Search

Using embedding methods, compounds with similar properties will be closely located in latent space, and these embedding vectors can be used to find other compounds with similar properties based on the distance between compounds. However, they often require computational resources and programming skills. Here we develop Dr.Emb Appyter, a user‐friendly web‐based chemical compound search platform for drug discovery without any technical barriers. It uses embedding vectors to identify compounds similar to a given query in the embedding space. Dr.Emb Appyter provides various types of embedding methods, such as fingerprinting, SMILES, and transcriptional response‐based methods, and embeds numerous compounds using them. The Faiss‐based search system efficiently finds the closest compounds of query in the library. Additionally, Dr.Emb Appyter offers information on the top compounds; visualizes the results with 3D scatter plots, heatmaps, and UpSet plots; and analyses the results using a drug‐set enrichment analysis. Dr.Emb Appyter is freely available at https://dremb.korea.ac.kr. [ABSTRACT FROM AUTHOR]

Phosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for further research, the significant structural conservation among PI3Ks makes it extremely difficult to develop new isoform-selective PI3Kα inhibitors. Embracing this challenge, we herein designed a hybrid protocol by integrating machine learning (ML) with in silico drug-designing strategies. A deep learning classification model was developed and trained on the physicochemical descriptors data of known PI3Kα inhibitors and used as a screening filter for a database of small molecules. This approach led us to the prediction of 662 compounds showcasing appropriate features to be considered as PI3Kα inhibitors. Subsequently, a multiphase molecular docking was applied to further characterize the predicted hits in terms of their binding affinities and binding modes in the targeted cavity of the PI3Kα. As a result, a total of 12 compounds were identified whereas the best poses highlighted the efficiency of these ligands in maintaining interactions with the crucial residues of the protein to be targeted for the inhibition of associated activity. Notably, potential activity of compound 12 in counteracting PI3Kα function was found in a previous in vitro study. Following the drug-likeness and pharmacokinetic characterizations, six compounds (compounds 1, 2, 3, 6, 7, and 11) with suitable ADME-T profiles and promising bioavailability were selected. The mechanistic studies in dynamic mode further endorsed the potential of identified hits in blocking the ATP-binding site of the receptor with higher binding affinities than the native inhibitor, alpelisib (BYL-719), particularly the compounds 1, 2, and 11. These outcomes support the reliability of the developed classification model and the devised computational strategy for identifying new isoform-selective drug candidates for PI3Kα inhibition. [ABSTRACT FROM AUTHOR]

Abstract Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

Introduction: Human Immunodeficiency Virus (HIV) is a global challenge for the health sector due to the absence of a definitive cure. More than 38 million people are affected by the disease worldwide, with approximately 1.5 million new cases reported annually. This situation has spurred continued efforts in searching for drug candidates. Meanwhile, rutin, luteolin, and quercetin are compounds known for efficacy in treating infectious diseases. The compounds have also been detected in Yucca aloifolia (絲蘭) variegate L. Therefore, this study aimed to conduct a computational investigation utilizing multiscale in silico exploration to assess the potential of Y. aloifolia as an anti-HIV agent targeting HIV-1 Protease (H1P). Methods: For the in silico study, the three-dimensional structure of H1P (PDB: 5V4Y) was retrieved and prepared using YASARA Structure. The binding pockets were identified using Cavity Plus server, and ligands were obtained from Y. aloifolia leaves alcohol extract. Furthermore, molecular docking was conducted with YASARA Structure to predict binding energies, followed by QSAR analysis for activity prediction. Density Functional Theory (DFT) analysis was performed to assess stability and reactivity, while toxicity was evaluated using ProTox 3.0. Molecular dynamics (MD) simulation and Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) calculation were also conducted for further analysis. Results: The results showed that Ramachandran plot analysis indicated favorable residue distribution based on the evaluation of enzyme preparation quality. Cavity Plus identified potential binding sites, with cavity no.2 showing the highest druggability. Molecular docking showed rutin and isorhamnetin-3-O-rutinoside as top binders to H1P, with favorable binding energies. Moreover, post-docking analysis produced specific interactions between ligands and the receptor. PASS prediction indicated the potential of rutin and isorhamnetin-3-O-rutinoside (narcissin) as H1P inhibitors. DFT analysis assessed stability, showing comparable values for the investigated compounds. Toxicity analysis suggested both compounds to be non-toxic. Finally, MD simulation demonstrated the superior stability and binding affinity of rutin compared to isorhamnetin-3-O-rutinoside and the control drug, grl-09510. Discussion: Rutin, hecogenin, and isorhamnetin-3-O-rutinoside from Y. aloifolia (絲蘭) leaves showed potential as H1P inhibitors through in silico study. Docking simulations indicated that rutin had the most favorable binding interactions, while MD simulation showed only the rutin-H1P complex to be stable, signifying the potential for further drug development. Rutin was identified as a promising lead for H1P inhibition due to its strong binding, stability, and predicted safety properties, underscoring the need for wet lab validation.

