Novel approach for efficient pharmacophore-based virtual screening: method and applications

Identification of potential biogenic chalcones against antibiotic resistant efflux pump (AcrB) via computational study

The cases of bacterial multidrug resistance are increasing every year and becoming a serious concern for human health. Multidrug efflux pumps are key players in the formation of antibiotic resistance, which transfer out a broad spectrum of drugs from the cell and convey resistance to the host. Efflux pumps have significantly reduced the efficacy of the previously available antibiotic armory, thereby increasing the frequency of therapeutic failures. In gram-negative bacteria, the AcrAB-TolC efflux pump is the principal transporter of the substrate and plays a major role in the formation of antibiotic resistance. In the current work, advanced computer-aided drug discovery approaches were utilized to find hit molecules from the library of biogenic chalcones against the bacterial AcrB efflux pump. The results of the performed computational studies via molecular docking, drug-likeness prediction, pharmacokinetic profiling, pharmacophore mapping, density functional theory, and molecular dynamics simulation study provided ZINC000004695648, ZINC000014762506, ZINC000014762510, ZINC000095099506, and ZINC000085510993 as stable hit molecules against the AcrB efflux pumps. Identified hits could successfully act against AcrB efflux pumps after optimization as lead molecules.Communicated by Ramaswamy H. Sarma.

Pharmacophore-based virtual screening, molecular docking and molecular dynamics studies for the discovery of novel neuraminidase inhibitors

The in silico evaluation of 27 p-aminosalicylic acid derivatives, also referred to as neuraminidase inhibitors was the focus of the current study. To search and predict new potential neuraminidase inhibitors, this study was based on the ligand-based pharmacophore modeling, 3D QSAR, molecular docking, ADMET and MD simulation studies. The data was generated from recently reported inhibitors and divided into two groups, one of these group has 17 compounds for training and the second group has 10 compounds for testing purpose. The generated pharmacophore has known as ADDPR_4 was found statistically significant 3D-QSAR model owing the high trust scores (R2 = 0.974, Q2 = 0.905, RMSE = 0.23). Morever external validation was also employed to evaluate the prediction capacity of the built pharmacophore model (R2pred = 0.905). In addition, in silico ADMET, analyses were employed to evaluate the obtained hits for drug likeness properties. The stability of formed complexes was further evaluated using molecular dynamics. Top two hits showed stable complexes with Neuraminidase based on calculated total binding energy by MM-PBSA.Communicated by Ramaswamy H. Sarma.

Structure-activity relationship of pharmacophores and toxicophores: the need for clinical strategy

OBJECTIVES

Sometimes clinical efficacy and potential risk of therapeutic and toxic agents are difficult to predict over a long period of time. Hence there is need for literature search with a view to assessing cause of toxicity and less efficacy of drugs used in clinical practice.

METHOD

Hence literatures were searched for physicochemical properties, chemical formulas, molecular masses, pH values, ionization, receptor type, agonist and antagonist, therapeutic, toxic and structure-activity relationship of chemical compounds with pharmacophore and toxicophore, with a view to identifying high efficacious and relative low toxic agents. Inclusion criteria were manuscripts published on PubMed, Scopus, Web of Science, PubMed Central, Google Scholar among others, between 1960 and 2023. Keywords such as pharmacophore, toxicophore, structure-activity-relationship and disease where also searched. The exclusion criteria were the chemicals that lack pharmacophore, toxicophore and manuscripts published before 1960.

RESULTS

Findings have shown that pharmacophore and toxicophore functional groups determine clinical efficacy and safety of therapeutics, but if they overlap therapeutic and toxicity effects go concurrently. Hence the functional groups, dose, co-administration and concentration of drugs at receptor, drug-receptor binding and duration of receptor binding are the determining factors of pharmacophore and toxicophore activity. Molecular mass, chemical configuration, pH value, receptor affinity and binding capacity, multiple pharmacophores, hydrophilic/lipophilic nature of the chemical contribute greatly to functionality of pharmacophore and toxicophore.

CONCLUSION

Daily single therapy, avoidance of reversible pharmacology, drugs with covalent adduct, maintenance of therapeutic dose, and the use of multiple pharmacophores for terminal diseases will minimize toxicity and improve efficacy.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Cheminformatics identification of phenolics as modulators of penicillin-binding protein-3 of Pseudomonas aeruginosa towards interventive antibacterial therapy

Antibacterial resistance to β-lactams in microorganisms has been attributed majorly to alterations in penicillin-binding proteins (PBPs) coupled with β-lactams' inactivation by β-lactamase. Consequently, the identification of a novel class of therapeutics with improved modulatory action on the PBPs is imperative and plant secondary metabolites, including phenolics, have found relevance in this regard. For the first time in this study, the over 10,000 phenolics currently known were computationally evaluated against PBP3 of Pseudomonas aeruginosa, a superbug implicated in several nosocomial infections. In doing this, a library of phenolics with an affinity for PBP3 of P. aeruginosa was screened using structure-activity relationship-based pharmacophore and molecular docking approaches. Subsequent thermodynamic screening of the top five phenolics with higher docking scores, more drug-likeness attributes, and feasible synthetic accessibility was achieved through a 120 ns molecular dynamic (MD) simulation. Four of the top five hits had higher binding free energy than cefotaxime (-18.72 kcal/mol), with catechin-3-rhamside having the highest affinity (-28.99 kcal/mol). All the hits were stable at the active site of the PBP3, with catechin-3-rhamside being the most stable (2.14 Å), and established important interactions with Ser294, implicated in the catalytic activity of PBP3. Also, PBP3 became more compact with less fluctuation of the active site amino acid residues following the binding of the hits. These observations are indicative of the potential of the test compounds as PBP3 inhibitors, with catechin-3-rhamside being the most prominent of the compounds that could be further improved for enhanced druggability against PBP3 in vitro and in vivo.Communicated by Ramaswamy H. Sarma.

