guided alignment-dependent Gaussian field- and atom-based 3D-QSAR on a dataset of 84 molecules. 3D-QSAR studies on two sets of dataset alignment have been carried out to understand the favourable and unfavourable structural features influencing the affinity of these inhibitors towards the enzyme. Using common pharmacophore hypotheses, the five-point pharmacophores for aldose reductase favourable features were generated. The molecular dynamics simulations (up to 100 ns) were performed for the potent molecule from each alignment set (compounds 24 and 65) compared to reference standard tolrestat and epalrestat to study target-ligand complexes’ binding energy and stability. Compound 65 was most stable with better interactions in the aldose reductase binding pocket than tolrestat. The MM-PBSA study suggests compound 65 possessed better binding energy than reference standard tolrestat, i.e. −87.437 ± 19.728 and −73.424 ± 12.502 kJ/mol, respectively. The generated 3D-QSAR models provide information about structure–activity relationships and ligand-target binding energy. Target-specific stability data from MD simulation would be helpful for rational compound design with better aldose reductase activity. Communicated by Ramaswamy H. Sarma

A computational investigation of thymidylate synthase inhibitors through a combined approach of 3D-QSAR and pharmacophore modelling

Thymidylate synthase (TS) is a crucial target of cancer drug discovery and is mainly involved in the De novo synthesis of the DNA precursor thymine. In the present study, to generate reliable models and identify a few promising molecules, we combined QSAR modelling with the pharmacophore hypothesis-generating technique. Input molecules were clustered on their similarity, and a cluster of 74 molecules with a pyrimidine moiety was chosen as the set for 3D-QSAR and pharmacophore modelling. Atom-based and field-based 3D-QSAR models were generated and statistically validated with R2 > 0.90 and Q2 > 0.75. The common pharmacophore hypothesis(CPH) generation identified the best six-point model ADHRRR. Using these best models, a library of FDA-approved drugs was screened for activity and filtered via molecular docking, ADME profiling, and molecular dynamics simulations. The top ten promising TS-inhibiting candidates were identified, and their chemical features profitable for TS inhibitors were explored. Communicated by Ramaswamy H. Sarma

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

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

Pharmacophore derived 3D-QSAR, molecular docking, and simulation studies of quinoxaline derivatives as ALR2 inhibitors

Aldose Reductase 2 (ALR2), a key enzyme of the polyol pathway, plays a crucial role in the pathogenesis of diabetic complications. Quinoxaline scaffold-based compounds have been identified as potential ALR2 inhibitors for the management of diabetic complications. In the present work, molecular dynamic simulation studies in conjugation with pharmacophore mapping and atom-based 3D-QSAR were performed on a dataset of 99 molecules in comparison with Epalrestat (reference) to mark the desirable structural features of quinoxaline analogs to generate a probable template for designing novel and effective ALR2 inhibitors. The most potent compound 81 was subjected to MD simulation studies and found to be stable, with better interactions with the binding pocket as compared to Epalrestat. The MM-GBSA and MM-PBSA calculations showed that compound 81 possessed binding free energies of −35.96 and −4.92 kcal/mol, respectively. Atom-based 3D-QSAR yielded various pharmacophoric features with excellent statistical measures, such as correlation coefficient (R 2 value), F-value (Fischer ratio), Q 2 value (cross-validated correlation coefficient), and Pearson’s R-value for training and test sets. Furthermore, the pharmacophore mapping provided a five-point hypothesis (AADRR) and docking analysis revealed the active ligand-binding orientations on the active site’s amino acid residues TYR 48, HIE 110, TRP 111, and TRP 219. The results of this study will help in designing potent inhibitors of ALR2 for the management of diabetic complications. Communicated by Ramaswamy H. Sarma

Structure-guided Design and Optimization of small Molecules as Pancreatic Lipase Inhibitors using Pharmacophore, 3D-QSAR, Molecular Docking, and Molecular Dynamics Simulation Studies.

