Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Building shape-focused pharmacophore models for effective docking screening

The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins' inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). SCIENTIFIC CONTRIBUTION: This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.

Discovery of a new Daboia russelli viper venom PLA2 inhibitor using virtual screening of pharmacophoric features of co-crystallized compound

Snake venom PLA2, a member of the group of hydrolase enzymes, has been recognized as a promising drug target for snake envenomation. In the present study, an attempt was made to identify potential inhibitors of snake venom PLA2 by employing a pharmacophore-based virtual screening, docking, and dynamics approach. A receptor-based pharmacophore model was generated based on the features of the established and bound co-crystal ligand (2-carbamoylmethyl-5-propyl-octahydro-indol-7-yl)-acetic acid in the PLA2 complex. The best pharmacophore model (ADDH) derived, consisted of four features, namely one hydrogen bond acceptor, two hydrogen bond donors, and one hydrophobic region. This common pharmacophore was then used to perform virtual screening against a drug-like diverse database, with due consideration to the Lipinski 'rule of five', so as to obtain a pool of lead molecules. The short-listed lead molecules were then subjected to docking analysis with that of the Daboia russelli viper venom PLA2 followed by a molecular simulation study for a duration of 100 ns. CAP04815700 was chosen as the best compound based on the simulation parameters, which were then taken for MM/PBSA calculation, and it was revealed that it has a similar effective inhibitory potential as that of the crystal ligand. Further, the cluster analysis also revealed the structural significance of the backbone protein after the interaction with CAP04815700. This study will continue to explore its bioactivity in vitro and in vivo.Communicated by Ramaswamy H. Sarma.

Computational Exploration of Potential Pharmacological Inhibitors Targeting the Envelope Protein of the Kyasanur Forest Disease Virus

The limitations of the current vaccination strategy for the Kyasanur Forest Disease virus (KFDV) underscore the critical need for effective antiviral treatments, highlighting the crucial importance of exploring novel therapeutic approaches through in silico drug design. Kyasanur Forest Disease, caused by KFDV, is a tick-borne disease with a mortality of 3-5% and an annual incidence of 400 to 500 cases. In the early stage of infection, the envelope protein plays a crucial role by facilitating host-virus interactions. The objective of this research is to develop effective antivirals targeting the envelope protein to disrupt the virus-host interaction. In line with this, the 3D structure of the envelope protein was modeled and refined through molecular modeling techniques, and subsequently, ligands were designed via de novo design and pharmacophore screening, yielding 12 potential hits followed by ADMET analysis. The top five candidates underwent geometry optimization and molecular docking. Notably, compounds L4 (SA28) and L3 (CNP0247967) are predicted to have significant binding affinities of -8.91 and -7.58 kcal/mol, respectively, toward the envelope protein, based on computational models. Both compounds demonstrated stability during 200 ns molecular dynamics simulations, and the MM-GBSA binding free-energy values were -85.26 ± 4.63 kcal/mol and -66.60 ± 2.92 kcal/mol for the envelope protein L3 and L4 complexes, respectively. Based on the computational prediction, it is suggested that both compounds have potential as drug candidates for controlling host-virus interactions by targeting the envelope protein. Further validation through in-vitro assays would complement the findings of the present in silico investigations.

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.

In silico and in vitro assessment of drugs potentially causing adverse effects by inhibiting CYP17A1

Cytochrome P450 enzymes (CYPs) play a crucial role in the metabolism and synthesis of various compound classes. While drug-metabolizing CYP enzymes are frequently investigated as anti-targets, the inhibition of CYP enzymes involved in adrenal steroidogenesis is not well studied. The steroidogenic enzyme CYP17A1 is a dual-function enzyme catalyzing hydroxylase and lyase reactions relevant for the biosynthesis of adrenal glucocorticoids and androgens. Inhibition of CYP17A1-hydroxylase leads to pseudohyperaldosteronism with subsequent excessive mineralocorticoid receptor activation, hypertension and hypokalemia. In contrast, specific inhibition of the lyase function might be beneficial for the treatment of prostate cancer by decreasing adrenal androgen levels. This study combined in silico and in vitro methods to identify drugs inhibiting CYP17A1. The most potent CYP17A1 inhibitors identified are serdemetan, mocetinostat, nolatrexed, liarozole, and talarozole. While some of these drugs are currently under investigation for the treatment of various cancers, their potential for the treatment of prostate cancer is yet to be explored. The DrugBank database was screened for CYP17A1 inhibitors, to increase the awareness for the risk of drug-induced pseudohyperaldosteronism and to highlight drugs so far unknown for their potential to cause side effects resulting from CYP17A1 inhibition.

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.

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.

Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling

The study of transporter proteins is key to understanding the mechanism behind multidrug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.

Design, synthesis, and pharmacological evaluation of indazole carboxamides of N-substituted pyrrole derivatives as soybean lipoxygenase inhibitors

In this paper, we attempted to develop a novel class of compounds against lipoxygenase, a key enzyme in the biosynthesis of leukotrienes implicated in a series of inflammatory diseases. Given the absence of appropriate human 5-lipoxygenase crystallographic data, solved soybean lipoxygenase-1 and -3 structures were used as a template to generate an accurate pharmacophore model which was further used for virtual screening purposes. Eight compounds (1-8) have been derived from the in-house library consisting of N-substituted pyrroles conjugated with 5- or 6-indazole moieties through a carboxamide linker. This study led to the discovery of hit molecule 8 bearing a naphthyl group with the IC50 value of 22 μM according to soybean lipoxygenase in vitro assay. Isosteric replacement of naphthyl ring with quinoline moieties and reduction of carbonyl carboxamide group resulted in compounds 9-12 and 13, respectively. Compound 12 demonstrated the most promising enzyme inhibition. In addition, compounds 8 and 12 were found to reduce the carrageenan-induced paw edema in vivo by 52.6 and 49.8%, respectively. In view of the encouraging outcomes concerning their notable in vitro and in vivo anti-inflammatory activities, compounds 8 and 12 could be further optimized for the discovery of novel 5-lipoxygenase inhibitors in future. A structure-based 3D pharmacophore model was used in the virtual screening of in-house library to discover novel potential 5-lipoxygenase inhibitors.