Multi-targeted anti-Alzheimer's agents: Synthesis, biological evaluation, and molecular modeling study of some pyrazolopyridine hybrids

A new series of compounds bearing a pyrazolopyridine scaffold was synthesized as integrated anti-Alzheimer's disease (AD) multi-targeted ligands. Compounds 49 and 51 showed remarkable activity as hAChE inhibitors with IC50 values of 0.17 and 0.16 μM, respectively; and proved to be active hBuChE inhibitors with IC50 values 0.17 and 0.69 μM, eight and two-fold more active than the reference compound rivastigmine, respectively. Compounds 49 and 51 showed potent GSK3β inhibition with IC50 values of 0.21 and 0.26 μM, respectively compared to L807mts. Also, 49 and 51 showed 66.0 and 60.0% as tau protein aggregation inhibitors; and Aβ1-42 self-aggregation inhibitors with 79.0 and 75.0% respectively. Furthermore, 49 and 51 could bind virtually with the PAS affecting Aβ aggregation, thus preventing Aβ-dependent neurotoxicity. They proved to have the ability to chelate bio-metals such as Fe2+, Cu2+, and Zn2+ preventing their oxidative damage in the brain of AD patients, in addition to their safety upon WI-38 cell line. Both compounds could virtually penetrate BBB and obeyed Lipinski's rule of five. Compounds 49 and 51 could be considered as MTDLs for AD patients and the obtained model and pattern of substitution could be used for further development of new multi-targeted anti-Alzheimer's agents.

Identification of hits as anti-obesity agents against human pancreatic lipase via docking, drug-likeness, in-silico ADME(T), pharmacophore, DFT, molecular dynamics, and MM/PB(GB)SA analysis

Obesity, characterized by excessive fat accumulation, is a major health concern. Inhibition of human pancreatic lipase, an enzyme involved in fat digestion, offers a potential strategy for weight loss and obesity treatment. This study aimed to identify polyphenols capable of forming stable complexes with human pancreatic lipase to block its activity. Molecular docking, density functional theory (DFT), molecular dynamics (MD) simulations, and MMPBGBSA calculations were employed to evaluate ligand binding, stability, and energy profiles. Pharmacophore modeling was also performed to identify key structural features for effective inhibition. Virtual screening identified ZINC000015120539, ZINC000000899200, ZINC000001531702, and ZINC000013340267 as potential candidates, exhibiting favorable binding and stable interactions over 100 ns MD simulations. These findings provide insights into the inhibitory potential of selected polyphenols on human pancreatic lipase and support further experimental investigations for obesity treatment.Communicated by Ramaswamy H. Sarma.

In silico characterization of ligand-receptor interactions for U-47700, N,N-didesmethyl-U-47700, U-50488 at mu- and kappa-opioid receptors

The increasing misuse of novel synthetic opioids (NSOs) represents a serious public health concern. In this regard, U-47700 (trans-3,4-dichloro-N-[2-(dimethylamino)cyclohexyl]-N-methylbenzamide) and related "U-compounds" emerged on recreational drug markets as synthetic substitutes for illicit heroin and constituents of counterfeit pain medications. While the pharmacology of U-compounds has been investigated using in vitro and in vivo methods, there is still a lack of understanding about the details of ligand-receptor interactions at the molecular level. To this end, we have developed a molecular modeling protocol based on docking and molecular dynamics simulations to assess the nature of ligand-receptor interactions for U-47700, N,N-didesmethyl U-47700, and U-50488 at the mu-opioid receptor (MOR) and kappa-opioid receptor (KOR). The evaluation of ligand-receptor and ligand-receptor-membrane interaction energies enabled the identification of subtle conformational shifts in the receptors induced by ligand binding. Interestingly, the removal of two key methyl groups from U-47700, to form N,N-didesmethyl U-47700, caused a loss of hydrogen bond contact with tryptophan (Trp)229, which may underlie the lower interaction energy and reduced MOR affinity for the compound. Taken together, our results are consistent with the reported biological findings for U-compounds and provide a molecular basis for the MOR selectivity of U-47700 and KOR selectivity of U-50488.

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.

Exploring biogenic chalcones as DprE1 inhibitors for antitubercular activity via in silico approach

Cases of drug-resistant tuberculosis (TB) have increased worldwide in the last few years, and it is a major threat to global TB control strategies and the human population. Mycobacterium tuberculosis is a common causative agent responsible for increasing cases of TB and as reported by WHO, approximately, 1.5 million death occurred from TB in 2020. Identification of new therapies against drug-resistant TB is an urgent need to be considered primarily. The current investigation aims to find the potential biogenic chalcone against the potential targets of drug-resistant TB via in silico approach. The ligand library of biogenic chalcones was screened against DprE1. Results of molecular docking and in silico ADMET prediction revealed that ZINC000005158606 has lead-like properties against the targeted protein. Pharmacophore modeling was done to identify the pharmacophoric features and their geometric distance present in ZINC000005158606. The binding stability study performed using molecular dynamics (MD) simulation of the DprE1-ZINC000005158606 complex revealed the conformational stability of the complex system over 100 ns with minimum deviation. Further, the in silico anti-TB sensitivity of ZINC000005158606 was found to be higher as compared to the standards against Mycobacterium tuberculosis. The overall in silico investigation indicated the potential of identified hit to act as a lead molecule against Mycobacterium tuberculosis.

In Silico Binding of 2-Aminocyclobutanones to SARS-CoV-2 Nsp13 Helicase and Demonstration of Antiviral Activity

