Molecular dynamics simulation and docking analysis of NF-κB protein binding with sulindac acid

Variolin B from sea sponge against lung cancer: a multitargeted molecular docking with fingerprinting and molecular dynamics simulation study

Lung Cancer is the one that causes more fatalities in the world compared to other cancers, and its uniqueness is that it can be found in both males and females. However, recent data has shown that males are more affected due to lifestyle habits like smoking, tobacco consumption and inhaling polluted air. The World Health Organization has kept lung cancer on its priority list as it causes 1.8 million deaths worldwide each year, and the predictions show that the cases are going to increase year by year, and by 2050, there can be 3.8 million new cases and 3.2 million deaths, and the global health system is not prepared for it. Also, finding drug candidates that can help shrink cancerous cells and lead to their death is essential to reduce global mortality. The system needs drug compounds that can inhibit multiple paths together not to enter drug resistance quickly and to reduce costs. Our study identified a compound named Variolin B (DB08694) that belongs to the organic compounds class of pyrrolopyridines. The identified compound can inhibit multiple proteins, drastically reducing the global burden. Variolin B was identified as a potential candidate against lung cancer using the multisampling algorithm such as HTVS, SP, and XP, followed by MM\GBSA calculations showing the docking score of -9.245 Kcal/mol to -5.92 Kcal/mol. Also, we have validated it with ADMET predictions and molecular fingerprinting to analyse the interaction patterns. Further, the study was extended to molecular dynamics simulations for 100 ns to understand the complex stability and simulative interactions. The complex's overall molecular dynamics simulation helped us understand that the identified candidate is stable with the lowest deviation and fluctuations.Communicated by Ramaswamy H. Sarma.

Structure-based multitargeted docking screening, pharmacokinetics, DFT, and dynamics simulation studies reveal mitoglitazone as a potent inhibitor of cellular survival and stress response proteins of lung cancer

Lung cancer is a disease in which lung cells grow abnormally and uncontrollably, and the cause of it is direct smoking, secondhand smoke, radon, asbestos, and certain chemicals. The worldwide leading cause of death is lung cancer, which is responsible for more than 1.8 million deaths yearly and is expected to rise to 2.2 million by 2030. The most common type of lung cancer is non-small cell lung cancer (NSCLC), which accounts for about 80% and small cell lung cancer (SCLC), which is more aggressive than NSCLC and is often diagnosed later and accounts for 20% of cases. The global concern for lung cancer demands efficient drugs with the slightest chance of developing resistance, and the idea of multitargeted drug designing came up with the solution. In this study, we have performed multitargeted molecular docking studies of Drug Bank compounds with HTVS, SP and XP algorithms followed by MM\GBSA against the four proteins of lung cancer cellular survival and stress responses, which revealed Mitoglitazone as a multitargeted inhibitor with a docking and MM\GBSA score ranging from - 5.784 to - 7.739 kcal/mol and - 25.81 to - 47.65kcal/mol, respectively. Moreover, we performed pharmacokinetics studies and QM-based DFT analysis, showing suitable candidate and interaction pattern analysis revealed the most count of interacting residues was 4GLY, 5PHE, 6ASP, 6GLU, 6LYS, and 6THR. Further, the results were validated with SPC water model-based MD simulation for 100ns in neutralised condition, showing the cumulative deviation and fluctuation < 2Å with many intermolecular interactions. The whole analysis has suggested that Mitoglitazone can be used as a multitargeted inhibitor against lung cancer-however, experimental studies are needed before human use.

Structure-Based In Silico Approaches Reveal IRESSA as a Multitargeted Breast Cancer Regulatory, Signalling, and Receptor Protein Inhibitor