Curcuma longa is used traditionally in the treatment of diabetes and diabetic complications. Aldose Reductase (ALR2) inhibition is a plausible therapeutic strategy against diabetic complications. This work was aimed at evaluating Curcuma longa phytochemicals, in silico, for their ALR2 inhibitory potentials. Thirty-nine (39) phytoconstituents of Curcuma longa were subjected to a succession of in silico screenings comprising molecular docking, drug-likeness and safety profiling to identify ALR2 inhibitor leads, validating their binding interactions with molecular dynamics simulations at 50 ns simulation time. The in silico evaluations afforded two demethoxylated curcuminoids, bisdemethoxycurcumin and demethoxycurcumin, as potential ALR2 inhibitor leads forming stable ALR2 complexes, their relative potencies correlating to their degrees of demethoxylation. The two curcuminoids are herein recommended as leads for the discovery of ALR2 inhibitory antidiabetic complication drug leads.

Antibiotic resistance is a global threat to public health, and the search for new antibacterial therapies is a current research priority. The aim of this in silico study was to test nine new fluoroquinolones previously designed with potential leishmanicidal activity against Campylobacter jejuni, Escherichia coli, Neisseria gonorrhoeae, Pseudomonas aeruginosa, and Salmonella typhi, all of which are considered by the World Health Organization to resistant pathogens of global concern, through molecular docking and molecular dynamics (MD) simulations using wild-type (WT) and mutant-type (MT) DNA gyrases as biological targets. Our results showed that compound 9FQ had the best binding energy with the active site of E. coli in both molecular docking and molecular dynamics simulations. Compound 9FQ interacted with residues of quinolone resistance-determining region (QRDR) in GyrA and GyrB chains, which are important to enzyme activity and through which it could block DNA replication. In addition to compound 9FQ, compound 1FQ also showed a good affinity for DNA gyrase. Thus, these newly designed molecules could have antibacterial activity against Gram-negative microorganisms. These findings represent a promising starting point for further investigation through in vitro assays, which can validate the hypothesis and potentially facilitate the development of novel antibiotic drugs. [ABSTRACT FROM AUTHOR]

Background: The objective of the study was to use docking and pharmacological research to explore phytochemicals as therapeutic candidates for the treatment of type 2 Diabetes Mellitus. Methods: The 100 plant compounds for the study were selected after a thorough review of the most recent literature using PubMed and Google Scholar. Three-dimensional structure in Structure-Data File Format of all phytochemicals was downloaded and collected from the PubChem platform. In parallel, the three-dimensional structure of the target protein dipeptidyl peptidase-4 in Protein Data Bank (PDB) format was obtained from the website of the Research Collaboratory for Structural Bioinformatics-PDB. AutoDock Vina software was used for the docking purpose. SwissADME and the admetSAR web server were used to further examine the top docked compounds for the pharmacological investigation. Results: Out of 100 phytochemicals, only 15 have shown better or comparable binding affinity above the benchmark medication, sitagliptin (−7.9 kcal/mol). All of these compounds were assessed to determine their viability as potential drugs by predicting their Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties. Two of these phytochemicals have proven their potential as medication candidates by passing the ADMET requirements. Conclusions: In silico studies help explore and find drug candidates among the enormous pool of phytochemicals and narrow down the screening process, saving time and money on experiments. In vitro and in vivo testing can be used in the future to further validate drug candidature. [ABSTRACT FROM AUTHOR]

Background: The objective of the study was to use docking and pharmacological research to explore phytochemicals as therapeutic candidates for the treatment of type 2 Diabetes Mellitus. Methods: The 100 plant compounds for the study were selected after a thorough review of the most recent literature using PubMed and Google Scholar. Three-dimensional structure in Structure-Data File Format of all phytochemicals was downloaded and collected from the PubChem platform. In parallel, the three-dimensional structure of the target protein dipeptidyl peptidase-4 in Protein Data Bank (PDB) format was obtained from the website of the Research Collaboratory for Structural Bioinformatics-PDB. AutoDock Vina software was used for the docking purpose. SwissADME and the admetSAR web server were used to further examine the top docked compounds for the pharmacological investigation. Results: Out of 100 phytochemicals, only 15 have shown better or comparable binding affinity above the benchmark medication, sitagliptin (−7.9 kcal/mol). All of these compounds were assessed to determine their viability as potential drugs by predicting their Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties. Two of these phytochemicals have proven their potential as medication candidates by passing the ADMET requirements. Conclusions: In silico studies help explore and find drug candidates among the enormous pool of phytochemicals and narrow down the screening process, saving time and money on experiments. In vitro and in vivo testing can be used in the future to further validate drug candidature.