The landscape of viral strains and lineages of SARS-CoV-2 keeps changing and is currently dominated by Delta and Omicron variants. Members of the latest Omicron variants, including BA.1, are showing a high level of immune evasion, and Omicron has become a prominent variant circulating globally. In our search for versatile medicinal chemistry scaffolds, we prepared a library of substituted ɑ-aminocyclobutanones from an ɑ-aminocyclobutanone synthon (11). We performed an in silico screen of this actual chemical library as well as other virtual 2-aminocyclobutanone analogs against seven SARS-CoV-2 nonstructural proteins to identify potential drug leads against SARS-CoV-2, and more broadly against coronavirus antiviral targets. Several of these analogs were initially identified as in silico hits against SARS-CoV-2 nonstructural protein 13 (Nsp13) helicase through molecular docking and dynamics simulations. Antiviral activity of the original hits as well as ɑ-aminocyclobutanone analogs that were predicted to bind more tightly to SARS-CoV-2 Nsp13 helicase are reported. We now report cyclobutanone derivatives that exhibit anti-SARS-CoV-2 activity. Furthermore, the Nsp13 helicase enzyme has been the target of relatively few target-based drug discovery efforts, in part due to a very late release of a high-resolution structure accompanied by a limited understanding of its protein biochemistry. In general, antiviral agents initially efficacious against wild-type SARS-CoV-2 strains have lower activities against variants due to heavy viral loads and greater turnover rates, but the inhibitors we are reporting have higher activities against the later variants than the wild-type (10–20X). We speculate this could be due to Nsp13 helicase being a critical bottleneck in faster replication rates of the new variants, so targeting this enzyme affects these variants to an even greater extent. This work calls attention to cyclobutanones as a useful medicinal chemistry scaffold, and the need for additional focus on the discovery of Nsp13 helicase inhibitors to combat the aggressive and immune-evading variants of concern (VOCs).

An in-silico pharmacophore-based molecular docking study to evaluate the inhibitory potentials of novel fungal triterpenoid Astrakurkurone analogues against a hypothetical mutated main protease of SARS-CoV-2 virus

BACKGROUND

The main protease is an important structural protein of SARS-CoV-2, essential for its survivability inside a human host. Considering current vaccines' limitations and the absence of approved therapeutic targets, Mpro may be regarded as the potential candidate drug target. Novel fungal phytocompound Astrakurkurone may be studied as the potential Mpro inhibitor, considering its medicinal properties reported elsewhere.

METHODS

In silico molecular docking was performed with Astrakurkurone and its twenty pharmacophore-based analogues against the native Mpro protein. A hypothetical Mpro was also constructed with seven mutations and targeted by Astrakurkurone and its analogues. Furthermore, multiple parameters such as statistical analysis (Principal Component Analysis), pharmacophore alignment, and drug likeness evaluation were performed to understand the mechanism of protein-ligand molecular interaction. Finally, molecular dynamic simulation was done for the top-ranking ligands to validate the result.

RESULT

We identified twenty Astrakurkurone analogues through pharmacophore screening methodology. Among these twenty compounds, two analogues namely, ZINC89341287 and ZINC12128321 showed the highest inhibitory potentials against native and our hypothetical mutant Mpro, respectively (-7.7 and -7.3 kcal mol-1) when compared with the control drug Telaprevir (-5.9 and -6.0 kcal mol-1). Finally, we observed that functional groups of ligands namely two aromatic and one acceptor groups were responsible for the residual interaction with the target proteins. The molecular dynamic simulation further revealed that these compounds could make a stable complex with their respective protein targets in the near-native physiological condition.

CONCLUSION

To conclude, Astrakurkurone analogues ZINC89341287 and ZINC12128321 can be potential therapeutic agents against the highly infectious SARS-CoV-2 virus.

Screening of BACE1 inhibitors with antiamyloidogenic activity: A study of flavonoids and flavonoid derivatives

Aggregates of β-amyloid peptide are found to occur in brains of AD patients and are formed upon sequential cleavage of the amyloid precursor protein by BACE1 and γ-secretase. Strategies inhibiting either peptide aggregation or the rate limiting enzyme BACE1 have been in demand for its implication in AD therapeutics. The present study is undertaken to mine compounds with dual ability. In this context, some natural compounds that were already predicted as BACE1 inhibitors by our group, were further tested for their activity as aggregation inhibitors. A pharmacophore model built with known antiamyloidogenic compounds was then applied for screening the natural compounds previously predicted as BACE1 inhibitors. Subsequently experimental validation by Thioflavin-T and Aβ-GFP assay filtered four compounds genistein, syringetin, tamarixetin and ZINC53276039. Out of them, ZINC53276039 showed promising antiamyloidogenic activity to act as a potent inhibitor of aggregation. Interestingly, our previous study revealed syringetin and ZINC53276039 to be good BACE1 inhibitors while tamarixetin to be a moderate BACE1 inhibitor. These good to moderate BACE1 inhibitors with moderate to reasonable antiamyloidogenic activity might show potency in reducing the amyloid load of AD brains.

Virtual Screening-Based Drug Development for the Treatment of Nervous System Diseases

The incidence rate of nervous system diseases has increased in recent years. Nerve injury or neurodegenerative diseases usually cause neuronal loss and neuronal circuit damage, which seriously affect motor nerve and autonomic nervous function. Therefore, safe and effective treatment is needed. As traditional drug research becomes slower and more expensive, it is vital to enlist the help of cutting- edge technology. Virtual screening (VS) is an attractive option for the identification and development of promising new compounds with high efficiency and low cost. With the assistance of computer- aided drug design (CADD), VS is becoming more and more popular in new drug development and research. In recent years, it has become a reality to transform non-neuronal cells into functional neurons through small molecular compounds, which provides a broader application prospect than transcription factor-mediated neuronal reprogramming. This review mainly summarizes related theory and technology of VS and the drug research and development using VS technology in nervous system diseases in recent years, and focuses more on the potential application of VS technology in neuronal reprogramming, thus facilitating new drug design for both prevention and treatment of nervous system diseases.

Design, Synthesis and Pharmacological Evaluation of Novel C2,C3-Quinoxaline Derivatives as Promising Anxiolytic Agents

A new series of quinoxaline derivatives, 2a-4b, were synthesized and their anxiolytic potential was evaluated in vivo using elevated plus maze (EPM), open field (OF) and light-dark box (LDB) techniques. According to the results of the EPM, four active compounds were found in 2a, 2b, 2c, 4b. Their anxiolytic properties were confirmed in terms of LDB and the most active was compound 2b. In the OF, only 2c had an influence on the locomotor activity of the rodents. Thus, the most promising substance was determined; this was 2b, which has the structure of 2-(2-{[3-(4-tert-butylphenyl)quinoxaline-2-yl]methyl}-4,5-dimethoxyphenyl)-N-methylethan-1-amine hydrochloride. The obtained data were analyzed with the pharmacophore feature prediction approach, which made it possible to compare the structures of the studied compounds with the reference drug diazepam, and to determine the contribution of pharmacophores to the manifestation of the activity under study. ADMET analysis was carried out for compound 2b and the acute oral toxicity of this substance was also tested in vivo. As a result of the study, a promising compound with a high anxiolytic effect and low level of toxicity 2b was found, which is of interest for further preclinical study of its properties.

