ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties

Log P analyzation-based discovery of GSH activated biotin-tagged fluorescence probe for selective colorectal cancer imaging

Targeted activatable fluorescent probes could provide an effective approach for colorectal cancer imaging. In this study, F1 was found as an effective targeted activatable fluorescent probe based on log P analysis. In vitro experiments demonstrated that the initial fluorescence of the developed probe F1 was initially well quenched, and the fluorescence increased after the probe interacted with glutathione. Cell imaging results showed that the probe had good cell permeability and selectivity. Remarkably, F1 displayed enhanced tumor tissue fluorescence in MC-38 tumor-bearing mice. Notably, it showed selectivity in imaging clinical specimens of human colorectal cancer tissues. Accordingly, this study shows that log P analysis can facilitate the developing efficient of biotin-tagged activatable probes, and the identified F1 has a good potential in clinical colorectal cancer diagnosis.

BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers

Unintended inhibition of the human ether-à-go-go-related gene (hERG) ion channel by small molecules leads to severe cardiotoxicity. Thus, hERG channel blockage is a significant concern in the development of new drugs. Several computational models have been developed to predict hERG channel blockage, including deep learning models; however, they lack robustness, reliability and interpretability. Here, we developed a graph-based Bayesian deep learning model for hERG channel blocker prediction, named BayeshERG, which has robust predictive power, high reliability and high resolution of interpretability. First, we applied transfer learning with 300 000 large data in initial pre-training to increase the predictive performance. Second, we implemented a Bayesian neural network with Monte Carlo dropout to calibrate the uncertainty of the prediction. Third, we utilized global multihead attentive pooling to augment the high resolution of structural interpretability for the hERG channel blockers and nonblockers. We conducted both internal and external validations for stringent evaluation; in particular, we benchmarked most of the publicly available hERG channel blocker prediction models. We showed that our proposed model outperformed predictive performance and uncertainty calibration performance. Furthermore, we found that our model learned to focus on the essential substructures of hERG channel blockers via an attention mechanism. Finally, we validated the prediction results of our model by conducting in vitro experiments and confirmed its high validity. In summary, BayeshERG could serve as a versatile tool for discovering hERG channel blockers and helping maximize the possibility of successful drug discovery. The data and source code are available at our GitHub repository (https://github.com/GIST-CSBL/BayeshERG).

Network pharmacology and molecular docking approach to elucidate the mechanisms of Liuwei Dihuang pill in diabetic osteoporosis

Background

Diabetic osteoporosis (DOP) is one of the chronic complications of diabetes mellitus, but without a standardized treatment plan till now. Liuwei Dihuang pill (LDP) has gradually exerted a remarkable effect on DOP in recent years; its specific mechanism is not clear yet.

Methods

We adopted network pharmacology approaches, including multi-database search, pharmacokinetic screening, network construction analysis, gene ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis and molecular docking to elaborate the active components, signaling pathways and potential mechanisms of LDP in the treatment of DOP.

Results

Twenty-seven active ingredients and 55 related disease targets have been found through integrated network pharmacology. Functional enrichment analysis shows that five key active ingredients, including beta-sitosterol, stigmasterol, diosgenin, tetrahydroalstonine, and kadsurenone, may give full scope to insulin secretion estrogen-level raising and angiogenesis in biological process through the pivotal targets. In addition, the underlying effect of PI3K/AKT/FOXO and VEGF pathways is also suggested in the treatment.

Conclusion

Based on systematic network pharmacology methods, we predicted the basic pharmacological effects and potential mechanisms of LDP in the treatment of DOP, revealing that LDP may treat DOP through multiple targets and multiple signaling pathways, which provide evidence for the further study of pharmacological mechanism and broader clinical thinking.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-022-03194-2.