Obesity has now become a global issue due to the increase in the population of obese people. It also substantially impacts the individual's social, financial, and psychological well-being, which may contribute to depression. Being overweight induces many metabolic and chronic disorders, urging many researchers to focus on developing the drug for obesity treatment. Pancreatic lipase inhibitors and natural product/compound-derived pancreatic lipase inhibitors have recently received much attention because of their structural variety and low toxicity. This study aimed to build pharmacophores and QSAR for analyzing the necessary structure of pancreatic lipase inhibitors and designing new molecules with the best activity. Ligand-based pharmacophore modeling and Atom-Based 3D-QSAR were carried out using the PHASE module of Schrodinger to determine the critical structural properties necessary for pancreatic lipase (PL) inhibitory activity. A total of 157 phytoconstituents and a standard drug, orlistat, were selected for the present study. Considering the important features for pancreatic lipase inhibition, 15 new molecules were designed and subjected to molecular docking studies and molecular dynamics simulations. The activity of designed molecules was predicted using the Atom- Based QSAR tool of the PHASE module. The top docked score molecule is structure-7 with a docking score of -6.094 Kcal/mol, whereas the docking score of orlistat and tristin is -3.80Kcal/mol and -5.63Kcal/mol, respectively. The designed molecules have a high docking score and good stability, are in the desirable ADME range and are derived from natural products, so they might be used as lead molecules for anti-obesity drug development.

Structural insights into styrylquinolines analogs as tubulin polymerization inhibitors: In silico Approach

:<strong> </strong>Cancer is one of the fastest-growing epidemics that affect millions yearly. A handful of anticancer drugs are available on the market, but they produce undesirable side effects. Currently, tubulin inhibitors targeting the colchicine binding site are considered an important target due to their structural simplicity and favorable pharmacokinetics with fewer side effects. Different researchers conducted many studies to discover a novel tubulin inhibitor targeting the colchicine binding site with high safety and potency. In the present study, we performed computational analysis of 48 styrylquinolines analogs obtained from literature using different drug designing tools. The pharmacophore mapping study was conducted to identify the important pharmacophoric features essential for biological activity. Atom-based 3D-QSAR (3-dimensional quantitative structure-activity relationship) analysis was carried out to know the contribution of different atoms to model development. The generated model showed a statistically significant coefficient of determinations for the training and test sets. The best QSAR model was selected based on R<sup>2</sup> (0.8624) and Q<sup>2</sup> (0.6707) values. Contour plot analysis of the developed model unveiled the chemical features necessary for tubulin inhibition. A docking study was performed on potent styrylquinoline analog 9VII-f(46), which shows the highest SP docking scores (-5.494). ADME (Absorption, distribution, metabolism, and excretion) analysis provides valuable information about the drugability of newly designed compounds.

Field and atom-based 3D-QSAR models of chromone (1-benzopyran-4-one) derivatives as MAO inhibitors

Monoamine oxidases (MAOs) are flavo-enzymes that aid in the oxidative deamination of neurotransmitters like dopamine, serotonin, and epinephrine. MAO inhibitors are antidepressants that work by preventing the breakdown of brain neurotransmitters and regulating mood. MAO inhibitors that use the chromone (1-benzopyran-4-one) structure have been found to be quite effective in studies. The current study involves the creation of pharmacophore models, 3-D QSAR, virtual screening, and docking investigations, all of which are evaluated using various criteria. The investigation included 39 ligands that emerged pharmacophore AHRRR_1, as the best pharmacophore model with a survival score of 5.6485. The 3D QSAR investigation revealed a significant model with the values of R 2 = 0.9064 and Q 2 = 0.8239. Docking study revealed that compound 18 had the highest docking (−10.402 kcal/mol) score in the series and showed interactions with the essential amino acid TYR398 required for MAO inhibitory activity. ZINC compounds were screened using the created pharmacophore model, which was followed up with a virtual screening study. The ZINC compounds with the best XP docking scores are ZINC03113255, ZINC07777127, ZINC05166353 and ZINC09341502 (with docking scores −10.021, −9.486, −8.031 and −7.792 kcal/mol, respectively). ZINC03113255, which showed the best score, has binding interactions with amino acid residues, TYR326, TYR398 and LYS296 of monoamine oxidase B. The ADME analysis demonstrated the compound’s drug-like characteristics. The findings of this study may be used in the development of chromone compounds that target the MAO inhibitor. Communicated by Ramaswamy H. Sarma