Free Energy Calculations Using the Movable Type Method with Molecular Dynamics Driven Protein–Ligand Sampling

Fast and accurate biomolecular free energy estimation has been a significant interest for decades, and with recent advances in computer hardware, interest in new method development in this field has even grown. Thorough configurational state sampling using molecular dynamics (MD) simulations has long been applied to the estimation of the free energy change corresponding to the receptor-ligand complexing process. However, performing large-scale simulation is still a computational burden for the high-throughput hit screening. Among molecular modeling tools, docking and scoring methods are widely used during the early stages of the drug discovery process in that they can rapidly generate discrete receptor-ligand binding modes and their individual binding affinities. Unfortunately, the lack of thorough conformational sampling in docking and scoring protocols leads to difficulty discovering global minimum binding modes on a complicated energy landscape. The Movable Type (MT) method is a novel absolute binding free energy approach which has demonstrated itself to be robust across a wide range of targets and ligands. Traditionally, the MT method is used with protein-ligand binding modes generated with rigid-receptor or flexible-receptor (induced fit) docking protocols; however, these protocols are by their nature less likely to be effective with more highly flexible targets or with those situations in which binding involves multiple step pathways. In these situations, more thorough samplings are required to better explain the free energy of binding. Therefore, to explore the prediction capability and computational efficiency of the MT method when using more thorough protein-ligand conformational sampling protocols, in the present work, we introduced a series of binding mode modeling protocols ranging from conventional docking routines to single-trajectory conventional molecular dynamics (cMD) and parallel Monte Carlo molecular dynamics (MCMD). Through validation against several structurally and mechanistically diverse protein-ligand test sets, we explore the performance of the MT method as a virtual screening tool to work with the docking protocols and as an MD simulation-based binding free energy tool.

Interface inhibitory action on Interleukin-1β using selected anti-inflammatory compounds to mitigate the depression: A computational investigation

Interleukin-1β (IL1β) is a keynote mediator of inflammation with diverse physiological functions, playing a fundamental role in memory and mood regulation. The pleiotropic effects of IL-1β have been proposed to be implicated in the pathogenesis and etiology of depression. Thus, targeting IL-1β offers an inimitable opportunity to develop new strategies for an alternative therapy to treat depression. The focus of this study is to find out the potential inhibitors against IL-1β. Since, there is no oral specific drug reported yet thus, demanding an urgent need to develop new immunomodulatory drugs to combat chronic diseases. In this study, ligand-based pharmacophore modeling integrated with virtual screening and molecular docking strategy was designed to identify novel compounds capable of inhibiting the interactions towards cognitive receptor IL-1RI. In this connection, a set of 30,000 compounds were screened by a developed pharmacophore model that led to the retrieval of 2043 molecules from the in-house library and ZINC Database. Primarily, specific binding regions for IL-1β inhibitors have been explored by blind docking studies. After the selection of the binding site, the hits identified as actives based on the 3D-pharmacophore model were assessed by molecular docking studies. In a stepwise screening, six potential virtual hits were shortlisted for molecular dynamic simulation to acquire insights into their dynamic behavior. The obtained results highlighted that these compounds are stabilized in the targeted pocket of IL-1β and possibly block the formation of an active heterocomplex, subsequently locking the associated signaling cascade. Further in vitro experiments confirmed the inhibitory potential of Compound-157 and compound-283 with the IC₅₀ of 1.6 ± 0.1 and 9.1 ± 1.7 µg/mL respectively.

Discovery of novel thiophene derivatives as potent neuraminidase inhibitors

Neuraminidase (NA) is an important target for the treatment of influenza. In this study, a new lead NA inhibitor, 4 (ZINC01121127), was discovered by pharmacophore-based virtual screening and molecular dynamic (MD) simulation. Some novel NA inhibitors containing thiophene ring were synthesized by optimizing the skeleton of the lead compound 4. Compound 4b had the most potent inhibitory activity against NA (IC₅₀ = 0.03 μM), which was better than the positive control oseltamivir carboxylate (IC₅₀ = 0.06 μM). 4b (EC₅₀ = 1.59 μM) also exhibits excellent antiviral activity against A/chicken/Hubei/327/2004 (H5N1-DW), which is superior to the reference drug OSC (EC₅₀ = 5.97 μM). Molecular docking study shows that the thiophene moiety plays an essential role in compound 4b, which can bind well to the active site of NA. The good activity of 4b may be also ascribed to the extending of quinoline ring into the 150-cavity. The results of this study may provide an insightful help for the development of new NA inhibitors.

Pharmacophore modeling, docking and the integrated use of a ligand- and structure-based virtual screening approach for novel DNA gyrase inhibitors: synthetic and biological evaluation studies

Fluoroquinolones, a class of compound, act via inhibiting DNA gyrase and topoisomerase IV enzymes. This is an important class of drugs with high success rates for the treatment of tuberculosis and other bacterial infections. An indirect drug design approach was used to develop a meaningful pharmacophore model using the HypoGen module of Discovery Studio 2.0 on a set of 27 structurally diverse compounds with a wide range of biological activity (5 log units). The best hypothesis had three hydrogen bond acceptors (HBA) and one hydrophobic (Hy) moiety, showing r = 0.95, and it predicts the test set of 44 compounds well, with r 2 = 0.823. The same features (acceptor and hydrophobic functionality) were validated at the binding site of the DNA gyrase active site using GOLD version 3.0.1 and Molegro Virtual Docker, which showed corresponding hydrogen bond interactions and also π-π stacking interactions that correlated well with the PIC50 values (r 2 = 0.6142). The thoroughly validated model was used to screen an extensive database of 0.25 million compounds to identify potential leads. The validated model was implemented for the identification, design, synthesis, and biological evaluation of leads. Ten new chemical entities were synthesized based on our scaffold hopping techniques from the identified virtual screening and tested against the tuberculosis bacterium to obtain preliminary MIC values. The results showed that 3 out of 10 synthesized compounds exhibited good MICs, from 1.25 to 50 μM. This proves the robustness and applicability of the developed model, which is a promising tool for identifying new topoisomerase II inhibitors for the treatment of tuberculosis.

