Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors

Screening of Potential Inhibitors Targeting the Main Protease Structure of SARS-CoV-2 via Molecular Docking, and Approach with Molecular Dynamics, RMSD, RMSF, H-Bond, SASA and MMGBSA

Severe Acute Respiratory Syndrome caused by a coronavirus is a recent viral infection. There is no scientific evidence or clinical trials to indicate that possible therapies have demonstrated results in suspected or confirmed patients. This work aims to perform a virtual screening of 1430 ligands through molecular docking and to evaluate the possible inhibitory capacity of these drugs about the Mpro protease of Covid-19. The selected drugs were registered with the FDA and available in the virtual drug library, widely used by the population. The simulation was performed using the MolAiCalD algorithm, with a Lamarckian genetic model (GA) combined with energy estimation based on rigid and flexible conformation grids. In addition, molecular dynamics studies were also performed to verify the stability of the receptor-ligand complexes formed through analyses of RMSD, RMSF, H-Bond, SASA, and MMGBSA. Compared to the binding energy of the synthetic redocking coupling (-6.8 kcal/mol/RMSD of 1.34 Å), which was considerably higher, it was then decided to analyze the parameters of only three ligands: ergotamine (-9.9 kcal/mol/RMSD of 2.0 Å), dihydroergotamine (-9.8 kcal/mol/RMSD of 1.46 Å) and olysio (-9.5 kcal/mol/RMSD of 1.5 Å). It can be stated that ergotamine showed the best interactions with the Mpro protease of Covid-19 in the in silico study, showing itself as a promising candidate for treating Covid-19.

Arachidonic acid metabolism as a novel pathogenic factor in gastrointestinal cancers

Gastrointestinal (GI) cancers are a major global health burden, representing 20% of all cancer diagnoses and 22.5% of global cancer-related deaths. Their aggressive nature and resistance to treatment pose a significant challenge, with late-stage survival rates below 15% at five years. Therefore, there is an urgent need to delve deeper into the mechanisms of gastrointestinal cancer progression and optimize treatment strategies. Increasing evidence highlights the active involvement of abnormal arachidonic acid (AA) metabolism in various cancers. AA is a fatty acid mainly metabolized into diverse bioactive compounds by three enzymes: cyclooxygenase, lipoxygenase, and cytochrome P450 enzymes. Abnormal AA metabolism and altered levels of its metabolites may play a pivotal role in the development of GI cancers. However, the underlying mechanisms remain unclear. This review highlights a unique perspective by focusing on the abnormal metabolism of AA and its involvement in GI cancers. We summarize the latest advancements in understanding AA metabolism in GI cancers, outlining changes in AA levels and their potential role in liver, colorectal, pancreatic, esophageal, gastric, and gallbladder cancers. Moreover, we also explore the potential of targeting abnormal AA metabolism for future therapies, considering the current need to explore AA metabolism in GI cancers and outlining promising avenues for further research. Ultimately, such investigations aim to improve treatment options for patients with GI cancers and pave the way for better cancer management in this area.

Small molecule- and peptide-drug conjugates addressing integrins: A story of targeted cancer treatment

Targeted cancer treatment should avoid side effects and damage to healthy cells commonly encountered during traditional chemotherapy. By combining small molecule or peptidic ligands as homing devices with cytotoxic drugs connected by a cleavable or non-cleavable linker in peptide-drug conjugates (PDCs) or small molecule-drug conjugates (SMDCs), cancer cells and tumours can be selectively targeted. The development of highly affine, selective peptides and small molecules in recent years has allowed PDCs and SMDCs to increasingly compete with antibody-drug conjugates (ADCs). Integrins represent an excellent target for conjugates because they are overexpressed by most cancer cells and because of the broad knowledge about native binding partners as well as the multitude of small-molecule and peptidic ligands that have been developed over the last 30 years. In particular, integrin αVβ3 has been addressed using a variety of different PDCs and SMDCs over the last two decades, following various strategies. This review summarises and describes integrin-addressing PDCs and SMDCs while highlighting points of great interest.

Synthesis, molecular docking evaluation for LOX and COX-2 inhibition and determination of in-vivo analgesic potentials of aurone derivatives

In the current study, seven (7) aurone derivatives (ADs) were synthesized and employed to in-vitro LOX and COX-2 assays, in-vivo models of acetic acid-induced mice writhing, formalin-induced mice paw licking and tail immersion test to evaluate their analgesic potential at the doses of 10 mg and 20 mg/kg body weight. Molecular docking was performed to know the active binding site at both LOX and COX-2 as compared to standard drugs. Among the ADs, 2-(3,4-dimethoxybenzylidene)benzofuran-3(2H)-one (WE-4)possessed optimal LOX and COX-2 inhibitory strength (IC50=0.30 μM and 0.22 μM) as compared to standard (ZileutonIC50 = 0.08 μM, CelecoxibIC50 = 0.05 μM). Similarly in various pain models compound WE-4 showed significantly (p < 0.05) highest percent analgesic potency as compared to control at a dose of 20 mg/kg i.e. 77.60 % analgesic effect in acetic acid model, 49.97 % (in Phase-1) and 70.93 % (inPhase-2) analgesic effect in formalin pain model and 74.71 % analgesic response in tail immersion model. By the administration of Naloxone, the tail flicking latencies were reversed (antagonized) in all treatments. The WE-4 (at 10 mg/kg and 20 mg/kg) was antagonized after 90 min from 11.23 ± 0.93 and 13.41 ± 1.21 to 5.30 ± 0.48 and 4.80 ± 0.61 respectively as compared to standard Tramadol (from 17.74 ± 1.33 to 3.70 ± 0.48), showing the opiodergic receptor involvement. The molecular docking study of ADs revealed that WE-4 had a higher affinity for LOX and COX-2 with docking scores of -4.324 and -5.843 respectively. As a whole, among the tested ADs, compound WE-4 demonstrated excellent analgesic effects that may have been caused by inhibiting the LOX and COX-2 pathways.