Chemometric assisted natural DES based VA-DLLME-LC-MS/MS method for the quantitative determination of Garcinol in biofluids/tissues: A practical application to pharmacokinetics and biodistribution studies

Garcinol (GAR) is a polyisoprenylated benzophenone obtained from Garcinia indica used as anti-oxidant and anti-inflammatory in traditional medicine and due to these activities, it possesses anticancer properties. It is considered to be a next generation epigenetic drug. A green solvent based analytical method which is efficient, sophisticated, and highly enriched has been developed for the quantitative analysis of GAR in biological samples (plasma, liver, kidney and spleen) with the use of deep eutectic solvent (DES) for its extraction. A series of 23 DESs were synthesized and out of which, Thymol (Th)-Terpeniol (T), 2:1 molar ratio with a more hydrophobic environment and high interaction efficiency between GAR and DES was identified for the better extraction from mice plasma and tissue samples. The Design of Experiment approaches like placket-burmann design and central composite design were used to optimize the method conditions. The method validation characteristics, such as limit of detection (0.193-0.237 ng/mL), limit of quantification (0.644-0.697 ng/mL), lower limit of quantification (0.5 ng/mL), broad range of linearity with R2 (0.9994-0.9997) with a percent recovery not less than 87% was observed, which are well within the acceptance criteria for a bioanalytical method. The enrichment factor is upto 53-60 folds, with high extraction efficiency (89-97%). The measurement uncertainty was estimated with an expanded uncertainty ranged between 10.9%-19.0%. The method developed and validated was effectively applied to examine the pharmacokinetic and biodistribution patterns for GAR in mice.

Pharmacophore-based virtual screening, molecular docking, and molecular dynamics studies for the discovery of novel FLT3 inhibitors

FLT3 is considered a potential target of acute myeloid leukemia therapy. In this study, we applied a computer-aided methodology unifying molecular docking and pharmacophore screening to identify potent inhibitors against FLT3. To investigate the pharmacophore area and binding mechanism of FLT3, the reported co-crystallized Gilteritinib ligand was docked into the active site using Glide XP. Based on the docking results, we identified structure-based pharmacophore characteristics resistant to potent FLT3 inhibitors. The best hypothesis was corroborated using test and decoy sets, and the verified hypo was utilized to screen the chemical database. The hits from the pharmacophore-based screening were then screened again using a structure-based method that included molecular docking at various precisions; the selected molecules were further examined and refined using drug-like filters and ADMET analysis. Finally, two hits were picked out for molecular dynamic simulation. The results showed two hits were expected to have potent inhibitory activity and excellent ADMET characteristics, and they might be used as new leads in the development of FLT3 inhibitors.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.

Identification of Promising Sulfonamide Chalcones as Inhibitors of SARS-CoV-2 3CLpro through Structure-Based Virtual Screening and Experimental Approaches

3CLpro is a viable target for developing antiviral therapies against the coronavirus. With the urgent need to find new possible inhibitors, a structure-based virtual screening approach was developed. This study recognized 75 pharmacologically bioactive compounds from our in-house library of 1052 natural product-based compounds that satisfied drug-likeness criteria and exhibited good bioavailability and membrane permeability. Among these compounds, three promising sulfonamide chalcones were identified by combined theoretical and experimental approaches, with SWC423 being the most suitable representative compound due to its competitive inhibition and low cytotoxicity in Vero E6 cells (EC50 = 0.89 ± 0.32 μM; CC50 = 25.54 ± 1.38 μM; SI = 28.70). The binding and stability of SWC423 in the 3CLpro active site were investigated through all-atom molecular dynamics simulation and fragment molecular orbital calculation, indicating its potential as a 3CLpro inhibitor for further SARS-CoV-2 therapeutic research. These findings suggested that inhibiting 3CLpro with a sulfonamide chalcone such as SWC423 may pave the effective way for developing COVID-19 treatments.

Assessment of the inhibitory potential of anabolic steroids towards human AKR1D1 by computational methods and in vitro evaluation

Exposure to xenobiotics can adversely affect biochemical reactions, including hepatic bile acid synthesis. Bile acids are essential for dissolving lipophilic compounds in the hydrophilic environment of the gastrointestinal tract. The critical micellar concentration of bile acids depends on the Δ4-reduction stereochemistry, with the 3-oxo-5β-steroid-Δ4-dehydrogenase (AKR1D1) introducing the cis ring A/B conformation. Loss-of-function mutations in AKR1D1 cause hepatic cholestasis, which, if left untreated can progress into steatosis and liver cirrhosis. Furthermore, AKR1D1 is involved in clearing steroids with an A-ring Δ4-double bond. Here, we tested whether anabolic-androgenic steroids (AAS), often taken off-label at high doses, might inhibit AKR1D1, thereby potentially causing hepatotoxicity. A computational molecular model was established and used for virtual screening of the DrugBank database consisting of 2740 molecules, yielding mainly steroidal hits. Fourteen AAS were selected for in vitro evaluation, as such compounds can reach high hepatic concentrations in an abuse situation. Nandrolone, clostebol, methasterone, drostanolone, and methenolone inhibited to various extent the AKR1D1-mediated reduction of testosterone. Molecular modeling suggests that 9 out of 14 investigated AAS are competitive inhibitors. Moreover quantum mechanical calculations show that nadrolone and clostebol are substrates of AKR1D1 with different activation energy barriers for the hydrogen transfer from cofactor to the C5 position affecting their turnover. In this multidisciplinary approach, we established a molecular model of AKR1D1, identified several AAS as inhibitors, and described their binding mode. This approach may be applied to study other classes of inhibitors including non-steroidal compounds.