Computational Discovery of SARS-CoV-2 NSP 16 Drug Candidates Based on Pharmacophore Modeling and Molecular Dynamics Simulation

Non-Structural Protein 16 (NSP-16) is one of the most suitable targets for discovery of drugs for corona viruses including SARS-CoV-2. In this study, drug discovery of SARS-CoV-2 nsp-16 has been accomplished by pharmacophore-based virtual screening among some analogs (FDA approved drugs) and marine natural plants (MNP). The comparison of the binding energies and the inhibition constants was determined using molecular docking method. Three compounds including two FDA approved (Ibrutinib, Idelalisib) and one MNP (Kumusine) were selected for further investigation using the molecular dynamics simulations. The results indicated that Ibrutinib and Idelalisib are oral medications while Kumusine, with proper hydrophilic and solubility properties, is an appropriate candidate for nsp-16 inhibitor and can be effective to control COVID-19 disease.

Synthesis and Modeling of Ezetimibe Analogues

Ezetimibe is a well-known drug that lowers blood cholesterol levels by reducing its absorption in the small intestine when joining to Niemann-Pick C1-like protein (NPC1L1). A ligand-based study on ezetimibe analogues is reported, together with one-hit synthesis, highlighted in the study. A convenient asymmetric synthesis of (2S,3S)-N-α-(R)-methylbenzyl-3-methoxycarbonylethyl-4-methoxyphenyl β-lactam is described starting from Baylis-Hillman adducts. The route involves a domino process: allylic acetate rearrangement, stereoselective Ireland-Claisen rearrangement and asymmetric Michael addition, which provides a δ-amino acid derivative with full stereochemical control. A subsequent inversion of ester and acid functionality paves the way to the lactam core after monodebenzylation and lactam formation. It also shows interesting results when it comes to a pharmacophore study based on ezetimibe as the main ligand in lowering blood cholesterol levels, revealing which substituents on the azetidine-2-one ring are more similar to the ezetimibe skeleton and will more likely bind to NPC1L1 than ezetimibe.

Unraveling the binding mechanism of an Oxovanadium(IV) - Curcumin complex on albumin, DNA and DNA gyrase by in vitro and in silico studies and evaluation of its hemocompatibility

An oxovanadium(IV) - curcumin based complex, viz. [VO(cur)(2,2´-bipy)(H₂O)] where cur is curcumin and bipy is bipyridine, previously synthesized, has been studied for interaction with albumin and DNA. Fluorescence emission spectroscopy was used to evaluate the interaction of the complex with bovine serum albumin (BSA) and the BSA-binding constant (K_b) was calculated to be 2.56 x 10⁵ M^-1, whereas a single great-affinity binding site was revealed. Moreover, the hemocompatibility test demonstrated that the complex presented low hemolytic fraction (mostly below 1%), in all concentrations tested (0-250 μΜ of complex, 5% DMSO) assuring a safe application in interaction with blood. The binding of the complex to DNA was also investigated using absorption, fluorescence, and viscometry methods indicating a binding through a minor groove mode. From competitive studies with ethidium bromide the apparent binding constant value to DNA was estimated to be 4.82 x 10⁶ M^-1. Stern-Volmer quenching phenomenon gave a Κ_SV constant [1.92 (± 0.05) x 10⁴ M^-1] and k_q constant [8.33 (± 0.2) x 10¹¹ M^-1s^-1]. Molecular docking simulations on the crystal structure of BSA, calf thymus DNA, and DNA gyrase, as well as pharmacophore analysis for BSA target, were also employed to study in silico the ability of [VO(cur)(2,2´-bipy)(H₂O)] to bind to these target bio-macromolecules and explain the observed in vitro activity.

Repurposing potential of Ayurvedic medicinal plants derived active principles against SARS-CoV-2 associated target proteins revealed by molecular docking, molecular dynamics and MM-PBSA studies

All the plants and their secondary metabolites used in the present study were obtained from Ayurveda, with historical roots in the Indian subcontinent. The selected secondary metabolites have been experimentally validated and reported as potent antiviral agents against genetically-close human viruses. The plants have also been used as a folk medicine to treat cold, cough, asthma, bronchitis, and severe acute respiratory syndrome in India and across the globe since time immemorial. The present study aimed to assess the repurposing possibility of potent antiviral compounds with SARS-CoV-2 target proteins and also with host-specific receptor and activator protease that facilitates the viral entry into the host body. Molecular docking (MDc) was performed to study molecular affinities of antiviral compounds with aforesaid target proteins. The top-scoring conformations identified through docking analysis were further validated by 100 ns molecular dynamic (MD) simulation run. The stability of the conformation was studied in detail by investigating the binding free energy using MM-PBSA method. Finally, the binding affinities of all the compounds were also compared with a reference ligand, remdesivir, against the target protein RdRp. Additionally, pharmacophore features, 3D structure alignment of potent compounds and Bayesian machine learning model were also used to support the MDc and MD simulation. Overall, the study emphasized that curcumin possesses a strong binding ability with host-specific receptors, furin and ACE2. In contrast, gingerol has shown strong interactions with spike protein, and RdRp and quercetin with main protease (M^pro) of SARS-CoV-2. In fact, all these target proteins play an essential role in mediating viral replication, and therefore, compounds targeting aforesaid target proteins are expected to block the viral replication and transcription. Overall, gingerol, curcumin and quercetin own multitarget binding ability that can be used alone or in combination to enhance therapeutic efficacy against COVID-19. The obtained results encourage further in vitro and in vivo investigations and also support the traditional use of antiviral plants preventively.