Breast cancer begins in the breast cells, mainly impacting women. It starts in the cells that line the milk ducts or lobules responsible for producing milk and can spread to nearby tissues and other body parts. In 2020, around 2.3 million women across the globe received a diagnosis, with an estimated 685,000 deaths. Additionally, 7.8 million women were living with breast cancer, making it the fifth leading cause of cancer-related deaths among women. The mutational changes, overexpression of drug efflux pumps, activation of alternative signalling pathways, tumour microenvironment, and cancer stem cells are causing higher levels of drug resistance, and one of the major solutions is to identify multitargeted drugs. In our research, we conducted a comprehensive screening using HTVS, SP, and XP, followed by an MM/GBSA computation of human-approved drugs targeting HER2/neu, BRCA1, PIK3CA, and ESR1. Our analysis pinpointed IRESSA (Gefitinib-DB00317) as a multitargeted inhibitor for these proteins, revealing docking scores ranging from -4.527 to -8.809 Kcal/mol and MM/GBSA scores between -49.09 and -61.74 Kcal/mol. We selected interacting residues as fingerprints, pinpointing 8LEU, 6VAL, 6LYS, 6ASN, 5ILE, and 5GLU as the most prevalent in interactions. Subsequently, we analysed the ADMET properties and compared them with the standard values of QikProp. We extended our study for DFT computations with Jaguar and plotted the electrostatic potential, HOMO and LUMO regions, and electron density, followed by a molecular dynamics simulation for 100 ns in water, showing an utterly stable performance, making it a suitable drug candidate. IRESSA is FDA-approved for lung cancer, which shares some pathways with breast cancers, clearing the hurdles of multitargeted drugs against breast and lung cancer. This has the potential to be groundbreaking; however, more studies are needed to concreate IRESSA's role.

Virtual screening and molecular dynamics investigations using natural compounds against autotaxin for the treatment of chronic pain

Chronic pain is a common and debilitating condition with a huge social and economic burden worldwide. Currently, available drugs in clinics are not adequately effective and possess a variety of severe side effects leading to treatment withdrawal and poor quality of life. Recent findings highlight the potential role of autotaxin (ATX) as a promising novel target for chronic pain management, extending beyond its previously established involvement in arthritis and other neurological disorders, such as Alzheimer's disease. In the present study, we used a virtual screening strategy by targeting ATX against commercially available natural compounds (enamine- phenotypic screening library) to identify the potential inhibitors for the treatment of chronic pain. After initial identification using molecular docking based virtual screening, molecular mechanics (MM/GBSA), ADMET profiling and molecular dynamics simulation were performed to verify top hits. The computational screening resulted in the identification of fifteen top scoring structurally diverse hits that have free energy of binding (ΔG) values in the range of -25.792 (for compound Enamine_1850) to -74.722 Kcal/mol (for compound Enamine_1687). Moreover, the top-scoring hits have favourable ADME properties as calculated using in-silico algorithms. Additionally, the molecular dynamics simulation revealed the stable nature of protein-ligand interaction and provided information about amino acid residues involved in binding. This study led to the identification of potential autotaxin inhibitors with favourable pharmacokinetic properties. Identified hits may further be investigated for their safety and efficacy potential using in-vitro and in-vivo models of chronic pain.Communicated by Ramaswamy H. Sarma.

Delineated 3-1-BenCarMethInYlPro-Phosphonic Acid's Adroit Activity against Lung Cancer through Multitargeted Docking, MM\GBSA, QM-DFT and Multiscale Simulations

Lung cancer is a pervasive and challenging disease with limited treatment options, with global health challenges often present with complex molecular profiles necessitating the exploration of innovative therapeutic strategies. Single-target drugs have shown limited success due to the heterogeneity of this disease. Multitargeted drug designing is imperative to combat this complexity by simultaneously targeting multiple target proteins and pathways, which can enhance treatment efficacy and overcome resistance by addressing the dynamic nature of the disease and stopping tumour growth and spread. In this study, we performed the molecular docking studies of Drug Bank compounds with a multitargeted approach against crucial proteins of lung cancer such as heat shock protein 5 (BIP/GRP78) ATPase, myosin 9B RhoGAP, EYA2 phosphatase inhibitor, RSK4 N-terminal kinase, and collapsin response mediator protein-1 (CRMP-1) using HTVS, SP with XP algorithms, and poses were filtered using MM\GBSA which identified [3-(1-Benzyl-3-Carbamoylmethyl-2-Methyl-1h-Indol-5-Yloxy)-Propyl-]-Phosphonic Acid (3-1-BenCarMethIn YlPro-Phosphonic Acid) (DB02504) as multitargeted drug candidate with docking and MM\GBSA score ranges from -5.83 to -10.66 and -7.56 to -50.14 Kcal/mol, respectively. Further, the pharmacokinetic and QM-based DFT studies have shown complete acceptance results, and interaction fingerprinting reveals that ILE, GLY, VAL, TYR, LEU, and GLN were among the most interacting residues. The 100 ns MD simulation in the SPC water model with NPT ensemble showed stable performance with deviation and fluctuations <2 Å with huge interactions, making it a promising multitargeted drug candidate; however, experimental studies are needed before use.