Investigating Potential Cancer Therapeutics: Insight into Histone Deacetylases (HDACs) Inhibitions

Histone deacetylases (HDACs) are enzymes that remove acetyl groups from ɛ-amino of histone, and their involvement in the development and progression of cancer disorders makes them an interesting therapeutic target. This study seeks to discover new inhibitors that selectively inhibit HDAC enzymes which are linked to deadly disorders like T-cell lymphoma, childhood neuroblastoma, and colon cancer. MOE was used to dock libraries of ZINC database molecules within the catalytic active pocket of target HDACs. The top three hits were submitted to MD simulations ranked on binding affinities and well-occupied interaction mechanisms determined from molecular docking studies. Inside the catalytic active site of HDACs, the two stable inhibitors LIG1 and LIG2 affect the protein flexibility, as evidenced by RMSD, RMSF, Rg, and PCA. MD simulations of HDACs complexes revealed an alteration from extended to bent motional changes within loop regions. The structural deviation following superimposition shows flexibility via a visual inspection of movable loops at different timeframes. According to PCA, the activity of HDACs inhibitors induces structural dynamics that might potentially be utilized to define the nature of protein inhibition. The findings suggest that this study offers solid proof to investigate LIG1 and LIG2 as potential HDAC inhibitors.

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

Unveiling Arformoterol as a potent LSD1 inhibitor for breast cancer treatment: A comprehensive study integrating 3D-QSAR pharmacophore modeling, molecular docking, molecular dynamics simulations and in vitro assays

Lysine Specific Demethylase 1 (LSD1) has been identified as a chromatin-modifying enzyme implicated in various cancer pathogeneses, highlighting the potential for novel epigenetic cancer treatments through the development of effective inhibitors. We employed 3D-QSAR pharmacophore modeling, molecular docking, and molecular dynamics simulations to identify a promising drug candidate for LSD1 inhibition. RMSD, RMSF, H-bond, and DSSP analysis demonstrated that ZINC02599970 (Arformoterol) and ZINC13453966 exhibited the highest LSD1 inhibitory potential. Experimental validation using MCF-7 and MDA-MB-231 cell lines revealed that Arformoterol displayed potent antiproliferative activity with IC50 values of 12.30 ± 1.48 μM and 19.69 ± 1.15 μM respectively. In contrast, the IC50 values obtained for the control (tranylcypromine) in exposure to MCF-7 and MDA-MB-231 cells were 104.6 ± 1.69 μM and 77 ± 0.67 μM, respectively. Arformoterol demonstrated greater LSD1 inhibitory potency in MCF-7 cells compared to MDA-MB-231 cells. Also, the expression of genes involved in chromatin rearrangement (LSD1), angiogenesis (VEGF1), cell migration (RORα), signal transduction (S100A8), apoptosis, and cell cycle (p53) were investigated. Arformoterol enhanced apoptosis and induced cell cycle arrest at the G2/M phase, both in MCF-7 and MDA-MB-231 cancer cells. Based on our findings, we propose that Arformoterol represents a promising candidate for breast cancer treatment, owing to its potent LSD1 inhibitory activity.

Molecular characterization of glutor-GLUT interaction and prediction of glutor's drug-likeness: implications for its utility as an antineoplastic agent

Recent experimental evidence from our and other laboratories has strongly indicated that glutor, a piperazine-2-one derivative, which is a pan-GLUT inhibitor, displays a promising antineoplastic action by hampering glucose uptake owing to its ability to inhibit GLUT1 and GLUT3, which are overexpressed in neoplastic cells. However, the molecular mechanism(s) of the inhibiting action of glutor has remained elusive. Thus, for optimal utilization of the antineoplastic potential of glutor, it is essential to decipher the precise mechanism(s) of its interaction with GLUTs. Therefore, the present investigation was carried out to understand the molecular mechanism(s) of the binding of glutor to GLUT1 and GLUT3 in silico. This study suggests that glutor can effectively bind to GLUTs at the reported binding site. Moreover, the docking of glutor to GLUT was stabilised by several contacts between these two partners as shown by the 200 ns long molecular dynamic simulation carried out using Gromacs, indicating the formation of a stable complex. Moreover, glutor was found to possess all characteristics conducive to its drug-likeness. Hence, these observations suggest that glutor has the potential to be used in antineoplastic therapeutic applications.Communicated by Ramaswamy H. Sarma.

Screening of potent inhibitor from Aquilaria malaccensis Lam. against arachidonic inflammatory enzymes: an insight from molecular docking, ADMET, molecular dynamics simulation and MM-PBSA approaches

The three primary enzymes COX (cyclooxygenase), LOX (lipoxygenase) and CYT-P450 (cytochrome P450), which are part of the arachidonic inflammatory pathway, play crucial role in the development of asthma, rheumatoid arthritis and cardiovascular diseases. Ethnomedicinally, plant-derived chemicals have a major role in the treatment of fatal illnesses. Aquilaria malaccensis Lam. widely known as agarwood is prized for its fragrance and therapeutic properties. The phytochemicals and extracts of this plant have significant healing properties in the treatment of serious illnesses. In the current work, an in-silico approach including molecular docking, ADMET (absorption, distribution, metabolism, excretion and toxicity), molecular dynamics (MD) simulation and molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) was performed to screen 33 bioactive compounds from this plant against COX-2 and 5-LOX in order to find the most effective inhibitor. 2-(2-Phenylethyl)chromone was found to inhibit both 5-LOX and COX-2, showing the highest binding affinities (-9.1 kcal/mol and -9.0 kcal/mol, respectively) than standard Ibuprofen and nordihydroguaiaretic acid (NDGA). 2-(2-Phenylethyl)chromone showed the highest drug-likeness score and low risk of toxicity compared to other phytochemicals. MD modeling and MM-PBSA calculations showed that 2-(2-Phenylethyl)chromone had a strong persistent binding interaction with 5-LOX than COX-2, and this interaction is comparable to the bounded standards Ibuprofen and NDGA. From this study, we may infer that the 2-(2-Phenylethyl)chromone can serve as a potent inhibitor and has scope to be employed in the treatment of inflammatory ailments.Communicated by Ramaswamy H. Sarma.

Structure and ligand based design for identification of highly potent molecules against 5-LOX

We report here small molecules consisting of dichlorophenyl substituted oxindole that is further tagged with pyrrole/indole moieties. These molecules were designed on the basis of the analysis of binding mode of 5-LOX with arachidonic acid and zileuton. The molecules traverse the active site pocket of the enzyme that otherwise hosts AA and zileuton. Moreover, with a provision of derivatization at pyrrole/indole-N, the physico-chemical properties of the molecules can be adjusted. Appreciable 5-LOX inhibitory activities of the compounds in sub-micromolar range were observed and their aqueous solubility, binding with human serum albumin and stability in blood plasma and liver microsomes were checked. The Michaelis-Menten constants obtained during the binding of the compounds with 5-LOX indicated competitive binding of the compounds with the enzyme. Overall, the combination of molecular modelling and experimental studies identified promising molecules against inflammatory diseases.