A probable means to an end: exploring P131 pharmacophoric scaffold to identify potential inhibitors of Cryptosporidium parvum inosine monophosphate dehydrogenase

Compound P131 has been established to inhibit Cryptosporidium parvum's inosine monophosphate dehydrogenase (CpIMPDH). Its inhibitory activity supersedes that of paromomycin, which is extensively used in treating cryptosporidiosis. Through the per-residue energy decomposition approach, crucial moieties of P131 were identified and subsequently adopted to create a pharmacophore model for virtual screening in the ZINC database. This search generated eight ADMET-compliant hits that were examined thoroughly to fit into the active site of CpIMPDH via molecular docking. Three compounds ZINC46542062, ZINC58646829, and ZINC89780094, with favorable docking scores of - 8.3 kcal/mol, - 8.2 kcal/mol, and - 7.5 kcal/mol, were selected. The potential inhibitory mechanism of these compounds was probed using molecular dynamics simulation and Molecular Mechanics Generalized Poisson Boltzmann Surface Area (MM/PBSA) analyses. Results revealed that one of the hits (ZINC46542062) exhibited a lower binding free energy of - 39.52 kcal/mol than P131, which had - 34.6 kcal/mol. Conformational perturbation induced by the binding of the identified hits to CpIMPDH was similar to P131, suggesting a similarity in inhibitory mechanisms. Also, in silico investigation of the properties of the hit compounds implied superior physicochemical properties with regards to their synthetic accessibility, lipophilicity, and number of hydrogen bond donors and acceptors in comparison with P131. ZINC46542062 was identified as a promising hit compound with the highest binding affinity to the target protein and favorable physicochemical and pharmacokinetic properties relative to P131. The identified compounds can serve as a basis for conducting further experimental investigations toward the development of anticryptosporidials, which can overcome the challenges of existing therapeutic options. Graphical abstract P131 and the identified compounds docked in the NAD⁺ binding site of Cryptosporidium parvum IMPDH.

The antimicrobial potential and pharmacokinetic profiles of novel quinoline-based scaffolds: synthesis and in silico mechanistic studies as dual DNA gyrase and DHFR inhibitors

The resistance of pathogenic microbes to currently available antimicrobial agents has been considered a global alarming concern.

Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles

Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.

Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens

In this review, applications of various molecular modelling methods in the study of estrogens and xenoestrogens are summarized. Selected biomolecules that are the most commonly chosen as molecular modelling objects in this field are presented. In most of the reviewed works, ligand docking using solely force field methods was performed, employing various molecular targets involved in metabolism and action of estrogens. Other molecular modelling methods such as molecular dynamics and combined quantum mechanics with molecular mechanics have also been successfully used to predict the properties of estrogens and xenoestrogens. Among published works, a great number also focused on the application of different types of quantitative structure-activity relationship (QSAR) analyses to examine estrogen's structures and activities. Although the interactions between estrogens and xenoestrogens with various proteins are the most commonly studied, other aspects such as penetration of estrogens through lipid bilayers or their ability to adsorb on different materials are also explored using theoretical calculations. Apart from molecular mechanics and statistical methods, quantum mechanics calculations are also employed in the studies of estrogens and xenoestrogens. Their applications include computation of spectroscopic properties, both vibrational and Nuclear Magnetic Resonance (NMR), and also in quantum molecular dynamics simulations and crystal structure prediction. The main aim of this review is to present the great potential and versatility of various molecular modelling methods in the studies on estrogens and xenoestrogens.

Design, synthesis and biological activities of piperidine-spirooxadiazole derivatives as α7 nicotinic receptor antagonists

α: 7 nicotinic acetylcholine receptors (nAChRs) expressed in the nervous and immune systems have been suggested to play important roles in the control of inflammation. However, the lack of antagonist tools specifically inhibiting α7 nAChR impedes the validation of the channel as therapeutic target. To discover a selective α7 antagonist, we started a pharmacophore-based virtual screening and identified a piperidine-spirooxadiazole derivative T761-0184 that acts as a α7 antagonist. A series of novel piperidine-spirooxadiazole derivatives were subsequently synthesized and evaluated using two-electrode voltage clamp (TEVC) assay in Xenopus oocytes. Lead compounds from two series inhibited α7 with their IC₅₀ values ranging from 3.3 μM to 13.7 μM. Compound B10 exhibited α7 selectivity over other α4β2 and α3β4 nAChR subtypes. The analysis of structure-activity relationship (SAR) provides valuable insights for further development of selective α7 nAChR antagonists.

Identification of potential modulator of Anopheles gambiae odorant binding protein 1 by hierarchical virtual screening and molecular dynamics

Malaria is a protozoan infection transmitted by the bite of the infected female mosquito belonging to the genus Anopheles spp., which causes more than 445 million annual deaths worldwide. Available drugs have serious adverse effects (e.g. blurred vision, hypotension and headache) and species-dependent efficacy. An alternative to overcome these problems involve the use of molecules with affinity to the Anopheles gambiae mosquito odor receptors, minimizing the reinfection process as well as reducing the problems related to pharmacological therapy. The vector control can interrupt the epidemiological cycle and, therefore, control the malaria incidence. In the olfactory pathway, odorant binding protein 1 acts on the first level of odor recognition on malarial vector and thus can be used to modulate mosquito behavior and development of new attracts or repellents. Thus, this study applied ligand-based (2D-chemical similarity) and structure-based (docking and molecular dynamics) computational approaches to prioritize potential olfactory modulators on natural products catalogs at ZINC¹⁵ database (n = 98,379). Hierarchical virtual screening prioritized a potential olfactory modulator (Z8217) against Anopheles gambiae odorant binding protein 1 (AgOBP1). Next, it was submitted to molecular dynamics routine to identify structural requirements and the interactions profile required for binding-site affinity. This promising natural compound can interact like experimental ligand and will be used in repellency assay to confirm its sensorial behavior.Communicated by Ramaswamy H. Sarma.