From nature to cancer therapy: Evaluating the Streptomyces clavuligerus secondary metabolites for potential protein kinase inhibitors

The study aimed to evaluate the antioxidant, protein kinase inhibitory (PKIs) potential, cytotoxicity activity of Streptomyces clavuligerus extract. DPPH assay revealed a robust free radical scavenging capacity (IC50 28.90 ± 0.24 µg/mL) of organic extract with a maximum inhibition percentage of 61 ± 1.04%. PKIs assay revealed the formation of a whitish bald zone by S. clavuligerus extracts which indicates the presence of PKIs. The cytotoxicity activity of organic fraction of extract through Sulforhodamine B assay on MCF-7, Hop-62, SiHa, and PC-3 cell lines demonstrated the lowest GI50 value against the MCF-7 cell line followed by the PC-3 cell line, showing potent growth inhibitory potential against human breast cancer and human prostate cancer cell line. HR-LCMS analysis identified multiple secondary metabolites from the organic and aqueous extracts of S. clavuligerus when incubated at 30°C under 200 rpm for 3 days. All the secondary metabolites were elucidated for their potential to inhibit RTKs by molecular docking, molecular dynamic simulation, MM/GBSA calculations, and free energy approach. It revealed the superior inhibitory potential of epirubicin (Epi) and dodecaprenyl phosphate-galacturonic acid (DPGA) against fibroblast growth factors receptor (FGFR). Epi also exhibited excellent inhibitory activity against the platelet-derived growth factor receptor (PDGFR), while DPGA effectively inhibited the vascular endothelial growth factor receptor. Additionally, the presence Epi in S. clavuligerus extract was validated through the HPLC technique. Thus, our findings highlight a superior inhibitory potential of Epi against FGFR and PDGFR RTKs than the FDA-approved drug.

Molecular screening reveals Variolin B as a multitargeted inhibitor of lung cancer: a molecular docking-based fingerprinting and molecular dynamics simulation study

Lung Cancer is the topmost death causing cancer and results from smoking, air pollution, cigar, exposure to asbestos or radon-like substances, and genetic factors. The cases of Lung Cancer in south Asian developing nations are being seen most due to heavy pollution and unbalanced lifestyle and putting a considerable burden on healthcare systems. The Food and Drug Administration of the USA has approved almost 100 drugs against SCLC and NSLC and a few drugs that are given to minimise the side effect of anticancer drugs. However, the drugs are shown to be resistant at significantly higher stages and non-affective on cancerous cells and have long-term side effects due to designing the drug by keeping one protein/gene target while designing or repurposing the drugs. In this study, we have taken five main lung cancer protein targets- Nerve growth factor protein (1SG1), Apoptosis inhibitor survivin (1XOX), Heat shock protein (3IUC), Protein tyrosine phosphate (3ZM3), Aldo-keto reductase (4XZL) and screened the complete prepared Drug Bank library of 155888 compounds and identified Variolin B (DB08694) as a multitargeted inhibitor against lung cancer using HTVS, SP and XP sampling algorithms followed by MM\GBSA calculation to sort the best pose. Variolin B is a natural marine antitumor and antiviral compound, so we analysed the ADMET properties and interaction patterns and then simulated all five P-L complexes for 100 ns in water using the NPT ensemble to check its selves against lung cancer. The docking results, ADMET and fingerprints have shown a good performance, and RMSD and RMSF results were with least deviation and fluctuations (<2Å) and produced a huge contact with other residues making the complex stable. The complexes initially fluctuated and deviated due to changes in the solute medium and sudden heat and stabilise after a few ns. However, extensive experimental validation is required before human use.Communicated by Ramaswamy H. Sarma.