Abstract Background Despite the ongoing safety-driven spate of flavonoid xanthine oxidase (XOD) inhibition investigations, there is a lack of flavonoid-based uricostatic antihyperuricemic agents in clinical medicine. The poor pharmacokinetic profiles of glycosides (the natural form of existence of most flavonoids) relative to their aglycones could be largely responsible for this paradox. This investigation was aimed at providing both functional and molecular bases for the possible discovery of XOD inhibitory (or uricostatic) anti-hyperuricemic flavonoid aglycones from the leaves of a flavonoid-rich medicinal plant, Tribulus terrestris. To this end, the flavonoid aglycone fraction of T. terrestris leaf extract (FATT) was evaluated in vivo for antihyperuricemic activity in ethanol-induced hyperuricemic mice, monitoring serum and liver uric acid levels. Molecular docking and molecular dynamics simulation studies were carried out on the three major flavonoid aglycones of T. terrestris (isorhamnetin, quercetin and kaempferol) against an inhibitor conformation XOD model. The three flavonoids were also subjected to in vitro XOD activity assay, comparing their IC50 to that of allopurinol, a standard uricostatic antihyperuricemic drug. Results FATT significantly lowered serum uric acid (p

Background: The use of pharmacological agents to synergistically target key enzymes associated with carbohydrate digestion (α-glucosidase) and the hypertension-related angiotensin-converting enzyme (ACE) are critical strategies for the management of type 2 diabetes (T2D) and its end-stage complications. Furthermore, aside from their blood pressure-lowering effect, ACE inhibitors (ACEIs) are important therapeutic agents for preventing diabetic complications, highlighting their synergistic renoprotective and antihypertensive effects in diabetic patients who are normotensive and hypertensive. Objectives: We reviewed the safety and potent activity of phytochemicals discovered based on molecular docking and dynamics in recent years that could be used to treat T2D. Methods: We surveyed recently in silico drug discovery findings on α-glucosidase and ACE retrieved from the PubMed database. Computational in silico ADMET meta-analysis was performed on 57 compounds that could potentially inhibit α-glucosidase or ACE. Results: The review highlighted the fact that most hit compounds of α-glucosidase and ACE involving the use of molecular docking and molecular dynamics techniques are competitive and peptide inhibitors, respectively. Moreover, we found that most authors do not consider absorption distribution metabolism excretion toxicity (ADMET) studies on drug candidates, which is important in determining the safety profile of potent leads. Hence, we performed in silico ADMET meta-analysis of the reported compounds and found some inhibitors with an excellent pharmacological profile. Conclusion: We propose that further studies be conducted on these promising leads to demonstrate their efficacy and safety in the treatment of T2D.

Inherent oestrogen receptor alpha (ERα) and other nuclear receptor signaling activities of typical aromatase inhibitors (AIs) preclude their clinical use as anti-oestrogenic anti-benign prostatic hyperplasia (anti-BPH) agents. Spirostan sapogenins (SS) constitute a chemical space from which AIs without such deterrents could be sought. This work was aimed at in silico discovery of clinical aromatase inhibitory anti-oestrogenic anti-BPH drug leads. Fortysix SS were docked against an inhibitor conformation of the human placenta aromatase. Nuclear receptor signaling activation tendencies of seven of them showing high docking scores comparable to that of the co-crystalised ligand, exemestane, were determined in a ligand-based webserver screening (Protox-II) and docking against an agonist conformation of the ERα ligand binding domain (ERαLBD). Other toxicity and pharmacokinetic/druglikeness evaluations were caried out using Protox-II and SwissADME webservers. Stability of aromatase complex with the highest-docking-score SS was explored in a molecular dynamics simulation using Webgro molecular dynamics webserver at a 20 ns simulation time. None of the seven SS activated the nuclear receptor signaling pathways; pharmacokinetic/druglikeness predictors showed that they would be orally bioavailable; they were not susceptible to drug metabolising cytochrome P450 (CYP) isozymes and two of them demonstrated non-susceptibility to the efflux transport activity of P-glycoprotein (Pgp). Molecular dynamics data analysis revealed the root mean square deviation (RMSD) of 2 Å–3 Å and a radius of gyration of and 22 Å over the 20 ns simulation time. This investigation provides a molecular framework for anti-oestrogenic anti-BPH therapeutic strategy via aromatase inhibition (AI) and unmasks seven SS as potential anti-BPH AIs. [ABSTRACT FROM AUTHOR]