Association of LOX gene G473A polymorphism with the occurrence of allergic rhinitis and efficacy of montelukast sodium in children

Allergic rhinitis (AR) is very common in adolescents, and current treatment options are complex and unsatisfactory. The objective of this study was to analyze the association of lysyl oxidase (LOX) gene G473A polymorphism with susceptibility to AR in children. In addition, we analyzed the therapeutic effect of montelukast sodium on AR. Forty-five children with AR (research group, 8.16±2.88 years old) and 51 healthy children (control group, 8.22±3.87 years old) during the same period were selected. The LOX gene G473A polymorphism was detected with polymerase chain reaction (PCR)-restriction fragment length polymorphism method. The effect of G473A polymorphism in the occurrence of AR was assessed by logistic regression analysis. In addition, the levels of C-reactive protein (CRP), Interleukin (IL-6), and IL-8 were measured to observe the relationship between G473A polymorphism and inflammatory factors. Finally, montelukast sodium was given to children with AR to investigate the effect of G473A polymorphism on clinical outcomes. The number of G473A polymorphisms in the research group was not significantly different from the control group for GA-type (P = 0.521). However, the number of GG-type polymorphisms was less while the number of type AA was more than the control group (P = 0.044 and 0.046). Children carrying the AA gene had an approximately 4-fold increased risk of AR, while those carrying the GG gene had a decreased risk (P < 0.001). Moreover, children carrying the GG gene had lower levels of CRP, IL-6, and IL-8 and better clinical outcomes, while those carrying the AA gene had higher levels of inflammatory factors and worse outcomes (P<0.05). LOX gene G473A polymorphism is closely associated with AR pathogenesis and may have an important research value in antagonizing the therapeutic effect of montelukast sodium.

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Bioactive lipid regulation of platelet function, hemostasis, and thrombosis

Platelets are small, anucleate cells in the blood that play a crucial role in the hemostatic response but are also implicated in the pathophysiology of cardiovascular disease. It is widely appreciated that polyunsaturated fatty acids (PUFAs) play an integral role in the function and regulation of platelets. PUFAs are substrates for oxygenase enzymes cyclooxygenase-1 (COX-1), 5-lipoxygenase (5-LOX), 12-lipoxygenase (12-LOX) and 15-lipoxygenase (15-LOX). These enzymes generate oxidized lipids (oxylipins) that exhibit either pro- or anti-thrombotic effects. Although the effects of certain oxylipins, such as thromboxanes and prostaglandins, have been studied for decades, only one oxylipin has been therapeutically targeted to treat cardiovascular disease. In addition to the well-known oxylipins, newer oxylipins that demonstrate activity in the platelet have been discovered, further highlighting the expansive list of bioactive lipids that can be used to develop novel therapeutics. This review outlines the known oxylipins, their activity in the platelet, and current therapeutics that target oxylipin signaling.

CADD, AI and ML in drug discovery: A comprehensive review

Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.

Robust anti-inflammatory activity of genistein against neutrophil elastase: a microsecond molecular dynamics simulation study

Human Neutrophil Elastase (HNE) is one of the major causes of tissue destruction in numerous chronic and inflammatory disorders and has been reported as a therapeutic target for inflammatory diseases. Overexpression of this enzyme plays a critical role in the pathogenesis of rheumatoid arthritis (RA). The focus of this study is to identify potent natural inhibitors that could target the active site of the HNE through the use of computational methods. The molecular structure of small molecules was retrieved from several natural compound databases. This was followed by structure-based virtual screening, molecular docking, ADMET property predictions and molecular dynamic simulation studies to screen potential HNE inhibitors. In total, 1881 natural compounds were extracted and subjected to molecular docking studies, and 10 compounds were found to have good interactions, exhibiting the best docking scores. Genistein showed higher binding efficacy (-10.28 Kcal/mol) to HNE in comparison to other natural compounds. The conformational stability of the docked complex of the ELANE gene (HNE) with genistein was assessed using 1-microsecond molecular dynamic simulation (MDs), which reliably revealed the unique stereochemical alteration of the complex, indicating its conformational stability and flexibility. Alterations in the enzyme structure upon complex formation were further characterized through clustering analysis and linear interaction energy (LIE) calculation. The outcomes of this research propose novel potential candidates against target HNE.Communicated by Ramaswamy H. Sarma.

Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology

Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes telemedicine, electronic health records, wearable devices, mobile health applications, and other forms of digital health technology. To this end, several research and developmental activities in various fields are gaining momentum. For instance, in the medical devices sector, several smart biomedical materials and medical devices that are digitally enabled are rapidly being developed and introduced into clinical settings. In the pharma and allied sectors, digital health-focused technologies are widely being used through various stages of drug development, viz. computer-aided drug design, computational modeling for predictive toxicology, and big data analytics for clinical trial management. In the biotechnology and bioengineering fields, investigations are rapidly growing focus on digital health, such as omics biology, synthetic biology, systems biology, big data and personalized medicine. Though digital health-focused innovations are expanding the horizons of health in diverse ways, here the development in the fields of medical devices, pharmaceutical technologies and biotech sectors, with emphasis on trends, opportunities and challenges are reviewed. A perspective on the use of digital health in the Indian context is also included.

Fragmented blind docking: a novel protein-ligand binding prediction protocol

In the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed docking protocol can provide binding site identification and binding pose prediction at the same time, by a systematical exploration of the protein volume performed with several preliminary docking calculations. In our opinion, this protocol can be successfully applied during the first steps of the virtual screening pipeline, because it provides binding site identification and binding pose prediction at the same time without visual evaluation of the binding site. After the binding pose prediction, MM/GBSA re-scoring rescoring procedures has been applied to improve the accuracy of the protein-ligand bound state. The FRAD protocol has been tested on 116 protein-ligand complexes of the Heat Shock Protein 90 - alpha, on 176 of Human Immunodeficiency virus protease 1, and on more than 100 protein-ligand system taken from the PDBbind dataset. Overall, the FRAD approach combined to MM/GBSA re-scoring can be considered as a powerful tool to increase the accuracy and efficiency with respect to other standard docking approaches when the ligand-binding site is unknown.Communicated by Ramaswamy H. Sarma.