Delineating Pixantrone Maleate's adroit activity against cervical cancer proteins through multitargeted docking-based MM\GBSA, QM-DFT and MD simulation

Cervical cancer poses a substantial worldwide health challenge, especially in low- and middle-income nations, caused by high-risk types of human papillomavirus. It accounted for a significant percentage of cancer-related deaths among women, particularly in areas with limited healthcare resources, necessitating innovative therapeutic approaches, and single-targeted studies have produced significant results, with a considerable chance of developing resistance. Therefore, the multitargeted studies can work as a beacon of hope. This study is focused on performing the multitargeted molecular docking of FDA-approved drugs with the three crucial proteins TBK1, DNA polymerase epsilon, and integrin α-V β-8 of cervical cancer. The docking studies using multisampling algorithms HTVS, SP, and XP reveal Pixantrone Maleate (DB06193) as a multitargeted inhibitor with docking scores of -8.147, -8.206 and -7.31 Kcal/mol and pose filtration with MM\GBSA computations with scores -40.55, -33.67, and -37.64 Kcal/mol. We also have performed QM-based DFT and pharmacokinetics studies of the compound and compared it with the standard values, which results in the compound being entirely suitable against cervical cancer proteins. The interaction fingerprints have revealed that PHE, VAL, SER and ALA are the residues among most interactions. We also explore the stability of the multitargeted potential of Pixantrone Maleate through 100ns MD simulations and investigate the RMSD, RMSF and intermolecular interactions between all three proteins-ligand complexes. All computational studies favour Pixantrone Maleate as a multitargeted inhibitor of the TBK1, DNA polymerase epsilon, and integrin α-V β-8 and can be validated experimentally before use.

Cheminformatics approach for identification of N-HyMenatPimeMelly as a novel potential ligand against RAS and renal chloride channel

Some angiotensin receptor (AR) blockers interfere with the renal chloride channel (ClC-K), which plays an important role in urine concentration. Identifying ligands targeting this channel, whether activating or blocking, is highly desirable because it could open the way for interventions that modulate their activity. In this study, the Asinex (BioDesign) complete library was screened to identify a compound with favorable physicochemical and pharmacokinetic properties, which have both AR blocking and ClC-Ka-modulating activities to present it as a novel potential oral candidate which could be useful for treatment of salt-sensitive hypertension without major ClC-K affection. A compound, N-{[4-Hydroxy-1-(2-methyl-1,6-naphthyridin-4-yl)-4-piperidinyl]methyl}-N-methyl-L-lysinamide (N-HyMenatPimeMelly) (Chem Spider ID 68416221), was identified as a potent potential oral ligand of the renin-angiotensin system (RAS) and ClC-Ka with docking scores ranging from -10.978 to -7.324 with the four selected proteins (4YAY: AR type 1, 2PFI: Cytoplasmic domain of ClC-Ka, 6JOD: AR type 2 and 6M0J: Angiotensin-converting enzyme 2). The protein-ligand complex was used to perform molecular dynamics (MD) simulation for 100 ns. The QikProp and SwissADME tools' results showed that the compound has ADME/T and drug-likeness properties, which are within the permissible ranges for 95% of known drugs. The density functional theory (DFT) analysis and MD simulation extended the study toward computational validation. Throughout the study, N-HyMenatPimeMelly has shown good interactions and stable performance in MD simulation and DFT analysis. The whole analysis has produced promising results, and N-HyMenatPimeMelly can be treated as a novel potential RAS and ClC-K oral ligand, however, experimental validation is needed before human use.Communicated by Ramaswamy H. Sarma.

PheroxyPyrabenz and Carbopyrropyridin against major proteins of SARS CoV-2: a comprehensive in-silico molecular docking and dynamics simulation studies

The pandemic that started in 2020 left us with so much information about viruses and respiratory diseases, and the cause behind it was severe acute respiratory syndrome coronavirus-2 (SARS CoV-2). The world is still recovering, which costs so many economic and other indirect disasters; despite that, no medications are available on the market. Although the WHO approved a few vaccines on an emergency basis, the remarks and the reinfection chances are still under investigation, and a few pharmaceutical companies are also claiming that a few medications can be effective. However, there is no situation in control. SARS CoV-2 mutates and comes in different forms, making the situation unpredictable. In this study, we have screened the complete Asinex's BioDesign library, which contains 170,269 compounds, and shorted the data against the docking score that helps in the identification of 4-[5-(3-Ethoxy-4-hydroxyphenyl)-1-(2-hydroxyethyl)-1H-pyrazol-3-yl]-1, 2-benzenediol (PheroxyPyrabenz) and 1-[(3R,4R)-1-(5-Aminopentanoyl)-4-hydroxy-3-pyrrolidinyl]-1H-pyrrolo[2,3-b]pyridine-4-carboxamide (Carbopyrropyridin) as a significant drug candidate that can work against the multiple proteins of the SARS CoV-2 resulting in seizing the complete biological process of the virus. Further, the study extended to Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and molecular dynamics (MD) simulation of both the compounds with their complexity. The complete workflow of the study has shown satisfactory results, and both drug candidates can potentially stop the hunt for drugs against this virus after its experimental validation. Further, we checked both compounds' absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, showing case-proof validatory results.Communicated by Ramaswamy H. Sarma.