Selection of potential natural compounds for poly-ADP-ribose polymerase (PARP) inhibition in glioblastoma therapy by in silico screening methods

Glioblastoma (GBM), the most prevalent brain tumor, is one of the least treatable malignancies due to its propensity for intracranial spread, high proliferative potential, and innate resistance to radiation and chemotherapy. Current GBM therapy is limited due to unfavorable, non-specific therapeutic effects in healthy cells and the difficulty of small molecules to penetrate the blood brain barrier (BBB) and reach the tumor microenvironment. Adding PARP-1 inhibitors inhibit DNA repair enzymes thereby increasing the cytotoxicity of anticancer agents. Hence, we aimed to discover potential naturally occurring PARP-1 inhibitors that can be utilized in the treatment of glioma by using multiple in silico tools like molecular docking, absorption, distribution, metabolism, and excretion (ADME) profile, pharmacophore modeling, and molecular dynamic (MD) simulations. Among 43 phytocompounds we screened, two of them (Ellagic acid and Naringin) were discovered to be bound to the catalytic site of PARP-1 with an affinity more remarkable than commercially available PARP-1 inhibitors (Talazoparib, Niraparib, and Rucaparib) except Olaparib. The molecular interactions were analyzed, and data shows that bound entity attained a conserved domain via hydrogen bond interactions, polar interactions, and π-π stacking. Pharmacophore modeling studies showed electronic and steric features of ligands responsible for supramolecular interaction with PARP-1. ADME properties were studied, to assess drug-likeness, hydrophilic nature, hydrophobicity, brain permeability, and oral bioavailability of the natural PARP-1 inhibitors. The simulation study demonstrated the development of a stable complex between Naringin, Ellagic acid, and PARP-1 protein. Moreover, cell culture studies and animal investigations are essential to determine pharmacokinetics and pharmacodynamics.

Advances in the discovery of new chemotypes through ultra-large library docking

INTRODUCTION

The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (M^pro).

AREAS COVERED

This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel M^pro inhibitors targeting the SARS-CoV-2 virus.

EXPERT OPINION

With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.

Combination of In Silico and In Vitro Screening to Identify Novel Glutamate Carboxypeptidase II Inhibitors

Glutamate carboxypeptidase II (GCPII) is a metalloprotease implicated in neurological diseases and prostate oncology. While several classes of potent GCPII-specific inhibitors exist, the development of novel active scaffolds with different pharmacological profiles remains a challenge. Virtual screening followed by in vitro testing is an effective means for the discovery of novel active compounds. Structure- and ligand-based pharmacophore models were created based on a dataset of known GCPII-selective ligands. These models were used in a virtual screening of the SPECS compound library (∼209.000 compounds). Fifty top-scoring virtual hits were further experimentally tested for their ability to inhibit GCPII enzymatic activity in vitro. Six hits were found to have moderate to high inhibitory potency with the best virtual hit, a modified xanthene, inhibiting GCPII with an IC50 value of 353 ± 24 nM. The identification of this novel inhibitory scaffold illustrates the applicability of pharmacophore-based modeling for the discovery of GCPII-specific inhibitors.

Inhibiting Leishmania donovani Sterol Methyltransferase to Identify Lead Compounds Using Molecular Modelling

The recent outlook of leishmaniasis as a global public health concern coupled with the reportage of resistance and lack of efficacy of most antileishmanial drugs calls for a concerted effort to find new leads. The study combined In silico and in vitro approaches to identify novel potential synthetic small-molecule inhibitors targeting the Leishmania donovani sterol methyltransferase (LdSMT). The LdSMT enzyme in the ergosterol biosynthetic pathway is required for the parasite’s membrane fluidity, distribution of membrane proteins, and control of the cell cycle. The lack of LdSMT homologue in the human host and its conserved nature among all Leishmania parasites makes it a viable target for future antileishmanial drugs. Initially, six known inhibitors of LdSMT with IC50 < 10 μM were used to generate a pharmacophore model with a score of 0.9144 using LigandScout. The validated model was used to screen a synthetic library of 95,630 compounds obtained from InterBioScreen limited. Twenty compounds with pharmacophore fit scores above 50 were docked against the modelled three-dimensional structure of LdSMT using AutoDock Vina. Consequently, nine compounds with binding energies ranging from −7.5 to −8.7 kcal/mol were identified as potential hit molecules. Three compounds comprising STOCK6S-06707, STOCK6S-84928, and STOCK6S-65920 with respective binding energies of −8.7, −8.2, and −8.0 kcal/mol, lower than 22,26-azasterol (−7.6 kcal/mol), a known LdSMT inhibitor, were selected as plausible lead molecules. Molecular dynamics simulation studies and molecular mechanics Poisson–Boltzmann surface area calculations showed that the residues Asp25 and Trp208 were critical for ligand binding. The compounds were also predicted to have antileishmanial activity with reasonable pharmacological and toxicity profiles. When the antileishmanial activity of the three hits was evaluated in vitro against the promastigotes of L. donovani, mean half-maximal inhibitory concentrations (IC50) of 21.9 ± 1.5 μM (STOCK6S-06707), 23.5 ± 1.1 μM (STOCK6S-84928), and 118.3 ± 5.8 μM (STOCK6S-65920) were obtained. Furthermore, STOCK6S-84928 and STOCK6S-65920 inhibited the growth of Trypanosoma brucei, with IC50 of 14.3 ± 2.0 μM and 18.1 ± 1.4 μM, respectively. The identified compounds could be optimised to develop potent antileishmanial therapeutic agents.

Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

Arabinofuranosyl Thymine Derivatives-Potential Candidates against Cowpox Virus: A Computational Screening Study

Cowpox is caused by a DNA virus known as the cowpox virus (CPXV) belonging to the Orthopoxvirus genus in the family Poxviridae. Cowpox is a zoonotic disease with the broadest host range among the known poxviruses. The natural reservoir hosts of CPXV are wild rodents. Recently, the cases of orthopoxviral infections have been increasing worldwide, and cowpox is considered the most common orthopoxviral infection in Europe. Cowpox is often a self-limiting disease, although cidofovir or anti-vaccinia gammaglobulin can be used in severe and disseminated cases of human cowpox. In this computational study, a molecular docking analysis of thymine- and arabinofuranosyl-thymine-related structures (1-21) on two cowpox-encoded proteins was performed with respect to the cidofovir standard and a 3D ligand-based pharmacophore model was generated. Three chemical structures (PubChem IDs: 123370001, 154137224, and 90413364) were identified as potential candidates for anti-cowpox agents. Further studies combining in vitro and in silico molecular dynamics simulations to test the stability of these promising compounds could effectively improve the future design of cowpox virus inhibitors, as molecular docking studies are not sufficient to consider a ligand a potential drug.

Modern drug discovery for inflammatory bowel disease: The role of computational methods

Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided (in silico) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.

Exploiting butyrylcholinesterase inhibitors through a combined 3-D pharmacophore modeling, QSAR, molecular docking, and molecular dynamics investigation†

Alzheimer's disease (AD), a neurodegenerative condition associated with ageing, can occur. AD gradually impairs memory and cognitive function, which leads to abnormal behavior, incapacity, and reliance. By 2050, there will likely be 100 million cases of AD in the world's population. Acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibition are significant components of AD treatment. This work developed models using the genetic method multiple linear regression, atom-based, field-based, and 3-D pharmacophore modelling. Due to internal and external validation, all of the models have solid statistical (R² > 0.81 and Q² > 0.77) underpinnings. From a pre-plated CNS library (6055), we discovered a hit compound using virtual screening on a QSAR model. Through molecular docking, additional hit compounds were investigated (XP mode). Finally, a molecular dynamics simulation revealed that the Molecule5093-4BDS complex was stable (100 ns). Finally, the expected ADME properties for the hit compounds (Molecule5093, Molecule1076, Molecule4412, Molecule1053, and Molecule3344) were found. According to the results of our investigation and the prospective hit compounds, BuChE inhibitors may be used as a treatment for AD.

Alzheimer's disease (AD), a neurodegenerative condition associated with ageing, can occur.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors

Targeting SARS-CoV-2 papain-like protease using inhibitors is a suitable approach for inhibition of virus replication and dysregulation of host anti-viral immunity. Engaging all five binding sites far from the catalytic site of PLpro is essential for developing a potent inhibitor. We developed and validated a structure-based pharmacophore model with 9 features of a potent PLpro inhibitor. The pharmacophore model-aided virtual screening of the comprehensive marine natural product database predicted 66 initial hits. This hit library was downsized by filtration through a molecular weight filter of ≤ 500 g/mol. The 50 resultant hits were screened by comparative molecular docking using AutoDock and AutoDock Vina. Comparative molecular docking enables benchmarking docking and relieves the disparities in the search and scoring functions of docking engines. Both docking engines retrieved 3 same compounds at different positions in the top 1 % rank, hence consensus scoring was applied, through which CMNPD28766, aspergillipeptide F emerged as the best PLpro inhibitor. Aspergillipeptide F topped the 50-hit library with a pharmacophore-fit score of 75.916. Favorable binding interactions were predicted between aspergillipeptide F and PLpro similar to the native ligand XR8-24. Aspergillipeptide F was able to engage all the 5 binding sites including the newly discovered BL2 groove, site V. Molecular dynamics for quantification of Cα-atom movements of PLpro after ligand binding indicated that it exhibits highly correlated domain movements contributing to the low free energy of binding and a stable conformation. Thus, aspergillipeptide F is a promising candidate for pharmaceutical and clinical development as a potent SARS-CoV-2 PLpro inhibitor.

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches

Background

The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches.

Methods

We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control.

Results

From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside.

Conclusions

Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-022-03686-y.

Synthesis and Theoretical Studies of Biologically Active Thieno Nucleus Incorporated Tri and Tetracyclic Nitrogen Containing Heterocyclics Scaffolds via Suzuki Cross-Coupling Reaction

A new series of thieno nucleus embellished trinuclear (19, 20) and tetranuclear (21-24) nitrogen heteroaryl have been synthesized by the Suzuki cross-coupling reaction using bis(triphenylphosphine)palladium(II) dichloride. All the synthesized compounds were characterized by IR, ¹ H-NMR, ¹³ CNMR and Mass spectral properties. In vitro antibacterial studies of the synthesized compound were conducted using broth microdilution assay employing Gram-positive and Gram-negative strains and half-maximal inhibitory concentration (IC₅₀ ) was determined. The result showed that compound 20 possess best antibacterial activity against S. aureus and E. coli with IC₅₀ values of 60 μg mL^-1 and 90 μg mL^-1 . Further to determine the mode of antibacterial action, compounds 20 and 21 were examined for in vitro bacterial dehydrogenase inhibitory assay. To understand the binding affinity of the synthesized compounds, the docking study was performed in the bacterial dehydrogenase enzyme by AutoDock Vina software and structure was confirmed by Discovery Studio Visualizer. All the synthesized compounds were docked in a good manner within the active sites of the bacterial dehydrogenase enzyme and exhibited good binding energies.