• We provide guidelines for drug discovery campaigns for rare Hereditary spastic paraplegia (HSP). • Alternative models beyond traditional drug discovery paradigms are required. • Gene therapy and drug repurposing have different application domains in the delivery of targeted therapies. • In silico and specific experimental methods are suggested. • We provide concrete examples of the application of gene therapy and targeted therapy to treat Hereditary spastic paraplegia 50 (SPG50) and Infantile ascending hereditary spastic paralysis (IAHSP). Patients diagnosed with rare diseases and their and families search desperately to organize drug discovery campaigns. Alternative models that differ from default paradigms offer real opportunities. There are, however, no clear guidelines for the development of such models, which reduces success rates and raises costs. We address the main challenges in making the discovery of new preclinical treatments more accessible, using rare hereditary paraplegia as a paradigmatic case. First, we discuss the necessary expertise, and the patients' clinical and genetic data. Then, we revisit gene therapy, de novo drug development, and drug repurposing, discussing their applicability. Moreover, we explore a pool of recommended in silico tools for pathogenic variant and protein structure prediction, virtual screening, and experimental validation methods, discussing their strengths and weaknesses. Finally, we focus on successful case applications. [ABSTRACT FROM AUTHOR]

Chronic pain is a widespread disorder affecting millions of people and is insufficiently addressed by current classes of analgesics due to significant long-term or high dosage side effects. A promising approach that was recently proposed involves the systemic inhibition of the voltage-gated sodium channel Nav1.7, capable of cancelling pain perception completely. Notwithstanding numerous attempts, currently no drugs have been approved for the inhibition of Nav1.7. The task is complicated by the difficulty of creating a selective drug for Nav1.7, and avoiding binding to the many human paralogs performing fundamental physiological functions. In our work, we obtained a promising set of ligands with up to 5–40-fold selectivity and reaching 5.2 nanomolar binding affinity by employing a proper treatment of the problem and an innovative differential in silico screening procedure to discriminate for affinity and selectivity against the Nav paralogs. The absorption, distribution, metabolism, and excretion (ADME) properties of our top-scoring ligands were also evaluated, with good to excellent results. Additionally, our study revealed that the top-scoring ligand is a stereoisomer of an already-approved drug. These facts could reduce the time required to bring a new effective and selective Nav1.7 inhibitor to the market. [ABSTRACT FROM AUTHOR]

Recently, growing interest has been devoted to the investigation of compounds with antimicrobial activity due to rising cases of resistance of microbes to known therapies. A reliable and versatile source of novel drug discovery was recently found among endophytic fungi. Hitherto, the research usually enclosed the in vitro evaluation of antimicrobial activity and chemical structure elucidation of biomolecules extracted from fungal material. Therefore, this research was designed as an extension to previous investigations of endophytic fungi growing on conifer needles by means of conducting a molecular docking study. The in silico methods were used with the main goal to make a contribution to the understanding of the mechanisms underlying the interaction of biomolecules isolated from fungus Phomopsis species and eight different types of receptors that belong to usually multidrug resistant bacterial pathogens. The results revealed valuable interactions with receptors 3G7B (Staphylococcus aureus’s gyrase B), 1F0K (1.9 Å structure of Escherichia coli’s transferase) and 1SHV (Klebsiella pneumoniae’s SHV-1 β-lactamase) thus pointing out the receptors that trigger antibiotic response upon activation by the most potent compounds 325-3, 325-5, phomoenamide and phomol. These findings also recommended further discovery of novel potent and broadspectrum antibiotics based on the structure of selected molecules.

Exploring biomolecule behavior, such as proteins and nucleic acids, using quantum mechanical theory can identify many life science phenomena from first principles. Fragment molecular orbital (FMO) calculations of whole single particles of biomolecules can determine the electronic state of the interior and surface of molecules and explore molecular recognition mechanisms based on intermolecular and intramolecular interactions. In this review, we summarized the current state of FMO calculations in drug discovery, virology, and structural biology, as well as recent developments from data science. • The fragment molecular orbital method allows quantitative analysis of intra- and intermolecular interactions of biomolecules. • It can be used to understand molecular recognition mechanism of complex systems such as proteins, nucleic acids and ligands. • Collaboration between structural biology and FMO database can build an information infrastructure for life sciences. • COVID-19 brought innovations in FMO research, including drug screening, multi-structure sampling and machine learning. [ABSTRACT FROM AUTHOR]

To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an in silico method to identify candidate drugs for treating COVID-19. [ABSTRACT FROM AUTHOR]

Peptide-based drugs are promising anticancer candidates due to their biocompatibility and low toxicity. In particular, tumor-homing peptides (THPs) have the ability to bind specifically to cancer cell receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD and the THPep, and more recently the SCMTHP. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox is presented for THP discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THP sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that apply a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. The predictions achieved accuracies that ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894–0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subsequently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for the rapid and accurate identification of THPs.