In-silico screening and molecular dynamics simulation of quinazolinone derivatives as PARP1 and STAT3 dual inhibitors: a novel DML approaches

Modern cancer therapy now routinely employs the blocking of many oncoproteins or pathways. With two or more medicines, multiple inhibitions are often accomplished via DML techniques. In this study, we developed 30 quinazolinone derivatives as PARP1 and STAT3 dual inhibitors using DML methods and these compounds were tested for their dual inhibitory effect on PARP1 and STAT3 using docking, MM-GBSA, and molecular dynamics simulation investigations. The docking studies of ligands against PARP1 and STAT3 were performed using the Glide module, in silico ADMET screening was performed using the quickprob module, binding energy calculation was performed using the Prime MM-GBSA module, MD simulation was performed using the Desmond module, and atomic charges were calculated using the Jaguar module of Schrodinger suite 2019-4. Of the substances studied, the derivative 1f has a considerable gliding score. The in silico ADMET characteristics are within the approved range. PRIME MM-GB/SA was used to compute the binding free energy, and the results are substantial. To investigate the dynamic behavior of the protein-ligand complex, compound 1f was subjected to MD simulation at 100 ns. The tested chemical 1f produced the greatest results in MD simulations and MM-GBSA calculations, indicating that this ligand can inhibit more effectively.Communicated by Ramaswamy H. Sarma.

Theoretical, antioxidant, antidiabetic and in silico molecular docking and pharmacokinetics studies of heteroleptic oxovanadium(IV) complexes of thiosemicarbazone-based ligands and diclofenac

A series of new heteroleptic oxovanadium(IV) complexes with the general formula [VOL1-6(Dcf)] (1-6), where L1-6 = thiosemicarbazone (TSC)-based ligands and Dcf = diclofenac have been synthesized and characterized. The spectral studies along with the density functional theory calculations evidenced the distorted square-pyramidal geometry around oxovanadium(IV) ion through imine nitrogen and thione sulfur atoms of TSC moiety, and two asymmetric carboxylate oxygen atoms of diclofenac drug. The complexes were evaluated for in vitro antioxidant activity using 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,2'-diphenyl-1-picrylhydrazyl (DPPH), hydrogen peroxide (H2O2) and superoxide radical scavenging assays with respect to the standard antioxidant drugs butylated hydroxyanisole (BHA) and rutin. The in vitro antidiabetic activity of the complexes was tested with enzymes such as α-amylase, α-glucosidase and glucose-6-phosphatase. The complexes containing methyl substituent showed higher activity than that containing the nitro substituent due to the electron-donating effect of methyl group. The in silico molecular docking studies of the oxovanadium(IV) complexes with α-amylase and α-glucosidase enzymes showed strong interaction via hydrogen bonding and hydrophobic interactions. The dynamic behavior of the proposed complexes was analyzed by molecular dynamics (MDs) simulations, which revealed the stability of docked structures with α-amylase and α-glucosidase enzymes. The in silico physicochemical and pharmacokinetics parameters, such as Lipinski's 'rule of five', Veber's rule and absorption, distribution, metabolism and excretion (ADME) properties predicted non-toxic, non-carcinogenic and safe oral administration of the synthesized complexes.Communicated by Ramaswamy H. Sarma.