Discovery of Novel HIV Protease Inhibitors Using Modern Computational Techniques

The human immunodeficiency virus type 1 (HIV-1) has continued to be a global concern. With the new HIV incidence, the emergence of multi-drug resistance and the untoward side effects of currently used anti-HIV drugs, there is an urgent need to discover more efficient anti-HIV drugs. Modern computational tools have played vital roles in facilitating the drug discovery process. This research focuses on a pharmacophore-based similarity search to screen 111,566,735 unique compounds in the PubChem database to discover novel HIV-1 protease inhibitors (PIs). We used an in silico approach involving a 3D-similarity search, physicochemical and ADMET evaluations, HIV protease-inhibitor prediction (IC50/percent inhibition), rigid receptor-molecular docking studies, binding free energy calculations and molecular dynamics (MD) simulations. The 10 FDA-approved HIV PIs (saquinavir, lopinavir, ritonavir, amprenavir, fosamprenavir, atazanavir, nelfinavir, darunavir, tipranavir and indinavir) were used as reference. The in silico analysis revealed that fourteen out of the twenty-eight selected optimized hit molecules were within the acceptable range of all the parameters investigated. The hit molecules demonstrated significant binding affinity to the HIV protease (PR) when compared to the reference drugs. The important amino acid residues involved in hydrogen bonding and п-п stacked interactions include ASP25, GLY27, ASP29, ASP30 and ILE50. These interactions help to stabilize the optimized hit molecules in the active binding site of the HIV-1 PR (PDB ID: 2Q5K). HPS/002 and HPS/004 have been found to be most promising in terms of IC50/percent inhibition (90.15%) of HIV-1 PR, in addition to their drug metabolism and safety profile. These hit candidates should be investigated further as possible HIV-1 PIs with improved efficacy and low toxicity through in vitro experiments and clinical trial investigations.

Recent Scaffold Hopping Applications in Central Nervous System Drug Discovery

The concept of bioisosterism and the implementation of bioisosteric replacement is fundamental to medicinal chemistry. The exploration of bioisosteres is often used to probe key structural features of candidate pharmacophores and enhance pharmacokinetic properties. As the understanding of bioisosterism has evolved, capabilities to undertake more ambitious bioisosteric replacements have emerged. Scaffold hopping is a broadly used term in the literature referring to a variety of different bioisosteric replacement strategies, ranging from simple heterocyclic replacements to topological structural overhauls. In this work, we have highlighted recent applications of scaffold hopping in the central nervous system drug discovery space. While we have highlighted the benefits of using scaffold hopping approaches in central nervous system drug discovery, these are also widely applicable to other medicinal chemistry fields. We also recommend a shift toward the use of more refined and meaningful terminology within the realm of scaffold hopping.

Metabolite Profiling of the Environmental-Controlled Growth of Marsilea crenata Presl. and Its In Vitro and In Silico Antineuroinflammatory Properties

This study was aimed to evaluate the metabolite contents and antineuroinflammatory potential of Marsilea crenata Presl. grown under a controlled environmental condition. The antineuroinflammatory test has been carried out in vitro using ethanolic extract of M. crenata leaves on HMC3 microglia cells. An in silico approach was applied to predict the active compounds of the extract. The HMC3 microglia cells were induced with IFNγ to create prolonged inflammatory conditions and then treated with 96% ethanolic extract of the M. crenata leaves of 62.5, 125, and 250 μg/mL. The expression of MHC II was analyzed using the ICC method with the CLSM instrument. Metabolites of the extract were profiled using UPLC-QToF-MS/MS instrument and MassLynx 4.1 software. In silico evaluation was conducted with molecular docking on 3OLS protein using PyRx 0.8 software, and physicochemical properties of the compounds were analyzed using SwissADME webtool. The ethanolic extract of M. crenata leaves could reduce the MHC II expression in HMC3 microglia cells in all concentrations with the values 97.458, 139.574, and 82.128 AU. The result of metabolite profiling found 79 compounds in the extract. In silico evaluation showed that 19 compounds gave agonist interaction toward 3OLS, and three met all parameters of physicochemical analysis. The ethanolic extract of the environmental-controlled growth of M. crenata leaves antineuroinflammatory activity on HMC3 microglia cells. The extract was predicted to contain some phytoestrogen compounds which act as 3OLS agonists.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

Structure-based rational design of hydroxysteroid dehydrogenases for improving and diversifying steroid synthesis

A group of steroidogenic enzymes, hydroxysteroid dehydrogenases are involved in steroid metabolism which is very important in the cell: signaling, growth, reproduction, and energy homeostasis. The enzymes show an inherent function in the interconversion of ketosteroids and hydroxysteroids in a position- and stereospecific manner on the steroid nucleus and side-chains. However, the biocatalysis of steroids reaction is a vital and demanding, yet challenging, task to produce the desired enantiopure products with non-natural substrates or non-natural cofactors, and/or in non-physiological conditions. This has driven the use of protein design strategies to improve their inherent biosynthetic efficiency or activate their silent catalytic ability. In this review, the innate features and catalytic characteristics of enzymes based on sequence-structure-function relationships of steroidogenic enzymes are reviewed. Combining structure information and catalytic mechanisms, progress in protein redesign to stimulate potential function, for example, substrate specificity, cofactor dependence, and catalytic stability are discussed.