• Strategies to develop multitarget-directed ligands via in silico drug discovery. • The effects of multidirectional drugs are more favorable than those of single-target or combination treatments on drug resistance, pharmacokinetics, pharmacodynamics, safety, and cost effectiveness. • The use of multitarget drugs can increase therapeutic efficacy by targeting multiple signaling pathways and addressing complex diseases with multiple underlying causes and dominant resistance mechanisms. • The drugs can be designed to avoid off-target effects, which can lead to reduced side effects and enhanced efficacy compared to single target agents. To combat multifactorial refractory diseases, such as cancer, cardiovascular, and neurodegenerative diseases, multitarget drugs have become an emerging area of research aimed at 'synthetic lethality' (SL) relationships associated with drug-resistance mechanisms. In this review, we discuss the in silico design of dual and triple-targeted ligands, strategies by which specific 'warhead' groups are incorporated into a parent compound or scaffold with primary inhibitory activity against one target to develop one small molecule that inhibits two or three molecular targets in an effort to increase potency against multifactorial diseases. We also discuss the analytical exploration of structure–activity relationships (SARs), physicochemical properties, polypharmacology, scaffold feature extraction of US Food and Drug Administration (FDA)-approved multikinase inhibitors (MKIs), and updates regarding the clinical status of dual-targeted chemotypes. [ABSTRACT FROM AUTHOR]

COVID-19, as a newly emerging disease, has disrupted human’s different activities. Hence, it is essential to develop drugs or vaccines in order to control COVID-19. Since there is not a medication or vaccine for treating the disease and drug development project is a time and cost consuming process, drug repurposing approaches may yield to proper curing plans. However, there are some limitations in this field, which make the process a challenging one. This letter aims to introduce drug repurposing methods and the existing challenges to detect candidate drugs which may be helpful in controlling COVID-19.

Several studies have linked Cancer stem cells (CSCs) to cancer resistance development to chemotherapy and radiotherapy. ALDH1A1 is a key enzyme that regulates the gene expression of CSCs and creates an immunosuppressive tumor microenvironment. It was reported that quercetin and resveratrol were among the inhibitors of ALDH1A1. In early 2022, it was reported that new 11 flavonostilbenes (rhamnoneuronal D-N) were isolated from Rhamnoneuron balansae as potential antiaging natural products. Rhamnoneuronal H ( 5 ) could be envisioned as a natural hybrid of quercetin and resveratrol. It was therefore hypothesized that 5 and its analogous isolates rhamnoneuronal D-G ( 1-4 ) and rhamnoneuronal I-N ( 6-11 ) would have potential ALDH1A1 inhibitory activity. To this end, all isolates were subjected to molecular docking, MM-GBSA, ADMET, and molecular dynamics simulations studies to assess their potential as new leads for cancer treatment targeting ALDH1A1. In silico findings revealed that natural hybrid 5 has a similar binding affinity, judged by MM-GBSA, to the ALDH1A1 active site when compared to the co-crystalized ligand (-64.71 kcal/mole and -64.12 kcal/mole, respectively). Despite having lesser affinity than that of the co-crystalized ligand, the rest of the flavonostilbenes, except 2 - 4 , displayed better binding affinities (-37.55 kcal/mole to -58.6 kcal/mole) in comparison to either resveratrol (-34.44 kcal/mole) or quercetin (-36.48 kcal/mole). Molecular dynamic simulations showed that the natural hybrids 1 , 5 - 11 are of satisfactory stability up to 100 ns. ADMET outcomes indicate that these hybrids displayed acceptable properties and hence could represent an ideal starting point for the development of potent ALDH1A1 inhibitors for cancer treatment.Communicated by Ramaswamy H. Sarma.