Pharmacophore Modeling, 3D QSAR, Molecular Dynamics Studies and Virtual Screening on Pyrazolopyrimidines as anti-Breast Cancer Agents

Various targets, such as estrogen receptor (ER), mammalian target ofrapamycin(mTOR), epidermal growth factor receptor (EGFR), androgen receptor (AR) andpoly adenosine diphosphate-ribose polymerase (PARP), are focused on the treatment of breast cancer. In this study, in silico tools such as pharmacophore modeling, 3D QSAR study, molecular docking, binding free energy determination and molecular dynamics were executed on a series of pyrazolopyrimidines derivatives. The pharmacophore modeling of forty-one anticancer derivatives was generated, and atom-based 3D QSAR was applied. Molecular docking, dynamics, binding energy and high-throughput virtual screening (HTVS) were conducted by the software Schrodinger. The best five featured pharmacophore hypotheses AHRRR_1 with a maximum survival score of 5.533 was subjected to rigorous scoring function analysis and 3D QSAR studies. Docking studies and binding free energy were carried out on 41 inhibitors for their anti-breast cancer activity against human estrogen, progesterone receptor, EGFR kinase and mammalian target of rapamycin mTOR. Molecular dynamics simulation of the docked complex-34/4WKQ validated the stability of this complex. HTVS was performed to determine the virtual hits with the best-fitted model AHRRR_1. New EGFR kinase inhibitors were designed based on the active compound templates. Molecular docking was carried out on virtual hits, and newly designed compounds and dynamics studies revealed that the binding modes obtained after MD simulation were more or less similar to that acquired post docking mode. Promiscuity assessments demonstrated that designed compounds would be specific rather than a promiscuous one. Based on these findings, we have designed four novel compounds as anti-breast inhibitors, with potent binding affinity and desirable ADME properties.

Molecular modelling investigation on 4-aminoquinoline derivatives as potent anti-tubercular agents

The emergency of new Mycobacterium tuberculosis strains leads to multi-drug resistant tuberculosis (MDR) and total drug resistant tuberculosis (TDR) indicating the urgency in the development of newer anti-tubercular agents to be effective against the resistance strains. In the present study pharmacophore modelling and atom-based 3D-QSAR of 4-Aminoquinoline derivatives was carried out to understand the key structural features responsible for anti-tb activity. Phase module of Schrödinger was used to generate a five-point Pharmacophore for 46 molecules belonging to 4-Aminoquinolines. The best scoring hypothesis (ADHHR_1) had one H-bond Acceptor (A1), one H-bond donor (D5), two Hydrophobic regions (H10 and H11) and one Ring (R14) with a good regression coefficient of 0.9604. The validity of the hypothesis was judged by the cross-validation coefficient (Q2= 0.7885) and Pearson- r (0.9158) followed by the external validation which showed high predictive power. Newer molecules were designed and subjected to molecular docking to explore the alleged binding modes followed by molecular dynamic simulation studies. The molecular modelling study revealed the significance of aromatic ring and hydrogen bond responsible for potent anti-tb activity.