"Mango in all her majesty"-the potential of mangiferin and its analogues in the inhibition of Eimeria tenella hexokinase-a computational approach

The potential of natural products in mitigating infections and diseases are being considered lately. Herein, via in silico methods, we report the possible molecular mechanism of mangiferin (isolated from the fruit, peel, bark and leaves of mango tree) and its derivatives in inhibiting Eimeria tenella hexokinase. We evaluated the binding affinity of these inhibitors to the glucose binding site of EtHK and thereafter proceeded to molecular dynamics simulation. The Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) reveals that three of the derivatives (CPAMM, MxPAMM and NAMM) had better total binding free energy than mangiferin. The ADMET and physicochemical properties assessed shows that inhibitors also hold a potential to be drug-likely. Finally, in mediating their inhibitory potentials, the ligands stabilize both the global and local structures of the protein. This study provides a theoretical premise on which the anti-coccidial propensities of mangiferin most especially its derivatives can be investigate in vitro and in vivo.Communicated by Ramaswamy H. Sarma.

Computational Identification of Potential Multitarget Inhibitors of Nipah Virus by Molecular Docking and Molecular Dynamics

Nipah virus (NiV) is a recently emerged paramyxovirus that causes severe encephalitis and respiratory diseases in humans. Despite the severe pathogenicity of this virus and its pandemic potential, not even a single type of molecular therapeutics has been approved for human use. Considering the role of NiV attachment glycoprotein G (NiV-G), fusion glycoprotein (NiV-F), and nucleoprotein (NiV-N) in virus replication and spread, these are the most attractive targets for anti-NiV drug discovery. Therefore, to prospect for potential multitarget chemical/phytochemical inhibitor(s) against NiV, a sequential molecular docking and molecular-dynamics-based approach was implemented by simultaneously targeting NiV-G, NiV-F, and NiV-N. Information on potential NiV inhibitors was compiled from the literature, and their 3D structures were drawn manually, while the information and 3D structures of phytochemicals were retrieved from the established structural databases. Molecules were docked against NiV-G (PDB ID:2VSM), NiV-F (PDB ID:5EVM), and NiV-N (PDB ID:4CO6) and then prioritized based on (1) strong protein-binding affinity, (2) interactions with critically important binding-site residues, (3) ADME and pharmacokinetic properties, and (4) structural stability within the binding site. The molecules that bind to all the three viral proteins (NiV-G ∩ NiV-F ∩ NiV-N) were considered multitarget inhibitors. This study identified phytochemical molecules RASE0125 (17-O-Acetyl-nortetraphyllicine) and CARS0358 (NA) as distinct multitarget inhibitors of all three viral proteins, and chemical molecule ND_nw_193 (RSV604) as an inhibitor of NiV-G and NiV-N. We expect the identified compounds to be potential candidates for in vitro and in vivo antiviral studies, followed by clinical treatment of NiV.

Isobavachalcone: A comprehensive review of its plant sources, pharmacokinetics, toxicity, pharmacological activities and related molecular mechanisms

Isobavachalcone (IBC), also known as isobapsoralcone, is a natural flavonoid widely derived from many medicinal plants, including Fabaceae, Moraceae, and so forth. IBC has been paid more and more attention by researchers in recent years due to its pharmacological activity in many diseases. This review aims to describe in detail the plant sources, pharmacokinetics, toxicity, pharmacological activities, and molecular mechanisms of IBC on various diseases. We found that IBC can be obtained not only by extraction but also by chemical synthesis. Pharmacokinetic studies have shown that IBC has low bioavailability, but can penetrate the blood-brain barrier and is widely distributed in the brain. Its pharmacological activities mainly include anticancer, antibacterial, anti-inflammatory, antiviral, neuroprotective, bone protection, and other activities. In particular, IBC shows strong anti-tumor and anti-inflammatory therapeutic potential due to its anti-cancer and anti-inflammatory activities. However, due to its hepatotoxicity, there may be more drug interactions. Therefore, more and more in-depth studies are needed for its clinical application. Mechanically, IBC can induce the production of reactive oxygen species (ROS), inhibit AKT, ERK, and Wnt pathways, and promote apoptosis of cancer cells through mitochondrial or endoplasmic reticulum pathways. IBC can inhibit the NF-κB pathway and the production of multiple inflammatory mediators by activating NRF2/HO-1 pathway, thus producing anti-inflammatory effects. Moreover, we discussed the limitations of current research on IBC and put forward some new perspectives and challenges, which provide a strong basis for clinical application and new drug development of IBC in the future.