[Display omitted] • The complexation of bivalent metals with N-benzyl-N-nitrosohydroxylamine (L1) and N-(2F-benzyl)-N-nitrosohydroxylamine (L2) were reported. • The crystal structure of Co(L1) 2 (H 2 O) 2 showed that the organic species act as bidental chelating ligands occupying cis -positions in the coordination sphere of Co(II) cation. • These ligands obeyed Lipinski's and Veber's rules of drug discovery pipeline. • L2 showed different target preference in comparison to that of its parent molecule, L1. • Glutaminyl-tRNA synthetase and lumazine synthase were top-scored targets. N-nitrosohydroxylamine derivatives are dubbed as chelating agents. The complexation of bivalent metals with N-benzyl-N-nitrosohydroxylamine (L1) and N-(2F-benzyl)-N-nitrosohydroxylamine (L2) were studied with DFT B3LYP modeling, electronic spectroscopy, and X-Ray diffraction. The 3d -metal cations showed good affinity to N-nitroso-N-hydroxylamine derivatives and formed complex compounds with high degree of covalence. Their absorption bands shifted towards the spectra of neutral forms of the non-coordinated ligands. The value of the shift increased in a series of Mn2+ < Co2+ < Ni2+ < Cu2+ > Zn2+ > Cd2+. The determined formation constants of the complexes lied in the range of 9.86 – 11.58. The crystal structure of Co(L1) 2 (H 2 O) 2 showed that the organic species act as bidental chelating ligands occupying cis -positions in the coordination sphere of Co(II) cation. In the lattice, the molecules of the compound formed parallel layers which arranged in columns stabilized using a set of intermolecular hydrogen bonds. These ligands obeyed Lipinski's and Veber's rules of drug discovery pipeline. Besides, L1 and L2 also had half-life values of 0.7and 0.4 h, oral bioavailability score of 0.55, and positive blood–brain barrier permeability indicating their potential as therapeutic lead-like compounds. Further, potential targets were screened out computationally. L1 predominantly interacted with family A of G-protein coupled receptors and enzymes while L2 also interacted with protease and nuclear receptors in addition to the targets of its parent molecule, L1. Essentially, glutaminyl-tRNA synthetase and lumazine synthase were suggested as top-scored targets. In sum up, these ligands may be antimicrobial lead-like molecules suitable to be submitted to the in vitro validation. [ABSTRACT FROM AUTHOR]

Viral infectious illnesses represent a severe hazard to human health due to their widespread incidence worldwide. Among these ailments, the dengue virus (DENV) infection stands out. World Health Organization (WHO) estimates that DENV infection affects ~400 million people each year, with potentially fatal symptoms showing up in 1% of the cases. In several instances, academic and pharmaceutical researchers have conducted several pilot and clinical studies on a variety of topics, including viral epidemiology, structure and function analyses, infection source and route, therapeutic targets, vaccinations, and therapeutic drugs. Amongst Takeda, TAK-003, Sanofi, Dengvaxia®, and Butantan/NIH/Merck, Dengvaxia® (CYD-TDV) is the only licensed vaccination yet; however, the potential inhibitors are under development. The biology and evolution of DENVs are briefly discussed in this review, which also compiles the most recent studies on prospective antiviral targets and antiviral candidates. In conclusion, the triumphs and failures have influenced the development of anti-DENV medications, and the findings in this review article will stimulate more investigation., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

The interest of the pharmaceutical industry in developing new antibiotics is decreasing, as established screening systems which identify compounds that kill or inhibit the growth of bacteria can no longer be used. Consequently, antimicrobial screening using classical minimum inhibitory concentration (MIC) measurements is becoming obsolete. The discovery of antimicrobial agents that specifically target a bacterial pathogen without affecting the host and its beneficial bacteria is a promising strategy. However, few host–microbe models are available for in vivo screening of novel antivirulence molecules. Here we designed high-throughput developmental assays in the social amoeba Dictyostelium discoideum to measure Pseudomonas aeruginosa virulence and to screen for novel antivirulence molecules without side effects to the host and its beneficial bacteria Klebsiella aerogenes . Thirty compounds were evaluated that had been previously selected by virtual screening for inhibitors of P. aeruginosa PAO1 polyphosphate kinase 1 ( Pa PPK1) and diverse compounds with combined PPK1 inhibitory and antivirulence activities were identified. This approach demonstrates that D. discoideum is a suitable surrogate host for preliminary high-throughput screening of antivirulence agents and that PPK1 is a suitable target for developing novel antivirulence compounds that can be further validated in mammalian models. [ABSTRACT FROM AUTHOR]

Estimation of interactions between drug-like compounds and drug targets is very important for drug discovery and toxicity assessment. Using data extracted from the 19th version of the ChEMBL database (https://www.ebi.ac.uk/chembl) as a training set and a Bayesian-like method realized in PASS software (http://www.way2drug.com/PASSOnline), we developed a computational tool for the prediction of interactions between protein targets and drug-like compounds. After training, PASS Targets became able to predict interactions of drug-like compounds with 2507 protein targets from different organisms based on analysis of structure–activity relationships for 589,107 different chemical compounds. The prediction accuracy, estimated as AUC ROC calculated by the leave-one-out cross-validation and 20-fold cross-validation procedures, was about 96%. Average AUC ROC value was about 90% for the external test set from approximately 700 known drugs interacting with 206 protein targets. [ABSTRACT FROM AUTHOR]