Computer-Driven Development of an in Silico Tool for Finding Selective Histone Deacetylase 1 Inhibitors

Histone deacetylases (HDACs) are a class of epigenetic modulators overexpressed in numerous types of cancers. Consequently, HDAC inhibitors (HDACIs) have emerged as promising antineoplastic agents. Unfortunately, the most developed HDACIs suffer from poor selectivity towards a specific isoform, limiting their clinical applicability. Among the isoforms, HDAC1 represents a crucial target for designing selective HDACIs, being aberrantly expressed in several malignancies. Accordingly, the development of a predictive in silico tool employing a large set of HDACIs (aminophenylbenzamide derivatives) is herein presented for the first time. Software Phase was used to derive a 3D-QSAR model, employing as alignment rule a common-features pharmacophore built on 20 highly active/selective HDAC1 inhibitors. The 3D-QSAR model was generated using 370 benzamide-based HDACIs, which yielded an excellent correlation coefficient value (R² = 0.958) and a satisfactory predictive power (Q² = 0.822; Q²_F3 = 0.894). The model was validated (r²_{ext_ts} = 0.794) using an external test set (113 compounds not used for generating the model), and by employing a decoys set and the receiver-operating characteristic (ROC) curve analysis, evaluating the Güner-Henry score (GH) and the enrichment factor (EF). The results confirmed a satisfactory predictive power of the 3D-QSAR model. This latter represents a useful filtering tool for screening large chemical databases, finding novel derivatives with improved HDAC1 inhibitory activity.

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches

Adenosine Receptor Type 2A (A_2AAR) plays a role in important processes, such as anti-inflammatory ones. In this way, the present work aimed to search for compounds by pharmacophore-based virtual screening. The pharmacokinetic/toxicological profiles of the compounds, as well as a robust QSAR, predicted the binding modes via molecular docking. Finally, we used molecular dynamics to investigate the stability of interactions from ligand-A_2AAR. For the search for A_2AAR agonists, the UK-432097 and a set of 20 compounds available in the BindingDB database were studied. These compounds were used to generate pharmacophore models. Molecular properties were used for construction of the QSAR model by multiple linear regression for the prediction of biological activity. The best pharmacophore model was used by searching for commercial compounds in databases and the resulting compounds from the pharmacophore-based virtual screening were applied to the QSAR. Two compounds had promising activity due to their satisfactory pharmacokinetic/toxicological profiles and predictions via QSAR (Diverset 10002403 pEC₅₀ = 7.54407; ZINC04257548 pEC₅₀ = 7.38310). Moreover, they had satisfactory docking and molecular dynamics results compared to those obtained for Regadenoson (Lexiscan^®), used as the positive control. These compounds can be used in biological assays (in vitro and in vivo) in order to confirm the potential activity agonist to A_2AAR.

Dual-targeted hit identification using pharmacophore screening

Mycobacterium tuberculosis infection remains a major cause of global morbidity and mortality due to the increase of antibiotics resistance. Dual/multi-target drug discovery is a promising approach to overcome bacterial resistance. In this study, we built ligand-based pharmacophore models and performed pharmacophore screening in order to identify hit compounds targeting simultaneously two enzymes-M. tuberculosis leucyl-tRNA synthetase (LeuRS) and methionyl-tRNA synthetase (MetRS). In vitro aminoacylation assay revealed five compounds from different chemical classes inhibiting both enzymes. Among them the most active compound-3-(3-chloro-4-methoxy-phenyl)-5-[3-(4-fluoro-phenyl)-[1,2,4]oxadiazol-5-yl]-3H-[1,2,3]triazol-4-ylamine (1) inhibits mycobacterial LeuRS and MetRS with IC₅₀ values of 13 µM and 13.8 µM, respectively. Molecular modeling study indicated that compound 1 has similar binding mode with the active sites of both aminoacyl-tRNA synthetases and can be valuable compound for further chemical optimization in order to find promising antituberculosis agents.

Identification of Essential 2D and 3D Chemical Features for Discovery of the Novel Tubulin Polymerization Inhibitors

Background:

Tubulin polymerization inhibitors interfere with microtubule assembly and their functions lead to mitotic arrest, therefore they are attractive target for design and development of novel anticancer compounds.

Objective:

The proposed novel and effective structures following the use of three-dimensionalquantitative structure activity relationship (3D-QSAR) pharmacophore based virtual screening clearly demonstrate the high efficiency of this method in modern drug discovery.

Method:

Combined computational approach was applied to extract the essential 2D and 3D features requirements for higher activity as well as identify new anti-tubulin agents.

Results:

The best quantitative pharmacophore model, Hypo1, exhibited good correlation of 0.943 (RMSD=1.019) and excellent predictive power in the training set compounds. Generated model AHHHR, was well mapped to colchicine site and three-dimensional spatial arrangement of their features were in good agreement with the vital interactions in the active site. Total prediction accuracy (0.92 for training set and 0.86 for test set), enrichment factor (4.2 for training set and 4.5 for test set) and the area under the ROC curve (0.86 for training set and 0.94 for the test set), the developed model using Extended Class FingerPrints of maximum diameter 4 (ECFP_4) was chosen as the best model.

Conclusion:

Developed computational platform provided a better understanding of requirement features for colchicine site inhibitors and we believe the results of this study might be useful for the rational design and optimization of new inhibitors.

TASK Channels Pharmacology: New Challenges in Drug Design

Rational drug design targeting ion channels is an exciting and always evolving research field. New medicinal chemistry strategies are being implemented to explore the wild chemical space and unravel the molecular basis of the ion channels modulators binding mechanisms. TASK channels belong to the two-pore domain potassium channel family and are modulated by extracellular acidosis. They are extensively distributed along the cardiovascular and central nervous systems, and their expression is up- and downregulated in different cancer types, which makes them an attractive therapeutic target. However, TASK channels remain unexplored, and drugs designed to target these channels are poorly selective. Here, we review TASK channels properties and their known blockers and activators, considering the new challenges in ion channels drug design and focusing on the implementation of computational methodologies in the drug discovery process.