Atom-based 3D-QSAR and DFT analysis of 5‐substituted 2‐acylaminothiazole derivatives as HIV-1 latency-reversing agents

HIV-1 latency consists of viral DNA; integrated inside the host genome; it remains transcriptional silent. Combined Antiretroviral Therapy (cART) and the host immune system fail to recognize the latency cells or reservoirs, representing a major barrier to eradicating the HIV-1 infection. The Shock and Kill emerged as a promising strategy to target these cells using Latency reversal agents (LRAs); partially succeeded in producing viral mRNA but failed to reduce the size of reservoirs. In this Context, 2‐acylaminothiazole class derivatives appeared as promising HIV-1 latency-reversing agents. In this study, we have developed an atom-based 3 D-QSAR model by utilizing the 49 active compounds of the 5‐substituted 2‐acylaminothiazoles derivatives. These compounds are further randomly divided into training (37) and test (12) datasets, yielding statistically significant R2 (0.90) and Q2 (0.85) results, respectively. The internal and external validation of the model shows highly robust and reliable results. Next, the model was visualized to check the favourable and unfavourable groups in terms of hydrogen bond donor, electron-withdrawing and hydrophobic group on the most active compound 96 and least active compound 30. The investigated model reveals the structural insights required for obtaining more leads that are potent. Finally, DFT calculations on the most and least active compounds were performed to support the atom-based 3 D-QSAR model. Overall, this study will aid in understanding the minimum structural requirement and functional group required for screening the novel potent leads as HIV-1 latency reversal agents. Communicated by Ramaswamy H. Sarma

Interaction mechanism of flavonoids and Tartary buckwheat bran protein: A fluorescence spectroscopic and 3D-QSAR study

Tartary buckwheat bran protein (TBBP) is a valuable by-product of Tartary buckwheat processing. Current studies have indicated that TBBP is a desirable carrier and protector of flavonoids. However, the quantitative structure-affinity relationship of flavonoids with TBBP still remains to be elucidated. Here, by using the fluorescence spectroscopy method to measure the binding constants of 16 flavonoids with TBBP, we employ a combination of the atom-based three dimensional-quantitative structure-affinity relationships (3D-QSAR) and quantum-chemical calculation to explore the underlying binding mechanisms. We show that flavonoids can cause the intrinsic fluorescence quenching and form non-covalent complexes with TBBP driven by hydrogen bonding and van der Waal forces. The atom-based 3D-QSAR model reveals that the rutoside group at the C-7 position of flavones is favorable to enhance the binding constants of flavonoids with TBBP. Quantum-chemical calculations consistently disclose that the rutoside group can intensify interaction forces of flavonoids, thereby strengthening the binding. This work provides theoretical evidence that TBBP has the potential application to be raw materials for develop functional food, and be the delivery carriers of flavonoids.

A holistic molecular modelling approach to design novel indole-2-carboxamide derivatives as potential inhibitors of MmpL3

ABSTRACT Tuberculosis is an infectious air-borne disease and one of the leading causes of death globally among all infectious diseases. There is an urgent need to develop antitubercular drugs that would be highly efficient and less toxic than the presently available marketed drugs. Mycobacterium membrane protein large 3 (MmpL3) is an emerging drug target in tuberculosis with various classes of molecules that have been known to inhibit it. In this study, a dataset of indole-2-carboxamides showing antitubercular activity by inhibiting MmpL3 was utilized. Initially, a chimera-based homology model was developed and docking was performed with the filtered dataset to analyse the interactions. Thereafter, molecular dynamics simulations were run with representative molecules to gain a better insight on the binding patterns. To attain a more quantitative correlation, an atom-based 3D QSAR model was developed which complemented the results from the previous models. A library of novel indole-2-carboxamides was then generated using core hopping-based ligand enumeration and upon screening on our workflow model it predicted three molecules as potent antitubercular compounds. This work not only helps to gain new insights on the interactions at the MmpL3 binding site but also provides novel indole-2-carboxamides having the potential to become antitubercular drugs in future.

An Insight into the Structural Requirements and Pharmacophore Identification of Carbonic Anhydrase Inhibitors to Combat Oxidative Stress at High Altitudes: An In-Silico Approach.