Background: Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems. Results: Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods. Conclusions: Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery. [ABSTRACT FROM AUTHOR]

Background: Bacopa monnieri Linn. (Plantaginaceae), a well-known medicinal plant, is widely used in traditional medicine system. It has long been used in gastrointestinal discomfort, skin diseases, epilepsy and analgesia. This research investigated the in vitro antimicrobial activity of Bacopa monnieri leaf extract against Staphylococcus aureus and the interaction of possible compounds involved in this antimicrobial action. Methods: Non-edible plant parts were extracted with ethanol and evaporated in vacuo to obtain the crude extract. A zone of inhibition studies and the minimum inhibitory concentration (MIC) of plant extracts were evaluated against clinical isolates by the microbroth dilution method. Docking study was performed to analyze and identify the interactions of possible antimicrobial compounds of Bacopa monnieri in the active site of penicillin binding protein and DNA gyrase through GOLD 4.12 software. Results: A zone of inhibition studies showed significant (p < 0.05) inhibition capacity of different concentrations of Bacopa monnieri's extract against Staphylococcus aureus. The extract also displayed very remarkable minimum inhibitory concentrations (≥16 µg/ml) which was significant compared to that (≥75 µg/ml) of the reference antibiotic against the experimental strain Staphylococcus aureus. Docking studies recommended that luteolin, an existing phytochemical of Bacopa monnieri, has the highest fitness score and more specificity towards the DNA gyrase binding site rather than penicillin binding protein. Conclusions: Bacopa monnieri extract and its compound luteolin have a significant antimicrobial activity against Staphylococcus aureus. Molecular binding interaction of an in silico data demonstrated that luteolin has more specificity towards the DNA gyrase binding site and could be a potent antimicrobial compound. [ABSTRACT FROM AUTHOR]

Introduction: The nuclear transcription factor PPARγ, which can modulate cell growth via proliferation and apoptosis-related mechanisms, is a promising target in cancer therapy. This study aims to focus on PPARγ as the target and use virtual screening to find hits., Methods: A set of 5,677 flavonoid compounds were filtered by subjecting them to descriptor-based drug-likeness and ADMET strategies to discover drug-like compounds. The candidates' modes of binding to PPARγ were then evaluated using docking and MD simulation. PharmMapper was used to identify the potential targets of selected hits. The pharmacological network was constructed based on the GO and KEGG pathway analysis., Results: In primary screening, 3,057 compounds met various drug-likeness criteria and docked well as partial agonists in the PPARγ-LBD. Five compounds (euchrenone b 1 , kaempferol-7-Orhamnoside, vincetoxicoside B, morusin, and karanjin) were selected with the use of ADMET profiles for further MD simulation investigation. Based on the PharmMapper findings, 52 proteins were then submitted to GO and KEGG enrichment analysis. As expected by GO and KEGG pathway enrichment studies, core targets were enriched in the PI3K-Akt signaling pathway (p < 0.01), indicating that certain chemicals may be involved in cancer processes., Conclusion: Our results suggested that the selected compounds might have sufficient drug-likeness, pharmacokinetics, and in silico bioactivity by acting as PPARγ partial agonists. Although much work remains to illuminate extensive cancer therapeutic/ chemopreventive efficacy of flavonoids in vivo, in silico methodology of our cheminformatics research may be able to provide additional data regarding the efficacy and safety of potential candidates for therapeutic targets., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Abstract: The Ebola virus (EBOV) genome only encodes a single viral polypeptide with enzymatic activity, the viral large (L) RNA-dependent RNA polymerase protein. However, currently, there is limited information about the L protein, which has hampered the development of antivirals. Therefore, antifiloviral therapeutic efforts must include additional targets such as protein–protein interfaces. Viral protein 35 (VP35) is multifunctional and plays important roles in viral pathogenesis, including viral mRNA synthesis and replication of the negative-sense RNA viral genome. Previous studies revealed that mutation of key basic residues within the VP35 interferon inhibitory domain (IID) results in significant EBOV attenuation, both in vitro and in vivo. In the current study, we use an experimental pipeline that includes structure-based in silico screening and biochemical and structural characterization, along with medicinal chemistry, to identify and characterize small molecules that target a binding pocket within VP35. NMR mapping experiments and high-resolution x-ray crystal structures show that select small molecules bind to a region of VP35 IID that is important for replication complex formation through interactions with the viral nucleoprotein (NP). We also tested select compounds for their ability to inhibit VP35 IID–NP interactions in vitro as well as VP35 function in a minigenome assay and EBOV replication. These results confirm the ability of compounds identified in this study to inhibit VP35–NP interactions in vitro and to impair viral replication in cell-based assays. These studies provide an initial framework to guide development of antifiloviral compounds against filoviral VP35 proteins. [Copyright &y& Elsevier]