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning

Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.

Novel 5,6-diphenyl-1,2,4-triazine-3-thiol derivatives as dual COX-2/5-LOX inhibitors devoid of cardiotoxicity

A novel series of 5,6-diphenyl-1,2,4-triazine-3-thiol derivatives were designed, synthesized, and screened for their inhibitory potential against COX-2 and 5-LOX enzymes. The compounds from the series have shown moderate to excellent inhibitory potential against both targets. Compound 6k showed the inhibitions against COX-2 (IC₅₀ = 0.33 ± 0.02 μM) and 5-LOX inhibition (IC₅₀ = 4.90 ± 0.22 μM) which was better than the standard celecoxib (IC₅₀ = 1.81 ± 0.13 μM) for COX-2 and zileuton (IC₅₀ = 15.04 ± 0.18 μM) for 5-LOX respectively. Further investigation on the selected derivative 6k in rat paw edema models revealed significant anti-inflammatory efficacy. Compound 6k has also shown negligible ulcerogenic liability as compared to indomethacin. Moreover, in vivo biochemical analysis also established the compound's antioxidant properties. Compounds 6c and 6k were also observed to be devoid of cardiotoxicity post-myocardial infarction in rats. The molecular docking and dynamics simulation studies of the most active derivative 6k affirmed their consentient binding interactions with COX-2 specific ravine and cleft of 5-LOX.

Virtual screening and activity evaluation of multitargeting inhibitors for idiopathic pulmonary fibrosis

Transforming growth factor β receptor (TGF-β1R) and receptor tyrosine kinases (RTKs), such as VEGFRs, PDGFRs and FGFRs are considered important therapeutic targets in blocking myofibroblast migration and activation of idiopathic pulmonary fibrosis (IPF). To screen and design innovative prodrug to simultaneously target these four classes of receptors, we proposed an approach based on network pharmacology combining virtual screening and machine learning activity prediction, followed by efficient in vitro and in vivo models to evaluate drug activity. We first constructed Collagen1A2-A549 cells with type I collagen as the main biomarker and evaluated the activity of compounds to inhibit collagen expression at the cellular level. The data from the first round of Collagen1A2-A549 cell screening were substituted into the machine learning model, and the model was optimized accordingly. As a result, the false positive rate of the model was reduced from 85.0% to 66.7%, and two prospective compounds, Z103080500 and Z104578368, were finally selected. Collagen levels were reduced effectively by both Z103080500 (67.88% reduction) and Z104578368 (69.54% reduction). Moreover, these two compounds showed low cellular cytotoxicity. Subsequently, the effect of Z103080500 and Z104578368 was evaluated in a bleomycin-induced C57BL/6 mouse IPF model. These results showed that 50 mg/kg Z103080500 and Z104578368 could effectively reduce the number of inflammatory cells and the expression level of α-SMA. Meanwhile, Z103080500 and Z104578368 reduced the expression of major markers and inflammatory factors of IPF, such as collagen, IFN-γ, IL-17 and HYP, indicating that these screened Z103080500 and Z104578368 effectively delayed lung tissue inflammation and had a potential therapeutic effect on IPF. Our findings demonstrate that a screening and evaluation model for prodrug against IPF has been successfully established. It is of great significance to further modify these compounds to enhance their potency and activity.

Identification of new small molecule monoamine oxidase-B inhibitors through pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation studies

The discovery of a safe and efficacious drug is a complex, time-consuming, and expensive process. Computational methodologies driven by cheminformatics tools play a central role in the high-throughput lead discovery and optimization process especially when the structure of the biological target is known. Monoamine oxidases are the membrane-bound FAD-containing enzymes and the isoform monoamine oxidase-B (MAO-B) is an attractive target for treating diseases like Alzheimer's disease, Parkinson's disease, glioma, etc. In the current study, we have used a pharmacophore-based virtual screening technique for the identification of new small molecule MAO-B inhibitors. Safinamide was used for building a pharmacophore model and the developed model was used to probe the ZINC database for potential hits. The obtained hits were filtered against drug-likeness and PAINS. Out of the hit's library, two compounds ZINC02181408, ZINC08853942 (most active), and ZINC53327382 (least active) were further subjected to molecular docking and dynamics simulation studies to assess their virtual binding affinities and stability of the resultant protein-ligand complex. The docking studies revealed that active ligands were well accommodated within the active site of MAO-B and interacted with both substrate and entrance cavity residues. MD simulation studies unveiled additional hydrogen bond interactions with the substrate cavity residues, Tyr398 and Tyr435 that are crucial for the catalytic role of MAO-B. Moreover, the predicted ADMET parameters suggest that the compounds ZINC08853942 and ZINC02181408 are suitable for CNS penetration. Thus, the attempted computational campaign yielded two potential MAO-B inhibitors that merit further experimental investigation.Communicated by Ramaswamy H. Sarma.

A New Investigation into the Molecular Mechanism of Andrographolide towards Reducing Cytokine Storm

Cytokine storm is a condition in which the immune system produces an excessive number of inflammatory signals, which can result in organ failure and death. It is also known as cytokine release syndrome, CRS, or simply cytokine storm, and it has received a lot of attention recently because of the COVID-19 pandemic. It appears to be one of the reasons why some people experience life-threatening symptoms from COVID-19, a medical condition induced by SARS-CoV-2 infection. In situations where natural substances can be exploited as therapeutics to reduce cytokine storm, the drug development process has come to the rescue. In the present study, we tested the potentiality of Andrographolide, labdane diterpenoid targeting several key cytokines that are secreted as a result of cytokine storm. We used molecular docking analyses, molecular dynamics simulations, and pharmacokinetic properties to test the stability of the complexes. The compound’s binding energy with some cytokines was over −6.5 Kcal/mol. Furthermore, a post-molecular dynamics (MD) study revealed that Andrographolide was extremely stable with these cytokines. The compound’s pharmacokinetic measurements demonstrated excellent properties in terms of adsorption, distribution, metabolism, and excretion. Our research revealed that this compound may be effective in lowering cytokine storm and treating severe symptoms.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice

In the era of emerging microbial and non-communicable diseases and re-emerging microbial infections, the medical fraternity and the public are plagued by under-preparedness. It is evident by the severity of the Coronavirus disease (COVID-19) pandemic that novel microbial diseases are a challenge and are challenging to control. This is mainly attributed to the lack of complete knowledge of the novel microbe’s biology and pathogenesis and the unavailability of therapeutic drugs and vaccines to treat and control the disease. Clinical research is the only answer utilizing which can handle most of these circumstances. In this review, we highlight the importance of computer-assisted drug designing (CADD) and the aspects of molecular docking, molecular superimposition, 3D-pharmacophore technology, ethics, and good clinical practice (GCP) for the development of therapeutic drugs, devices, and vaccines.