Identification of Novel Dopamine D2 Receptor Ligands—A Combined In Silico/In Vitro Approach

Diseases of the central nervous system are an alarming global problem showing an increasing prevalence. Dopamine receptor D2 (D2R) has been shown to be involved in central nervous system diseases. While different D2R-targeting drugs have been approved by the FDA, they all suffer from major drawbacks due to promiscuous receptor activity leading to adverse effects. Increasing the number of potential D2R-targeting drug candidates bears the possibility of discovering molecules with less severe side-effect profiles. In dire need of novel D2R ligands for drug development, combined in silico/in vitro approaches have been shown to be efficient strategies. In this study, in silico pharmacophore models were generated utilizing both ligand- and structure-based approaches. Subsequently, different databases were screened for novel D2R ligands. Selected virtual hits were investigated in vitro, quantifying their binding affinity towards D2R. This workflow successfully identified six novel D2R ligands exerting micro- to nanomolar (most active compound KI = 4.1 nM) activities. Thus, the four pharmacophore models showed prospective true-positive hit rates in between 4.5% and 12%. The developed workflow and identified ligands could aid in developing novel drug candidates for D2R-associated pathologies.

3D-QSAR-Based Pharmacophore Modeling, Virtual Screening, and Molecular Dynamics Simulations for the Identification of Spleen Tyrosine Kinase Inhibitors

Spleen tyrosine kinase (SYK) is an essential mediator of immune cell signaling and has been anticipated as a therapeutic target for autoimmune diseases, notably rheumatoid arthritis, allergic rhinitis, asthma, and cancers. Significant attempts have been undertaken in recent years to develop SYK inhibitors; however, limited success has been achieved due to poor pharmacokinetics and adverse effects of inhibitors. The primary goal of this research was to identify potential inhibitors having high affinity, selectivity based on key molecular interactions, and good drug-like properties than the available inhibitor, fostamatinib. In this study, a 3D-QSAR model was built for SYK based on known inhibitor IC₅₀ values. The best pharmacophore model was then used as a 3D query to screen a drug-like database to retrieve hits with novel chemical scaffolds. The obtained compounds were subjected to binding affinity prediction using the molecular docking approach, and the results were subsequently validated using molecular dynamics (MD) simulations. The simulated compounds were ranked according to binding free energy (ΔG), and the binding affinity was compared with fostamatinib. The binding mode analysis of selected compounds revealed that the hit compounds form hydrogen bond interactions with hinge region residue Ala451, glycine-rich loop residue Lys375, Ser379, and DFG motif Asp512. Identified hits were also observed to form a desirable interaction with Pro455 and Asn457, the rare feature observed in SYK inhibitors. Therefore, we argue that identified hit compounds ZINC98363745, ZINC98365358, ZINC98364133, and ZINC08789982 may help in drug design against SYK.

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.

Drug Design by Pharmacophore and Virtual Screening Approach

Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.

Molecular Mingling: Multimodal Predictions of Ligand Promiscuity in Pentameric Ligand-Gated Ion Channels

Background: Human pentameric ligand-gated ion channels (pLGICs) comprise nicotinic acetylcholine receptors (nAChRs), 5-hydroxytryptamine type 3 receptors (5-HT3Rs), zinc-activated channels (ZAC), γ-aminobutyric acid type A receptors (GABAARs) and glycine receptors (GlyRs). They are recognized therapeutic targets of some of the most prescribed drugs like general anesthetics, anxiolytics, smoking cessation aids, antiemetics and many more. Currently, approximately 100 experimental structures of pLGICs with ligands bound exist in the protein data bank (PDB). These atomic-level 3D structures enable the generation of a comprehensive binding site inventory for the superfamily and the in silico prediction of binding site properties. Methods: A panel of high throughput in silico methods including pharmacophore screening, conformation analysis and descriptor calculation was applied to a selection of allosteric binding sites for which in vitro screens are lacking. Variant abundance near binding site forming regions and computational docking complement the approach. Results: The structural data reflects known and novel binding sites, some of which may be unique to individual receptors, while others are broadly conserved. The membrane spanning domain, comprising four highly conserved segments, contains ligand interaction sites for which in vitro assays suitable for high throughput screenings are critically lacking. This is also the case for structurally more variable novel sites in the extracellular domain. Our computational results suggest that the phytocannabinoid Δ9-tetrahydrocannabinol (Δ9-THC) can utilize multiple pockets which are likely to exist on most superfamily members. Conclusion: With this study, we explore the potential for polypharmacology among pLGICs. Our data suggest that ligands can display two forms of promiscuity to an extent greater than what has been realized: 1) Ligands can interact with homologous sites in many members of the superfamily, which bears toxicological relevance. 2) Multiple pockets in distinct localizations of individual receptor subtypes share common ligands, which counteracts efforts to develop selective agents. Moreover, conformational states need to be considered for in silico drug screening, as certain binding sites display considerable flexibility. In total, this work contributes to a better understanding of polypharmacology across pLGICs and provides a basis for improved structure guided in silico drug development and drug derisking.