Carbonic anhydrases (CA) inhibitory action could be linked to the treatment of a number of ailments, including cancer, osteoporosis, glaucoma, and several neurological problems. For the development of effective CA inhibitors, a variety of heterocyclic rings have been investigated. Furthermore, at high altitudes, oxygen pressure drops, resulting in the formation of reactive oxygen and nitrogen species, and CA inhibitors having role in combating this oxidative stress. Acetazolamide contains thiadiazole ring, which has aroused researchers’ interest because of its CA inhibitory action. In the present study, we used a number of drug design tools, such as pharmacophore modeling, 3D QSAR, docking, and virtual screening on twenty-seven 1,3,4-thiadiazole derivatives that have been described as potential CA inhibitors in the literature. An atom-based 3D-QSAR analysis was carried out to determine the contribution of individual atoms to model generation, while a pharmacophore mapping investigation was carried out to find the common unique pharmacophoric properties required for biological activity. The coefficient of determination for both the training and test sets were statistically significant in the generated model. The best QSAR model was chosen based on the values of R2 (0.8757) and Q2 (0.7888). A molecular docking study was also conducted against the most potent analogue 4m, which has the highest SP docking score (−5.217) (PDB ID: 6g3v). The virtual screening revealed a number of promising compounds. The screened compound ZINC77699643 interacted with the amino acid residues, Pro201 and Thr199, in the virtual screening study (PDB ID: 6g3v). These interactions demonstrated the significance of the CA inhibitory activity of the compound. Furthermore, ADME study revealed useful information regarding compound’s drug-like properties. Therefore, the findings of the present investigation could aid in the development of more potent CA inhibitors, which could benefit the treatment of oxidative stress at high altitudes.

Computational Atom-based Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Model for Predicting Anti-Dengue Agents

Dengue fever is a viral infection spread by the female mosquito Aedes aegypti. It is a virus spread by mosquitoes found all over the tropics with risk levels varying depending on rainfall, relative humidity, temperature and urbanization. There are no specific medications that can be used to treat the condition. The development of possible bioactive ligands to combat Dengue fever before it becomes a pandemic is a global priority. Few studies on building three-dimensional quantitative structure-activity relationship (3D QSAR) models for anti-dengue agents have been reported. Thus, we aimed at building a statistically validated atom-based 3D-QSAR model using bioactive ligands reported to possess significant anti-dengue properties. In this study, the Schrodinger PhaseTM atom-based 3D QSAR model was developed and was validated using known anti-dengue properties as ligand data. This model was also tested to see if there was a link between structural characteristics and anti-dengue activity of a series of 3-acyl-indole derivatives. The established 3D QSAR model has strong predictive capacity and is statistically significant [Model: R2 Training Set = 0.93, Q2 (R2 Test Set) = 0.72]. In addition, the pharmacophore characteristics essential for the reported anti-dengue properties were explored using combined effects contour maps (coloured contour maps: blue: positive potential and red: negative potential) of the model. In the pathway of anti-dengue drug development, the model could be included as a virtual screening method to predict novel hits.

Exploration of the structural requirements of Aurora Kinase B inhibitors by a combined QSAR, modelling and molecular simulation approach

Aurora kinase B plays an important role in the cell cycle to orchestrate the mitotic process. The amplification and overexpression of this kinase have been implicated in several human malignancies. Therefore, Aurora kinase B is a potential drug target for anticancer therapies. Here, we combine atom-based 3D-QSAR analysis and pharmacophore model generation to identify the principal structural features of acylureidoindolin derivatives that could potentially be responsible for the inhibition of Aurora kinase B. The selected CoMFA and CoMSIA model showed significant results with cross-validation values (q2) of 0.68, 0.641 and linear regression values (r2) of 0.971, 0.933 respectively. These values support the statistical reliability of our model. A pharmacophore model was also generated, incorporating features of reported crystal complex structures of Aurora kinase B. The pharmacophore model was used to screen commercial databases to retrieve potential lead candidates. The resulting hits were analyzed at each stage for diversity based on the pharmacophore model, followed by molecular docking and filtering based on their interaction with active site residues and 3D-QSAR predictions. Subsequently, MD simulations and binding free energy calculations were performed to test the predictions and to characterize interactions at the molecular level. The results suggested that the identified compounds retained the interactions with binding residues. Binding energy decomposition identified residues Glu155, Trp156 and Ala157 of site B and Leu83 and Leu207 of site C as major contributors to binding affinity, complementary to 3D-QSAR results. To best of our knowledge, this is the first comparison of WaterSwap field and 3D-QSAR maps. Overall, this integrated strategy provides a basis for the development of new and potential AK-B inhibitors and is applicable to other protein targets.