An Overview of Antiviral Peptides and Rational Biodesign Considerations

Viral diseases have contributed significantly to worldwide morbidity and mortality throughout history. Despite the existence of therapeutic treatments for many viral infections, antiviral resistance and the threat posed by novel viruses highlight the need for an increased number of effective therapeutics. In addition to small molecule drugs and biologics, antimicrobial peptides (AMPs) represent an emerging class of potential antiviral therapeutics. While AMPs have traditionally been regarded in the context of their antibacterial activities, many AMPs are now known to be antiviral. These antiviral peptides (AVPs) have been shown to target and perturb viral membrane envelopes and inhibit various stages of the viral life cycle, from preattachment inhibition through viral release from infected host cells. Rational design of AMPs has also proven effective in identifying highly active and specific peptides and can aid in the discovery of lead peptides with high therapeutic selectivity. In this review, we highlight AVPs with strong antiviral activity largely curated from a publicly available AMP database. We then compile the sequences present in our AVP database to generate structural predictions of generic AVP motifs. Finally, we cover the rational design approaches available for AVPs taking into account approaches currently used for the rational design of AMPs.

Estrogen receptor potentially stable conformations from molecular dynamics as a structure-based pharmacophore model for mapping, screening, and identifying ligands-a new paradigm shift in pharmacophore screening

Despite rigorous research on breast cancer has increased in recent decades, only few drugs are in practice to combat against the disease. Due to excessive usage, these drugs attain resistance is an avertable phenomenon resulting from inadequate treatment. A novel, and real-time approaches are expected to overcome to find the solution for the drug resistance. The molecular dynamics based multi-conformational sampling technique via computer-aided drug-designing approach, may be a promising route to identify the lead candidates from real-time generated frames. The estrogenic receptor, being one of the most widely targeted receptors for various breast cancer drugs namely, tamoxifen, raloxifene and GW5 (tamoxifen-resistance inhibitor) was used for simulating the molecular dynamics to obtain various real time frames. The energetically stable frames were funnelled based on Gibbs free binding energy, interaction energy and active site interaction to generate pharmacophores model for virtual screening of compounds. Generated pharmacophores are validated by receiver operating characteristic area under curve greater than 0.8. Further, screening of compounds with validated structure-based pharmacophore model of different estrogen bound drug complex conformations and binding orientations are complement for tamoxifen and tamoxifen-resistance inhibitor frames. Moreover, the best mapped compounds were docked and probed for ADMET, TopKat® and Lipinski's rule of five is more favourable for compound Andrographidine F sourced from medicinal herbal plant Andrographis paniculata. Hence, this compound had to be further analysed in in-vitro and in-vivo to prove the same.Communicated by Ramaswamy H. Sarma.

Design of Dual COX-2 and 5-LOX Inhibitors with Iron-Chelating Properties Using Structure-Based and Ligand-Based Methods

Background:

Inflammation is a critical component of many disease progressions, such as malignancy, cardiovascular and rheumatic diseases. The inhibition of inflammatory mediators synthesis by modulation of cyclooxygenase (COX) and lipoxygenase (LOX) pathways provides challenging strategy for development of more effective drugs.

Objective:

The aim of this study was to design dual COX-2 and 5-LOX inhibitors with iron-chelating properties using a combination of ligand-based (three-dimensional quantitative structure-activity relationship (3D-QSAR)) and structure-based (molecular docking) methods.

Methods:

The 3D-QSAR analysis was applied on a literature dataset consisting of 28 dual COX-2 and 5- LOX inhibitors in Pentacle software. The quality of developed COX-2 and 5-LOX 3D-QSAR models were evaluated by internal and external validation methods. The molecular docking analysis was performed in GOLD software, while selected ADMET properties were predicted in ADMET predictor software.

Results:

According to the molecular docking studies, the class of sulfohydroxamic acid analogues, previously designed by 3D-QSAR, were clustered as potential dual COX-2 and 5-LOX inhibitors with ironchelating properties. Based on the 3D-QSAR and molecular docking, 1j, 1g and 1l were selected as the most promising dual COX-2 and 5-LOX inhibitors. According to the in silico ADMET predictions, all compounds had ADMET_Risk score less than 7 and CYP_Risk score lower than 2.5. Designed compounds were not estimated as hERG inhibitors and 1j had improved intrinsic solubility (8.704) in comparison to the dataset compounds (0.411-7.946).

Conclusion:

By combining 3D-QSAR and molecular docking, three compounds (1j, 1g and 1l) were selected as the most promising designed dual COX-2 and 5-LOX inhibitors, for which good activity, as well as favourable ADMET properties and toxicity, are expected.

Advances in computer-aided drug design for type 2 diabetes

INTRODUCTION

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

AREAS COVERED

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

EXPERT OPINION

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.

Computational strategies to identify new drug candidates against neuroinflammation

The even more increasing application of computational approaches in these last decades has deeply modified the process of discovery and commercialization of new therapeutic entities. This is especially true in the field of neuroinflammation, in which both the peculiar anatomical localization and the presence of the blood-brain barrier makeit mandatory to finely tune the candidates' physicochemical properties from the early stages of the discovery pipeline. The aim of this review is therefore to provide a general overview to the readers about the topic of neuroinflammation, together with the most common computational strategies that can be exploited to discover and design small molecules controlling neuroinflammation, especially those based on the knowledge of the three-dimensional structure of the biological targets of therapeutic interest. The techniques used to describe the molecular recognition mechanisms, such as molecular docking and molecular dynamics, will therefore be eviscerated, highlighting their advantages and their limitations. Finally, we report several case studies in which computational methods have been applied in drug discovery on neuroinflammation, focusing on the last decade's research.