Indexing Ultrafast Shape-Based Descriptors in MongoDB to Identify TLR4 Pathway Agonists

A method is presented for an ultrafast shape-based search workflow for the screening of large compound collections, i.e., those of vendors. The three-dimensional shape of a molecule dictates its biological activity by enabling the molecule to fit into binding pockets of proteins. Quite often, distinctly different chemical compounds that have similar shapes can bind in a similar way. OpenEye pioneered an algorithm for comparing shapes of molecules by overlaying them in a computer and measuring differences between a query molecule and a target molecule. Overlaying shapes is a computationally intensive process and represents a bottleneck in searching for similar molecules. More recent publications describe alternative methods of overlaying molecules, which are accomplished by comparing shape-based descriptors. These methods were implemented in the Open Drug Discovery Toolkit (ODDT) package. We utilized a combination of open-source software packages like ODDT and RDkit to implement a workflow for ultrafast conformer generation and matching that does not require storing precomputed conformers on the file system or in memory. Moreover, the generated descriptors could be optionally stored in MongoDB for performing searches in the future. To speed up the search, we created a set of indexes from the transformed shape-based descriptors. We are in the process of calculating descriptors for multiple vendors, including Enamine's "REAL" collection of 1.2 billion compounds. Currently, the shape similarity search on more than 70 million compounds takes less than 8 s! We exemplified our methodology with the screen of compounds that can act as putative TLR4 agonists. The search was based on a literature-known small-molecule TLR4 agonist series. In due course, we identified compounds with novel structural motifs that were active in mouse and human TLR4 reporter cell lines.

Affinity-Recognition-Based Gravimetric Nanosensor for Equilin Detection

The estrogenic hormones that are widely used in postmenopausal hormone supplements for women contaminate natural water resources. Equilin (Equ) is one of the estrogenic hormones that have a maximum contaminant level of 0.35 µg/L in the chemical pollutants list. In this study, estrogenic hormones were precisely detected in a short time by affinity-recognition-based interactions in Quartz Crystal Microbalance (QCM) sensors. The QCM sensors were modified with 11-mercaptoundecanoic acid forming a self-assembled monolayer and with amino acids, namely tyrosine, tryptophan and phenylalanine. The affinity interactions between Equ and amino acids were studied using docking tools and confirmed by QCM experiments. The LODs of Equ were obtained as 4.59, 5.05 and 6.30 ng/L for tyrosine-, tryptophan- and phenylalanine-modified QCM nanosensors, respectively, with linear dynamic detection in the range of 25–500 nM. In terms of the LOD, selectivity and sensitivity calculations, the tyrosine-modified QCM nanosensor was found to have the highest performance for Equ detection compared to the tryptophan- and phenylalanine-modified ones.

Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions

Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.

Identification of New KRAS G12D Inhibitors through Computer-Aided Drug Discovery Methods

Owing to several mutations, the oncogene Kirsten rat sarcoma 2 viral oncogene homolog (KRAS) is activated in the majority of cancers, and targeting it has been pharmacologically challenging. In this study, using an in silico approach comprised of pharmacophore modeling, molecular docking, and molecular dynamics simulations, potential KRAS G12D inhibitors were investigated. A ligand-based common feature pharmacophore model was generated to identify the framework necessary for effective KRAS inhibition. The chemical features in the selected pharmacophore model comprised two hydrogen bond donors, one hydrogen bond acceptor, two aromatic rings and one hydrophobic feature. This model was used for screening in excess of 214,000 compounds from InterBioScreen (IBS) and ZINC databases. Eighteen compounds from the IBS and ten from the ZINC database mapped onto the pharmacophore model and were subjected to molecular docking. Molecular docking results highlighted a higher affinity of four hit compounds towards KRAS G12D in comparison to the reference inhibitor, BI-2852. Sequential molecular dynamics (MD) simulation studies revealed all four hit compounds them possess higher KRAS G12D binding free energy and demonstrate stable polar interaction with key residues. Further, Principal Component Analysis (PCA) analysis of the hit compounds in complex with KRAS G12D also indicated stability. Overall, the research undertaken provides strong support for further in vitro testing of these newly identified KRAS G12D inhibitors, particularly Hit1 and Hit2.

Structure-Based Pharmacophore Modeling, Virtual Screening, Molecular Docking, ADMET, and Molecular Dynamics (MD) Simulation of Potential Inhibitors of PD-L1 from the Library of Marine Natural Products

Background: In the past decade, several antibodies directed against the PD-1/PD-L1 interaction have been approved. However, therapeutic antibodies also exhibit some shortcomings. Using small molecules to regulate the PD-1/PD-L1 pathway may be another way to mobilize the immune system to fight cancer. Method: 52,765 marine natural products were screened against PD-L1(PDBID: 6R3K). To identify natural compounds, a structure-based pharmacophore model was generated, following by virtual screening and molecular docking. Then, the absorption, distribution, metabolism, and excretion (ADME) test was carried out to select the most suitable compounds. Finally, molecular dynamics simulation was also performed to validate the binding property of the top compound. Results: Initially, 12 small marine molecules were screened based on the pharmacophore model. Then, two compounds were selected for further evaluation based on the molecular docking scores. After ADME and toxicity studies, molecule 51320 was selected for further verification. By molecular dynamics analysis, molecule 51320 maintains a stable conformation with the target protein, so it has the chance to become an inhibitor of PD-L1. Conclusions: Through structure-based pharmacophore modeling, virtual screening, molecular docking, ADMET approaches, and molecular dynamics (MD) simulation, the marine natural compound 51320 can be used as a small molecule inhibitor of PD-L1.