Hybrid approach to sieve out natural compounds against dual targets in Alzheimer's Disease

Excess Aβ production by the key protease BACE1, results in Aβ aggregation, forming amyloid plaques, all of which contribute to the pathogenesis of Alzheimer’s disease. Besides the multi-factorial nature of the disease, the diversity in the size and shape of known ligands that bind to the active site of BACE1, that is the flexibility of the enzyme, pose a serious challenge for the identification of drug candidates. To address the issue of receptor flexibility we have carried out ensemble docking with multiple receptor conformations. Therein, two representative structures each from closed and semi-open BACE1 conformations were selected for virtual screening to identify compounds that bind to the active site of both the conformations. These outperformed compounds were ranked using pharmacophore models generated by a ligand-based approach, for the identification of BACE1 inhibitors. The inhibitors were further predicted for anti-amyloidogenic activity using a QSAR model already established by our group thus enlisting compounds with dual potency. BACE1 inhibitory and anti-amyloidogenic activity for the commercially available compounds were validated using in vitro studies. Thus, incorporation of receptor flexibility in BACE1 through ensemble docking in conjunction with structure and ligand-based approach for screening might act as an effective protocol for obtaining promising scaffolds against AD.

Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations-whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico⁻chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties.

Implementation of a Pipeline Using Disease-Disease Associations for Computational Drug Repurposing

Drug repurposing is a powerful technique which has been recently employed in both industry and academia to discover and validate previously approved drugs for new indications. It provides the quickest possible transition from bench to bedside. In essence, computational strategies are appealing because they putatively nominate the most encouraging candidate for a given indication. A wide range of computational methods exist for repositioning. In this chapter we present the guidelines for performing integrated drug repurposing strategy by combining disease-disease association and molecular simulation analysis.

Pharmacophore-based virtual screening for identification of potential selective inhibitors of human histone deacetylase 6

Histone deacetylase (HDAC) 6 plays a role in oncogenic transformation and cancer metastasis via tubulin deacetylation, making it a critical target for anticancer drug design. However, lack of selectivity shown by many of the current HDAC6 inhibitors in clinical use and trials prompts the continuous search for selective inhibitors. Here, 10 pharmacophore hypotheses were developed based on the 3D common features of training set of 20 HDAC inhibitors in clinical use and trials. The hypotheses were validated using a test set of another 20 HDAC inhibitors along with 400 inactive (decoys) molecules based on Güner-Henry pharmacophore scoring method. Hypothesis 1 consisting of 1 H-bond donor, 1 H-bond acceptor and 2 hydrophobic features, was used to screen "DruglikeDiverse" database using Biovia Discovery Studio 4.5. The top 10 hit compounds were selected based on the pharmacophore fit values (>3.00). Their binding affinity against HDAC6 compared to class I HDACs (1, 2, 3 & 8) and a class IIa member (HDAC7), was calculated by molecular docking using AutoDock4. The stability of binding modes of 2 potential HDAC6-selective inhibitors (ENA501965 and IBS399024) was examined by 30 ns-molecular dynamics (MD) simulation using nanoscale MD (NAMD) software. Both ligands showed potential stability in HDAC6 active site over time. Therefore, these may provide additional scaffolds for further optimization towards the design of safe, potent and selective HDAC6 inhibitors.

Pharmacophore-based virtual screening for identifying β5 subunit inhibitor of 20S proteasome

Proteasomal system plays an important role in maintaining cell homeostatis. Overexpression of proteasomes leads to several major diseases, such as cancer and autoimmune disorder. The β5 subunit of proteasome is a crucial active site in proteolysis, and targeting proteasome β5 subunit is essential for proteasome inhibition. In the present study, a pharmacophore-based virtual screening and molecular docking were employed to identify ligands as inhibitors of β5 subunit of proteasome. The pharmacophore features were built with one hydrogen bond donor, two hydrogen bond acceptors, and one hydrophobic feature using native ligand of proteasome (HU10), which was then used to screen ZINC database using ZINCPharmer. The retrieved virtual hits were subjected to molecular docking analysis using iDock. The best six hits were subjected to molecular dynamics (MD) simulation and each complex was stable during 40 ns MD simulation as indicated by root-mean-square-deviation (RMSD) and root-mean-square-fluctuation (RMSF) values. The current study identifies 5 best hits having better binding potentials than HU10 as predicted by molecular mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method, i.e. Lig1540/ZINC33356240, Lig1546/ZINC33356235, Lig1522/ZINC20854878, Lig980/ZINC12391945, and Lig1119/ZINC19865241, which can be used in the development of new proteasome inhibitors.

An Integrative Drug Repurposing Pipeline: Switching Viral Drugs to Breast Cancer

The growing incidence rate of breast cancer, coupled with cellular chemotherapeutic resistance, has made this disease one of the most prevalent cancers among women worldwide. Despite the recent efforts to understand the underlying cause of the resistance due to mutation, there are no feasible tactics to overcome this bottleneck. This issue could be addressed by the concept of polypharmacology-disguising drugs present in the pharmacopeia for novel purposes (drug repurposing). Of note, we have proposed a multi-modal computational drug-repositioning stratagem to predict drugs possessing anti-proliferative effect. Our results suggest that Ombitasvir, a Hepatitis C NS5B polymerase inhibitor, could be "repurposed" for the control and prevention of beta-tubulin-driven breast cancers. J. Cell. Biochem. 118: 1412-1422, 2017. © 2016 Wiley Periodicals, Inc.

Development of pharmacophore models for small molecules targeting RNA: Application to the RNA repeat expansion in myotonic dystrophy type 1

RNA is an important drug target, but current approaches to identify bioactive small molecules have been engineered primarily for protein targets. Moreover, the identification of small molecules that bind a specific RNA target with sufficient potency remains a challenge. Computer-aided drug design (CADD) and, in particular, ligand-based drug design provide a myriad of tools to identify rapidly new chemical entities for modulating a target based on previous knowledge of active compounds without relying on a ligand complex. Herein we describe pharmacophore virtual screening based on previously reported active molecules that target the toxic RNA that causes myotonic dystrophy type 1 (DM1). DM1-associated defects are caused by sequestration of muscleblind-like 1 protein (MBNL1), an alternative splicing regulator, by expanded CUG repeats (r(CUG)^exp). Several small molecules have been found to disrupt the MBNL1-r(CUG)^exp complex, ameliorating DM1 defects. Our pharmacophore model identified a number of potential lead compounds from which we selected 11 compounds to evaluate. Of the 11 compounds, several improved DM1 defects both in vitro and in cells.