Understanding structural characteristics of PARP-1 inhibitors through combined 3D-QSAR and molecular docking studies and discovery of new inhibitors by multistage virtual screening

Poly(ADP-ribose) polymerase (PARP) belongs to family of nuclear proteins having a critical role in base excision DNA repair system by catalysing the process of poly(ADP-ribosyl)ation leading to cell proliferation. The role of PARP inhibitors as anticancer agents was accepted after the approval of olaparib for the treatment of BRCA mutation-induced ovarian cancer. A combined ligand-based and receptor-based approach has been reported here for identification of PARP-1 inhibitors. Seventy one analogues of phthalazinone and 4-carboxamide benzimidazole showing activities against PARP-1 were used to develop ligand-based pharmacophore hypothesis using PHASE. The five-point pharmacophore hypothesis ADHRR.1031 was developed, and atom-based 3D-QSAR studies were carried. The developed 3D QSAR model has good correlation coefficient value (R2 = 0.952) and cross validated with correlation coefficient value (Q2 = 0.764). To further ascertain the structural requirement for PARP-1 inhibition, molecular interaction studies of inhibitors with PARP-1 were carried out by performing docking studies using Glide 5.6. Hydrogen bond interaction with Gly202 and Ser243 is an important interaction observed by the carboxyl amide group. The pharmacophore model depicts the importance of the hydrogen bond donor and acceptor nature of the carboxyl amide group. Further multistage virtual screening was performed to screen molecules with possible PARP-1 inhibition. Initial screening was performed based on pharmacophoric features followed by docking that consisted of HTVS, SP, and XP docking and final optimisation by Prime MM-GBSA calculation. The screened molecules were further scrutinised for their ADME-toxicity properties.

Molecular Modeling Studies on 2,4-Disubstituted Imidazopyridines as Anti-Malarials: Atom-Based 3D-QSAR, Molecular Docking, Virtual Screening, In-Silico ADMET and Theoretical Analysis

Malarial parasites have been reported for moderate-high resistance towards classical antimalarial agents and henceforth development of newer novel chemical entities targeting multiple targets rather than targeting single target will be a highly promising strategy in antimalarial drug discovery. Herein, we carried out molecular modeling studies on 2,4-disubstituted imidazopyridines as anti-hemozoin formation inhibitors by using Schrödinger’s molecular modeling package (2020_4). We have developed statistically robust atom-based 3D-QSAR model (training set, [Formula: see text]; test set, [Formula: see text]; [Formula: see text] [Formula: see text]; root-mean-square error, [Formula: see text]; standard deviation, [Formula: see text]). Our molecular docking, in-silico ADMET analysis showed that dataset molecule 37, has highly promising results. Our ligand-based virtual screening resulted in top five ZINC hits, among them ZINC73737443 hit was observed with lesser energy gap, i.e. 7.85[Formula: see text]eV, higher softness value (0.127[Formula: see text]eV), and comparatively good docking score of [Formula: see text]10.2[Formula: see text]kcal/mol. Our in-silico analysis for a proposed hit, ZINC73737443 showed that this molecule has good ADMET, in-silico nonames toxic as well as noncarcinogenic profile. We believe that further experimental as well as the in-vitro investigation will throw more lights on the identification of ZINC73737443 as a potential antimalarial agent.