Potential bioactive compounds as SARS-CoV-2 inhibitors from extracts of the marine red alga Halymenia durvillei (Rhodophyta) - A computational study

The respiratory infection COVID-19 caused by the virus SARS CoV-2 has continued to be a major health problem worldwide and has caused more than a million mortalities. Even if the development of COVID-19 vaccines has shown much progress, efforts to find novel, natural anti-viral drugs should be pursued. Halymenia durvillei is a marine red alga widely distributed around Southeast Asia. This study aimed to develop new anti SARS CoV-2 compounds from ethanolic and ethyl acetate extracts of H. durvillei via a computational approach, focusing onthe inhibitory action against the main protease (3CL-Mpro). In this study, 37 compounds were extracted and identified by GC–MS analysis. The potentials of compounds 1–2 tetradecandiol and E,E,Z-1,3,12-nonadecatriene-5,14-diol were identified for therapeutic purposes based on our pharmacophore study, while cholest-5-En-3-Ol (3.Beta.)- had a high fitness score in molecular docking studies both in monomer and dimer state compared to the N3 inhibitor and remdesivir affinity scores. As these compounds show competitive affinity scores against the 3CL-Mpro, these natural compounds may be effective for the treatment of COVID-19 infection. The ADME and pharmacokinetic studies should also be employed to assess the ability of the natural compounds as oral drugs. These promising results have shown the potentials of H. durvillei as an alternative drug in addressing COVID-19 infection. Accordingly, further studies should explore the effectiveness of these active compounds.

In Silico Approaches to Identify Polyphenol Compounds as α-Glucosidase and α-Amylase Inhibitors against Type-II Diabetes

Type-II diabetes mellitus (T2DM) results from a combination of genetic and lifestyle factors, and the prevalence of T2DM is increasing worldwide. Clinically, both α-glucosidase and α-amylase enzymes inhibitors can suppress peaks of postprandial glucose with surplus adverse effects, leading to efforts devoted to urgently seeking new anti-diabetes drugs from natural sources for delayed starch digestion. This review attempts to explore 10 families e.g., Bignoniaceae, Ericaceae, Dryopteridaceae, Campanulaceae, Geraniaceae, Euphorbiaceae, Rubiaceae, Acanthaceae, Rutaceae, and Moraceae as medicinal plants, and folk and herb medicines for lowering blood glucose level, or alternative anti-diabetic natural products. Many natural products have been studied in silico, in vitro, and in vivo assays to restrain hyperglycemia. In addition, natural products, and particularly polyphenols, possess diverse structures for exploring them as inhibitors of α-glucosidase and α-amylase. Interestingly, an in silico discovery approach using natural compounds via virtual screening could directly target α-glucosidase and α-amylase enzymes through Monte Carto molecular modeling. Autodock, MOE-Dock, Biovia Discovery Studio, PyMOL, and Accelrys have been used to discover new candidates as inhibitors or activators. While docking score, binding energy (Kcal/mol), the number of hydrogen bonds, or interactions with critical amino acid residues have been taken into concerning the reliability of software for validation of enzymatic analysis, in vitro cell assay and in vivo animal tests are required to obtain leads, hits, and candidates in drug discovery and development.

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.

Discovery of a Potent Candidate for RET-Specific Non-Small-Cell Lung Cancer-A Combined In Silico and In Vitro Strategy

Rearranged during transfection (RET) is a tyrosine kinase oncogenic receptor, activated in several cancers including non-small-cell lung cancer (NSCLC). Multiple kinase inhibitors vandetanib and cabozantinib are commonly used in the treatment of RET-positive NSCLC. However, specificity, toxicity, and reduced efficacy limit the usage of multiple kinase inhibitors in targeting RET protein. Thus, in the present investigation, we aimed to figure out novel and potent candidates for the inhibition of RET protein using combined in silico and in vitro strategies. In the present study, screening of 11,808 compounds from the DrugBank repository was accomplished by different hypotheses such as pharmacophore, e-pharmacophore, and receptor cavity-based models in the initial stage. The results from the different hypotheses were then integrated to eliminate the false positive prediction. The inhibitory activities of the screened compounds were tested by the glide docking algorithm. Moreover, RF score, Tanimoto coefficient, prime-MM/GBSA, and density functional theory calculations were utilized to re-score the binding free energy of the docked complexes with high precision. This procedure resulted in three lead molecules, namely DB07194, DB03496, and DB11982, against the RET protein. The screened lead molecules together with reference compounds were then subjected to a long molecular dynamics simulation with a 200 ns time duration to validate the inhibitory activity. Further analysis of compounds using MM-PBSA and mutation studies resulted in the identification of potent compound DB07194. In essence, a cell viability assay with RET-specific lung cancer cell line LC-2/ad was also carried out to confirm the in vitro biological activity of the resultant compound, DB07194. Indeed, the results from our study conclude that DB07194 can be effectively translated for this new therapeutic purpose, in contrast to the properties for which it was originally designed and synthesized.

Glioma-Targeted Therapeutics: Computer-Aided Drug Design Prospective

Amongst the several types of brain cancers known to humankind, glioma is one of the most severe and life-threatening types of cancer, comprising 40% of all primary brain tumors. Recent reports have shown the incident rate of gliomas to be 6 per 100,000 individuals per year globally. Despite the various therapeutics used in the treatment of glioma, patient survival rate remains at a median of 15 months after undergoing first-line treatment including surgery, radiation, and chemotherapy with Temozolomide. As such, the discovery of newer and more effective therapeutic agents is imperative for patient survival rate. The advent of computer-aided drug design in the development of drug discovery has emerged as a powerful means to ascertain potential hit compounds with distinctively high therapeutic effectiveness against glioma. This review encompasses the recent advances of bio-computational in-silico modeling that have elicited the discovery of small molecule inhibitors and/or drugs against various therapeutic targets in glioma. The relevant information provided in this report will assist researchers, especially in the drug design domains, to develop more effective therapeutics against this global disease.