Unveiling the multitargeted potency of Sodium Danshensu against cervical cancer: a multitargeted docking-based, structural fingerprinting and molecular dynamics simulation study

Cervical Cancer (CC) is one of the most common types of cancer in women worldwide, with a significant number of deaths reported yearly. Despite the various treatment options available, the high mortality rate associated with CC highlights the need to develop new and effective therapeutic agents. In this study, we have screened the complete prepared FDA library against the Mitotic kinesin-like protein 1, Cyclin B1, DNA polymerase, and MCM10-ID using three glide-based molecular docking algorithms: HTVS, SP and XP to produce a robust calculation. All four proteins are crucial proteins that actively participate in CC development, and inhibiting them together can be a game-changer step for multitargeted drug designing. Our multitargeted screening identified Sodium (Na) Danshensu, a natural FDA-approved phenolic compound of caffeic acid derivatives isolated from Salvia miltiorrhiza. The docking score ranges from -5.892 to -13.103 Kcal/mol, and the screening study was evaluated with the pharmacokinetics and interaction fingerprinting to identify the pattern of interactions that revealed that the compound has bound to the best site it can be fitted to where maximum bonds were created to make the complex stable. The molecular dynamics simulations for 100 ns were then extended to validate the stability of the protein-ligand complexes. The results provide insight into the repurposing, and Na-danshensu exhibited strong binding affinity and stable complex formation with the target proteins, indicating its potential as a multitargeted drug against CC.Communicated by Ramaswamy H. Sarma.

Unveiling the multitargeted repurposing potential of taxifolin (dihydroquercetin) in cervical cancer: an extensive MM\GBSA-based screening, and MD simulation study

Cervical cancer is a significant cause of morbidity and mortality in women worldwide. Despite the availability of effective therapies, the development of drug resistance and adverse side effects remain significant challenges in cervical cancer treatment. Thus, repurposing existing drugs as multitargeted therapies for cervical cancer is an attractive approach. In this study, we extensively screened the complete prepared FDA-approved drugs and identified the repurposing potential of taxifolin, a flavonoid with known antioxidant and anti-inflammatory properties, as a multitargeted therapy for cervical cancer. We performed a computational analysis using molecular docking with various sampling algorithms, namely HTVS, SP, and XP algorithms, for robust sampling pose and filtered with MM/GBSA analysis to determine the binding affinity of taxifolin with potential targets involved in cervical cancer, such as Symmetric Mad2 Dimer, replication initiation factor MCM10-ID, TPX2, DNA polymerase epsilon B-subunit, human TBK1, and alpha-v beta-8. We then conducted MD simulations to investigate the stability and conformational changes of the complex formed between taxifolin and the mentioned proteins. Our results suggest that taxifolin has a high binding affinity ranging from - 6.094 to - 9.558 kcal/mol, indicating its potential as a multitargeted therapy for cervical cancer. Furthermore, interaction fingerprints, pharmacokinetics and MD simulations revealed that the Taxifolin-target complexes remained stable over the simulation period, indicating that taxifolin may bind to the targets for an extended period. Our study suggests that taxifolin has the potential as a multitargeted therapy for cervical cancer, and further experimental studies are necessary to validate our findings.

De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein

The drug discovery and research for an anti-COVID-19 drug has been ongoing despite repurposed drugs in the market. Over time, these drugs were discontinued due to side effects. The search for effective drugs is still under process. The role of Machine Learning (ML) is critical in the search for novel drug compounds. In the current work, using the equivariant diffusion model, we built novel compounds targeting the spike protein of SARS-CoV-2. Using the ML models, 196 de novo compounds were generated which had no hits on any major chemical databases. These novel compounds fulfilled all the criteria of ADMET properties to be lead-like and drug-like compounds. Of the 196 compounds, 15 were docked with high confidence in the target. These compounds were further subjected to molecular docking, the best compound having an IUPAC name of (4aS,4bR,8aS,8bS)-4a,8a-dimethylbiphenylene-1,4,5,8(4aH,4bH,8aH,8bH)-tetraone and a binding score of -6.930 kcal/mol. The principal compound is labeled as CoECG-M1. Density Function Theory (DFT) and Quantum optimization was carried out along with the study of ADMET properties. This suggests that the compound has potential drug-like properties. The docked complex was further subjected to MD simulations, GBSA, and metadynamics simulations to gain insights into the stability of binding. The model can be in the future modified to improve the positive docking rate.