Lipoxygenases in chronic liver diseases: current insights and future perspectives

Chronic liver diseases (CLDs) caused by viral infections, alcohol/drug abuse, or metabolic disorders affect millions of people globally and have increased mortality owing to the lack of approved therapies. Lipoxygenases (LOXs) are a family of multifaceted enzymes that are responsible for the oxidation of polyunsaturated fatty acids (PUFAs) and are implicated in the pathogenesis of multiple disorders including liver diseases. This review describes the three main LOX signaling pathways - 5-, 12-, and 15-LOX - and their involvement in CLDs. We also provide recent insights and future perspectives on LOX-related hepatic pathophysiology, and discuss the potential of LOXs and LOX-derived metabolites as diagnostic biomarkers and therapeutic targets in CLDs.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

The emergence of the C-H functionalization strategy in medicinal chemistry and drug discovery

Owing to the market competitiveness and urgent societal need, an optimum speed of drug discovery is an important criterion for successful implementation. Despite the rapid ascent of artificial intelligence and computational and bioanalytical techniques to accelerate drug discovery in big pharma, organic synthesis of privileged scaffolds predicted in silico for in vitro and in vivo studies is still considered as the rate-limiting step. C-H activation is the latest technology added into an organic chemist's toolbox for the rapid construction and late-stage modification of functional molecules to achieve the desired chemical and physical properties. Particularly, elimination of prefunctionalization steps, exceptional functional group tolerance, complexity-to-diversity oriented synthesis, and late-stage functionalization of privileged medicinal scaffolds expand the chemical space. It has immense potential for the rapid synthesis of a library of molecules, structural modification to achieve the required pharmacological properties such as absorption, distribution, metabolism, excretion, toxicology (ADMET) and attachment of chemical reporters for proteome profiling, metabolite synthesis, etc. for preclinical studies. Although heterocycle synthesis, late-stage drug modification, ¹⁸F labelling, methylation, etc. via C-H functionalization have been reviewed from the synthetic standpoint, a general overview of these protocols from medicinal and drug discovery aspects has not been reviewed. In this feature article, we will discuss the recent trends of C-H activation methodologies such as synthesis of medicinal scaffolds through C-H activation/annulation cascade; C-H arylation for sp²-sp² and sp²-sp³ cross-coupling; C-H borylation/silylation to introduce a functional linchpin for further manipulation; C-H amination for N-heterocycles and hydrogen bond acceptors; C-H fluorination/fluoroalkylation to tune polarity and lipophilicity; C-H methylation: methyl magic in drug discovery; peptide modification and macrocyclization for therapeutics and biologics; fluorescent labelling and radiolabelling for bioimaging; bioconjugation for chemical biology studies; drug-metabolite synthesis for biodistribution and excretion studies; late-stage diversification of drug-molecules to increase efficacy and safety; cutting-edge DNA encoded library synthesis and improved synthesis of drug molecules via C-H activation in medicinal chemistry and drug discovery.

Part-II- in silico drug design: application and success

In silico tools have indeed reframed the steps involved in traditional drug discovery and development process and the term in silico has become a familiar term in pharmaceutical sector like the terms in vitro and in vivo. The successful design of HIV protease inhibitors, Saquinavir, Indinavir and other important medicinal agents, initiated interest of researchers in structure based drug design approaches (SBDD). The interactions between biomolecules and a ligand, binding energy, free energy and stability of biomolecule-ligand complex can be envisioned and predicted by applying molecular docking studies. Protein-ligand, protein-protein, DNA-ligand interactions etc. aid in elucidating molecular level mechanisms of drug molecules. In the Ligand based drug design (LBDD) approaches, QSAR studies have tremendously contributed to the development of antimicrobial, anticancer, antimalarial agents. In the recent years, multiQSAR (mt-QSAR) approaches have been successfully employed for designing drugs against multifactorial diseases. Output of a research in several instances is rewarding when both SBDD and LBDD approaches are combined. Application of in silico studies for prediction of pharmacokinetics was once a real challenge but one can see unlimited number publications comprising tools, data bases which can accurately predict almost all the pharmacokinetic parameters. Absorption, distribution, metabolism, transporters, blood brain barrier permeability, hERG toxicity, P-gp affinity and several toxicological end points can be accurately predicted for a candidate molecule before its synthesis. In silico approaches are greatly encouraged a result of growing limitations and new legislations related to the animal use for research. The combined use of in vitro data and in silico tools will definitely decrease the use of animal testing in the future.In this chapter, in silico approaches and their applications are reviewed and discussed giving suitable examples.

Spectrum of deep learning algorithms in drug discovery

Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovation has been undertaken to apply DL in data/libraries management, reaction optimizations, differentiating uncertainties, molecule constructions, creating metrics from qualitative results, and prediction of structures or interactions. From source identification to lead discovery and medicinal chemistry of the drug candidate, drug delivery, and modification, the challenges can be subjected to artificial intelligence algorithms to aid in the generation and interpretation of data. Discovery and design approach, both demand automation, large data management and data fusion by the advance in high-throughput mode. The application of DL can accelerate the exploration of drug mechanisms, finding novel indications for existing drugs (drug repositioning), drug development, and preclinical and clinical studies. The impact of DL in the workflow of drug discovery, design, and their complementary tools are highlighted in this review. Additionally, the type of DL algorithms used for this purpose, and their pros and cons along with the dominant directions of future research are presented.

Molecular docking based design of Inhibitors for viral Non-Nucleosidase as potential anti-retroviral agents

Reverse Transcriptase (RT) inhibitors are highly promising agents for use as an effective anti-retroviral therapy (HAART) which is typically a combination of three or four antiretroviral drugs. We used direct drug design approach to discover new chemical entities for the target protein. The validated template of the protein targeting reverse transcriptase PDB ID 1JKH was extracted for three sites hydrophobic, steric, and electronic parameters explain the interactions at the active site by the inhibitors. We used the Zinc library of compounds to explore the possible leads for HAART through RT inhibition. We report 12 new chemical entities with possible activity against the targeted viral protein. These leads will provide new therapeutic means in antiretroviral therapy.