In light of the low success rate of target-based genomics and HTS (High Throughput Screening) approaches in anti-infective drug discovery, in silico structure-based drug design (SBDD) is becoming increasingly prominent at the forefront of drug discovery. In silico SBDD can be used to identify novel enzyme inhibitors rapidly, where the strength of this approach lies with its ability to model and predict the outcome of protein-ligand binding. Over the past 10 years, our group have applied this approach to a diverse number of anti-infective drug targets ranging from bacterial D-ala-D-ala ligase to Plasmodium falciparum DHODH. Our search for new inhibitors has produced lead compounds with both enzyme and whole-cell activity with established on-target mode of action. This has been achieved with greater speed and efficiency compared with the more traditional HTS initiatives and at significantly reduced cost and manpower. [ABSTRACT FROM AUTHOR]

The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set of tools to approach structure-based drug design problems in the computer-aided drug design space. A novel programmatic tool that incorporates in silico and deep learning based approaches for de novo drug design for any target of interest has been reported. Once the user specifies the target of interest in the form of a representative amino acid sequence or corresponding nucleotide sequence, the programmatic workflow of the tool generates compounds from the PubChem ligand library and novel SMILES of compounds not present in any ligand library but are likely to be active against the target. Following this, the tool performs a computationally efficient In-Silico modeling of the target and the newly generated compounds and stores the results of the protein-ligand interaction in the working folder of the user. Further, for the protein-ligand complex associated with the best protein-ligand interaction, the tool performs an automated Molecular Dynamics (MD) protocol and generates plots such as RMSD (Root Mean Square Deviation) which reveal the stability of the complex. A demonstrated use of the tool has been shown with the target signatures of Tumor Necrosis Factor-Alpha, an important therapeutic target in the case of anti-inflammatory treatment. The future scope of the tool involves, running the tool on a High-Performance Cluster for all known target signatures to generate data that will be useful to drive AI and Big data driven drug discovery. The code is hosted, maintained, and supported at the GitHub repository given in the link below https://github.com/bengeof/Target2DeNovoDrugCommunicated by Ramaswamy H. Sarma.

Alpha sphere filter (ASF) method is a novel previrtual screening method to undertake a rapid virtual screening of a huge chemical space. The small-molecule binding site of a target molecule can be characterized by a set of alpha spheres generated at the site. Two types of pseudomolecules representing molecules that likely fit into the binding site were molded from the set of alpha spheres. Based on the pseudomolecules, pseudomolecular descriptors corresponding to the conventional two-dimensional (2D) molecular descriptors were calculated. The correlations between the pseudomolecular descriptors and the 2D molecular descriptors were analyzed for a set of high-quality X-ray structures of the complexes between ligands and proteins. By use of these correlations, specific value ranges of the 2D molecular descriptors were determined. These value ranges were applied in virtual screening. In a trial to screen 200 active ligands out of a chemical database with 42,547 molecules, the enrichment rate of 5.8 was attained. The enrichment rate was good enough for a prescreening tool prior to docking simulations. As the ASF method screens molecules by 2D molecular descriptors, it is rapid enough to screen a huge chemical space and could significantly decrease the number of trivial compounds to be considered in the following docking simulations. Therefore, the ASF method can contribute to enlarge the possibility of virtual screening. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010 [ABSTRACT FROM AUTHOR]

Peptide-based drugs are promising anticancer candidates due to their biocompatibility and low toxicity. In particular, tumor-homing peptides (THPs) have the ability to bind specifically to cancer cell receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD and the THPep, and more recently the SCMTHP. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox is presented for THP discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THP sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that apply a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. The predictions achieved accuracies that ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894–0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subsequently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for the rapid and accurate identification of THPs. [ABSTRACT FROM AUTHOR]

COVID-19, as a newly emerging disease, has disrupted human's different activities. Hence, it is essential to develop drugs or vaccines in order to control COVID-19. Since there is not a medication or vaccine for treating the disease and drug development project is a time and cost consuming process, drug repurposing approaches may yield to proper curing plans. However, there are some limitations in this field, which make the process a challenging one. This letter aims to introduce drug repurposing methods and the existing challenges to detect candidate drugs which may be helpful in controlling COVID-19. [ABSTRACT FROM AUTHOR]

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications., Competing Interests: Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.