Identification of 5-nitroindazole as a multitargeted inhibitor for CDK and transferase kinase in lung cancer: a multisampling algorithm-based structural study

Lung cancer is the second most common cancer, which is the leading cause of cancer death worldwide. The FDA has approved almost 100 drugs against lung cancer, but it is still not curable as most drugs target a single protein and block a single pathway. In this study, we screened the Drug Bank library against three major proteins- ribosomal protein S6 kinase alpha-6 (6G77), cyclic-dependent protein kinase 2 (1AQ1), and insulin-like growth factor 1 (1K3A) of lung cancer and identified the compound 5-nitroindazole (DB04534) as a multitargeted inhibitor that potentially can treat lung cancer. For the screening, we deployed multisampling algorithms such as HTVS, SP and XP, followed by the MM\GBSA calculation, and the study was extended to molecular fingerprinting analysis, pharmacokinetics prediction, and Molecular Dynamics simulation to understand the complex's stability. The docking scores against the proteins 6G77, 1AQ1, and 1K3A were - 6.884 kcal/mol, - 7.515 kcal/mol, and - 6.754 kcal/mol, respectively. Also, the compound has shown all the values satisfying the ADMET criteria, and the fingerprint analysis has shown wide similarities and the water WaterMap analysis that helped justify the compound's suitability. The molecular dynamics of each complex have shown a cumulative deviation of less than 2 Å, which is considered best for the biomolecules, especially for the protein-ligand complexes. The best feature of the identified drug candidate is that it targets multiple proteins that control cell division and growth hormone mediates simultaneously, reducing the burden of the pharmaceutical industry by reducing the resistance chance.

Repurposing of known drugs for COVID-19 using molecular docking and simulation analysis

We selected fifty one drugs already known for their potential disease treatment roles in various studies and subjected to docking and molecular docking simulation (MDS) analyses. Five of them showed promising features that are discussed and suggested as potential candidates for repurposing for COVID-19. These top five compounds were boswellic acid, pimecrolimus, GYY-4137, BMS-345541 and triamcinolone hexacetonide that interacted with the chosen receptors 1R42, 4G3D, 6VW1, 6VXX and 7MEQ, respectively with binding energies of -9.2 kcal/mol, -9.1 kcal/mol, -10.3 kcal/mol, -10.1 kcal/mol and -8.7 kcal/mol, respectively. The MDS studies for the top 5 best complexes revealed binding features for the chosen receptor, human NF-kappa B transcription factor as an important drug target in COVID-19-based drug development strategies.

Molecular docking, ADMET and molecular dynamics simulation revealed metralindole as a multitargeted inhibitor for division kinases

Lung cancer is the most common type of cancer in the world, and alone, in 2020, almost 2.21 million new cases were diagnosed, with 1.80 million deaths, and are increasing daily. Non-small cell lung (NSCLC) is the primary type of lung cancer, predominantly forms around 80% of cases compared to small cell carcinoma, and about 75% of patients are already in an advanced state when diagnosed. Despite notable advances in early diagnosis and treatment, the five-year survival rate for NSCLC is not encouraging. Therefore, it is crucial to investigate the molecular causes of non-small cell lung cancer to create more efficient therapeutic approaches. Lung cancer showed a more significant and persistent binding affinity and energy landscape with the target CDK2 staurosporine and FGF receptor-1. In this study, we have picked two essential target proteins, human cyclin-dependent kinase-2 and Human Protein Kinase CK2 Holoenzyme and screened the entire prepared DrugBank prepared library of 1,55,888 compounds and identified 2-(2-methyl-5-nitroimidazole-1-yl) ethanol (Metralindole) as a major inhibitor. Metralindole has displayed high docking scores of -5.159 Kcal/mol and -5.99 Kcal/mol with good hydrogen bonding and other bonding topologies such as van der Waals force, and ADMET results shown excellent bioavailability, outstanding solubility, no side effects, and toxicity. The molecular dynamics simulation for 100ns in a water medium confirmed the compound's stability and interaction pattern with the lowest deviation and fluctuations. Our in-silico study suggests Metralindole, an experimental compound, can effectively cure lung cancer. Further, the experimental validation of the compound is a must before any prescription.