Application of per-residue energy decomposition to identify the set of amino acids critical for in silico prediction of COX-2 inhibitory activity

The enormous magnitude of scientific research carried out in the field of NSAIDs and cyclooxygenases (COXs) is known. They are crucial in pain management. COX-2 inhibitors have evolved over the years; from traditional NSAIDs to isoform-specific. The present study is aimed to identify a cluster of amino acids in the catalytic site whose energy contribution can better explain COX-2 inhibitory activity accurately than the binding energy of the whole protein. Initially, MD simulations (25 ns) and MM-PBSA calculations were performed for 8 diarylheterocyclic inhibitors. Per-residue energy decomposition studies were carried out to elucidate the energy contribution of each amino acid, and their correlation with COX-2 inhibitory activity was enumerated. A cluster of catalytic amino acids whose free energy sum has a high correlation with biological data was identified. The cluster of Gln178, Ser339, Tyr341, Arg499, Phe504, Val509 and Ala513 showed the correlation of -0.60. Further, the study was extended to a total of 26 COX-2 inhibitors belonging to different classes to validate the applicability of the cluster of amino acids identified. Results clearly suggest that the cluster of amino acids identified provide accurate screening method, and can be applied to predict COX-2 inhibitory activity of small molecules.

In silico design and molecular docking study of CDK2 inhibitors with potent cytotoxic activity against HCT116 colorectal cancer cell line

BACKGROUND

Colorectal cancer is common to both sexes; third in terms of morbidity and second in terms of mortality, accounting for 10% and 9.2% of cancer cases in men and women globally. Although drugs such as bevacizumab, Camptosar, and cetuximab are being used to manage colorectal cancer, the efficacy of the drugs has been reported to vary from patient to patient. These drugs have also been reported to have varying degrees of side effects; thus, the need for novel drug therapies with better efficacy and lesser side effects. In silico drugs design methods provide a faster and cost-effect method for lead identification and optimization. The aim of this study, therefore, was to design novel imidazol-5-ones via in silico design methods.

RESULTS

A QSAR model was built using the genetic function algorithm method to model the cytotoxicity of the compounds against the HCT116 colorectal cancer cell line. The built model had statistical parameters; R2 = 0.7397, R2adj = 0.6712, Q2cv = 0.5547, and R2ext. = 0.7202 and revealed the cytotoxic activity of the compounds to be dependent on the molecular descriptors nS, GATS5s, VR1_Dze, ETA_dBetaP, and L3i. These molecular descriptors were poorly correlated (VIF < 4.0) and made unique contributions to the built model. The model was used to design a novel set of derivatives via the ligand-based drug design approach. Compounds e, h, j, and l showed significantly better cytotoxicity (IC50 < 5.0 μM) compared to the template. The interaction of the compounds with the CDK2 enzyme (PDB ID: 6GUE) was investigated via molecular docking study. The compounds were potent inhibitors of the enzyme having binding affinity of range -10.8 to -11.0 kcal/mol and primarily formed hydrogen bond interaction with lysine, aspartic acid, leucine, and histidine amino acid residues of the enzyme.

CONCLUSION

The QSAR model built was stable, robust, and had a good predicting ability. Thus, predictions made by the model were reliably employed in further in silico studies. The compounds designed were more active than the template and showed better inhibition of the CDK2 enzyme compared to the standard drugs sorafenib and kenpaullone.

Deciphering the Inhibition Mechanism of under Trial Hsp90 Inhibitors and Their Analogues: A Comparative Molecular Dynamics Simulation

Heat shock protein 90 (Hsp90) performs functions in cellular activities together with other signaling pathways. Hsp90 is evolutionarily conserved and universally articulated as a human cancer-causing agent involved in lung cancer and breast cancer followed by colon and rectum cancers. It has emerged as an effective drug candidate, and inhibition may affect several signaling pathways associated with cancer spread. Therefore, in-silico approaches, molecular docking, molecular dynamics simulation, and binding free energy calculations were applied to create insights into the inhibition mechanism against Hsp90 to identify new cancer therapeutic drugs. Top-docked Hsp90-inhibitor complexes with their analogues were selected as the best complexes based on the GOLD fitness score and orientation. The significant interaction of Hsp90 inhibitors and their analogues were observed to be bound with active site residues as well as residing within the same cavity region. System stability factors RMSD, RMSF, beta-factor, and radius of gyration were analyzed for top-docked complexes and ensure strong binding interaction between inhibitors and the Hsp90 cavity. Cavity bound inhibitors were found to retain consistent hydrogen bonding during the simulation. The radial distribution function (RDF) illustrated that interacting active site residues drive the binding and stability of the inhibitors. Similarly, the axial frequency distribution, which is an indigenously developed analytical tool, produced noteworthy knowledge of the hydrogen-bonding pattern. Results yielded new insights into the design of cancer therapeutic drugs against Hsp90. This finding suggests that under trial Hsp90 inhibitors MPC-3100 could be a potential starting point into the development of potential anticancer agents with the possibility of future directions for the improvement of early existing Hsp90 inhibitors CNF-2024 and SNX-5422 as an anticancer agent.

A cheminformatic study on chemical space characterization and diversity analysis of 5-LOX inhibitors

The process of blocking 5-lipoxygenase (5-LOX) catalyzed leukotriene biosynthesis has been recognized for the past few decades as a promising therapeutic strategy for acute inflammatory, allergic, and respiratory diseases. Due to the toxicity effect of FDA approved 5-LOX inhibitor zileuton, novel 5-LOX inhibitors have been sought by the scientific community. As a result, a significant and relevant amount of information on the structure-activity of 5-LOX inhibitors has been released and stored in public databases. In this study, we aimed at the comprehensive cheminformatic characterization of the diversity and complexity of the chemical space of 5-LOX inhibitors and its activating protein FLAP inhibitors by comparing it with the Approved drug space and virtual LOX library. The visual representation of the property space indicates some compounds in the 5-LOX inhibitors space broaden the traditional medicinal space. The structural diversity of the databases is computed using complementary approaches, including Physicochemical Property (PCP) descriptors, molecular fingerprints, and molecular scaffold. With the apparent exception of approved drugs, the 5-LOX dataset shows more diversity compared to FLAP and LOX virtual library set. This study was able to identify the underlying patterns in the chemical and pharmacological properties space that were decisive for the drug discovery and development of 5-LOX inhibitors.