Molecular docking analysis of mefluhybenamine with lung cancer targets

Lung cancer is the most prevalent type of cancer worldwide, with 2.21 million cases and 1.80 million fatalities in 2020. The main factor influencing lung cancer is smoking, and the most common form of lung cancer, non-small cell lung cancer (NSCLC), accounts for around 80% of instances compared to small cell carcinoma, and about 75% of patients are already in an advanced stage when they are detected. Despite significant early detection and therapy improvements, the five-year survival rate for NSCLC is not encouraging. Therefore, it is essential to look into the molecular origins of non-small cell lung cancer to develop more effective therapeutic strategies-the binding affinities and energy landscape with the proteins. Cyclin Dependent kinase 2 (CDK2) and Insulin-like Growth Factor 1 Receptor (IGF1) were more substantial and sustained in lung cancer that was chosen as the two primary target proteins in this. We screened the entire Drug Bank-prepared library of 1,55,888 compounds and found (2R,3R)-7-(Methylsulfonyl)-3-(2,4,5-trifluorophenyl) -1,2,3,4-tetrahydropyrido[1,2-a] benzimidazol-2-aminium (Mefluhybenamine) to be a significant inhibitor. Mefluhybenamine showed strong hydrogen bonding and other bonding topologies, such as van der Waals force, in its high docking scores of -6.168 Kcal/mol and -5.26 Kcal/mol, and ADMET results showed excellent bioavailability, remarkable solubility, no side effects, and toxicity. The molecular dynamicsimulation confirmed the compound's stability and interaction pattern for 100 ns in an SPC water medium with the slightest deviation and fluctuation. Data shows that Mefluhybenamine is a potential candidate. However, validation of the compound is essential.

Design of Novel Coumarin Derivatives as NUDT5 Antagonists That Act by Restricting ATP Synthesis in Breast Cancer Cells

Breast cancer, a heterogeneous disease, is among the most frequently diagnosed diseases and is the second leading cause of death due to cancer among women after lung cancer. Phytoactives (plant-based derivatives) and their derivatives are safer than synthetic compounds in combating chemoresistance. In the current work, a template-based design of the coumarin derivative was designed to target the ADP-sugar pyrophosphatase protein. The novel coumarin derivative (2R)-2-((S)-sec-butyl)-5-oxo-4-(2-oxochroman-4-yl)-2,5-dihydro-1H-pyrrol-3-olate was designed. Molecular docking studies provided a docking score of -6.574 kcal/mol and an MM-GBSA value of -29.15 kcal/mol. Molecular dynamics simulation studies were carried out for 500 ns, providing better insights into the interaction. An RMSD change of 2.4 Å proved that there was a stable interaction and that there was no conformational change induced to the receptor. Metadynamics studies were performed to calculate the unbinding energy of the principal compound with NUDT5, which was found to be -75.171 kcal/mol. In vitro validation via a cytotoxicity assay (MTT assay) of the principal compound was carried out with quercetin as a positive control in the MCF7 cell line and with an IC₅₀ value of 55.57 (+/-) 0.7 μg/mL. This work promoted the research of novel natural derivatives to discover their anticancer activity.

Current trends on the application of artificial intelligence in medical sciences

Artificial Intelligence (AI) is expanding with colossal applications in various sectors. In the healthcare sector, it is booming to make life simpler with utmost accuracy by predicting, diagnosing and up to care with the help of Machine Learning (ML) and Deep Learning (DL) applications. Modern computer algorithms have attained accuracy levels comparable to those of human specialists in medical sciences, although computers often do jobs more quickly than people do. It is also expected that there will not be a mandate for humans to be present for the jobs that machines can do, and it is gaining the highest peak because of good trained artificial models in the medical field. ML enhances the therapeutic process and improves health by encouraging more patient participation. ML may get more accurate patient data when used with the Internet of Medical Things (IoMT) and automate message notifications that prompt patients to respond at certain times. The motivation behind this article is to make a comprehensive review of the on-going implementation of ML in medical science, what challenges it is facing now, and how it can be simplified for future researchers to contribute better to medical sciences while applying it to the practitioners' jobs easier. In this review, we have extensively mined the data and brought up systematised applications of AI in healthcare, what challenges have been faced by the experts, and what ethical responsibilities are liable to them while taking the data. We also tabulated which algorithms will be helpful for what kind of data and disease conditions will be useful for future researchers and developers. This article will provide a better insight into AI and ML for the beginner to the advanced developer and researcher to understand the concepts from the basics.