Molecular docking-based virtual drug screening revealing an oxofluorenyl benzamide and a bromonaphthalene sulfonamido hydroxybenzoic acid as HDAC6 inhibitors with cytotoxicity against leukemia cells

HDAC6 is a crucial epigenetic modifier that plays a vital role in tumor progression and carcinogenesis due to its multiple biological functions. It is a unique member of class-II HDAC enzymes. It possesses two catalytic domains, which function independently of the overall enzyme activity. Up to date, there are only a few selective HDAC6 inhibitors with anti-cancer activity. In this study, 175,204 ligands obtained from the ZINC15 and OTAVAchemical databases were used for virtual drug screening against HDAC6. Molecular docking studies were performed for 100 selected compounds. Furthermore, the top 10 compounds obtained from docking were tested for their efficacy to inhibit the function of HDAC6. Five compounds (N-(9-oxo-9H-fluoren-3-yl)benzamide, 2-hydroxy-5-[(5-oxo-6-phenyl-4,5-dihydro-1,2,4-triazin-3-yl)amino]benzoic acid, 5-(4-bromonaphthalene-1-sulfonamido)-2-hydroxybenzoic acid, 2-(naphthalen-2-yl)-N-(1H-1,2,3,4-tetrazol-5-yl)cyclopropane-1-carboxamide, and 4-oxa-5,6 diazapentacyclo[10.7.1.0³,⁷.0⁸,²⁰.0¹⁴,¹⁹]icosa-1,3(7),5,8(20),9,11,14,16,18-nonaen-13-one) inhibited enzymatic activity by more than 50 % compared to DMSO as the control. Two candidates, (N-(9-oxo-9H-fluoren-3-yl)benzamide and 5-(4-bromonaphthalene-1-sulfonamido)-2-hydroxybenzoic acid), were identified with considerable cytotoxicity towards drug-sensitive CCRF-CEM and multidrug-resistant CEM/ADR5000 leukemia cells. Microscale thermophoresis revealed the binding of N-(9-oxo-9H-fluoren-3-yl)benzamide and 5-(4-bromonaphthalene-1-sulfonamido)-2-hydroxybenzoic acid to purified HDAC6 protein. Both compounds induced apoptosis in a dose-dependent manner as analyzed by flow cytometry. In conclusion, we demonstrate for the first time that these two compounds bind to HDAC6, inhibit its function, and exert cytotoxic activity by apoptosis induction.

Computational studies of a series of 2-substituted phenyl-2-oxo-, 2-hydroxyl- and 2-acylloxyethylsulfonamides as potent anti-fungal agents

Botrytis Cinerea is a plant pathogen that affect a large number of plant species like tomatoes, Lettuce, Grapes, and Strawberries among others. Sulfonamides are widely used in pharmaceutical industries as anti-cancer, anti-inflammatory and anti-viral agents. To complement our previous QSAR study, a ligand-based design and ADME/T study were carried out on these sulfonamides compounds for their fungicidal activity toward “Botrytis Cinerea”. With the help of AutoDock Vina version 4.0 in Pyrex software, the docking analysis was performed after optimization of the compounds at DFT/B3LYP/6-31G∗ quantum mechanical method using Spartan 14 softwar. Using the model generated in the previous QSAR work, the descriptors of the chosen model were considered in modifying the most promising compound ‘9’ in which twelve (12) derivatives were designed and found to have better activity than the template (compound 9). With compound 9j having the highest activity that turns out to be about 14 and 15 times more potent than the commercial fungicides “procymidone and chlorothalonil”. Furthermore, ADME/T properties of the designed compounds were calculated using the SwissADME online tool in which all the compounds were found to have good pharmacokinetic profile. Moreover, a molecular docking study on selected compounds of the dataset (compound 8, 13, 14, 19, 20, 21, 22 and 29) revealed that compound ‘20’ turned out to have the highest docking score of -8.5 kJ/mol. This compound has a strong affinity with the macromolecular target point (PDB ID: 3wh1) producing H-bond and hydrophobic interaction at the target point of amino acid residue. The molecular docking analysis gave an insight on the structure-based design of the new compounds with better activity against B. cinerea.

An Analysis of QSAR Research Based on Machine Learning Concepts

Quantitative Structure-Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to the significant role of machine learning strategies in QSAR modeling, this area of research has attracted much attention from researchers. A considerable amount of literature has been published on machine learning based QSAR modeling methodologies whilst this domain still suffers from lack of a recent and comprehensive analysis of these algorithms. This study systematically reviews the application of machine learning algorithms in QSAR, aiming to provide an analytical framework. For this purpose, we present a framework called 'ML-QSAR'. This framework has been designed for future research to: a) facilitate the selection of proper strategies among existing algorithms according to the application area requirements, b) help to develop and ameliorate current methods and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a structured categorization is depicted which studied the QSAR modeling research based on machine models. Then several criteria are introduced in order to assess the models. Finally, inspired by aforementioned criteria the qualitative analysis is carried out.

Identification of novel selective Mtb-DHFR inhibitors as antitubercular agents through structure-based computational techniques

Inhibition of dihydrofolate reductase from Mycobacterium tuberculosis-dihydrofolate reductase (Mtb-DHFR) has emerged as a promising approach for the treatment of tuberculosis. To identify novel Mtb-DHFR inhibitors, structure-based virtual screening (SBVS) of the Molecular Diversity Preservation International (MolMall) database was performed using Glide against the Mtb-DHFR and h-DHFR enzymes. On the basis of SBVS, receptor fit, drug-like filters, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis, 16 hits were selected and tested for their antitubercular activity against the H₃₇ R_V strain of M. tuberculosis. Five compounds showed promising activity with compounds 11436 and 15275 as the most potent hits with IC₅₀ values of 0.65 and 12.51 μM, respectively, against the H₃₇ R_V strain of M. tuberculosis. The two compounds were further tested in the Mtb-DHFR and h-DHFR enzymatic assay for selectivity and were found to be three- to eight-fold selective towards Mtb-DHFR over h-DHFR with minimum inhibitory concentration values of 5.50, 73.89 µM and 42.00, 263.00 µM, respectively. In silico simulation studies also supported the stability of the protein-ligand complex formation. The present study demonstrates the successful utilization of in silico SBVS tools for the identification of novel and potential Mtb-DHFR inhibitors and compound 11436 ((2,4-dihydroxyphenyl)(3,4,5-trihydroxyphenyl)methanone) as a potential lead for the development of novel Mtb-DHFR inhibitors.