Recognition and Health Impacts of Organic Pollutants with Significantly Different Proportions in the Gas Phase and Size-Fractionated Particulate Phase in Ambient Air

The distributions of organic pollutants in the gas phase and size-fractionated particle phases can largely affect human health risks posed by them. Gas-particle partitioning and particle-size distributions of some known pollutants have been investigated. However, the pollutants which are more likely to enter the human body and cause strong adverse effects may be neglected. In this study, a nontargeted screening approach combining comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry and chemometrics was developed. Eighty-eight compounds with markedly different proportions in the gas phase and PM₁, as well as 50 contaminants with significant differences in PM₁ and particles with diameters of 1-2.5 μm, were identified. Of these compounds, 18 were found in the air for the first time. There were obvious discrepancies between the measured and predicted gas-particle partitioning coefficients for some pollutants, suggesting unexpected environmental fates and health risks. The human daily intakes through inhalation and dermal exposure to these pollutants were estimated with the International Commission on Radiological Protection deposition model and transdermal permeability model. A risk-based prioritization was performed. The results indicated that adverse effects posed by 9H-fluoren-9-one, 2-ethylhexyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate, p-cumenol, 2,4-diisocyanato-1-methyl-benzene, bis(2-ethylhexyl) phthalate, perylene, (E)-cinnamaldehyde, 4-methyl-2-nitro-phenol, benzoic acid, and bis(2-methylpropyl) ester hexanedioic acid in ambient air may be more severe than those posed by conventionally monitored pollutants. The findings would facilitate raising concerns about these pollutants before they cause further severe and widespread impacts.

Investigation of antidiabetic properties of shikonin by targeting aldose reductase enzyme: In silico and in vitro studies

Diabetes is a complicated multifactorial disorder in which the patient generally observes polyphagia, polydipsia, and polyuria due to uncontrolled growth in blood sugar levels. For its management, the pharmaceutical industry is working day and night to find a better drug with no or least toxicity. That's why nowadays a more focused branch is to use herbal phytoconstituents for its prevention. Shikonin is a naphthoquinone natural dye that is isolated from the plants of the Boraginaceae family and has proven its role as an anti-cancer, anti-inflammatory, and anti-gonadotrophic agent. In our previous study, we have published its anti-diabetic action by inhibiting the enzyme protein tyrosine phosphatase 1B. In this study, we were more focused on finding out the role of Shikonin and its pharmacophores by inhibiting the action of aldose reductase (AR) enzyme. The study was conducted using pharmacophore modeling, molecular docking, and molecular dynamics simulation studies. The absorption, distribution, metabolism, excretion (ADME), and toxicity profile were also evaluated in this study. Along with all the computational biology parameters we also focused on the in vitro activity and kinetic study of inhibitory activity of Shikonin against aldose reductase.

Targeting protein tyrosine phosphatase 1B in obesity-associated colon cancer: Possible role of sweet potato (Ipomoea batatas)

Protein tyrosine phosphatase 1B (PTP1B) has emerged as one of the links between obesity and colon cancer (CC). Anti-obesity and anti-CC attributes of sweet potato (Ipomoea batatas) reported sparsely. Here, we aimed to study the potential of PTP1B as a target in CC, particularly in obese population. Expression and genomic alteration frequency of PTPN1 (PTP1B) were checked in CC. Interacting partners of PTP1B through STRING and hub genes through Cytoscape (MCODE) were identified. Hub genes were subjected to functional enrichment analyses (via Metascape), differential gene expression, copy number variation, and single nucleotide variation analyses (GSCA database). Cancer-related pathways and associated immune infiltrates of the hub genes were checked too. Eleven sweet potato-derived compounds selected through drug likeness (DL) and toxicity filters were explored via molecular docking (AutoDock Vina) to reveal the interactions with PTP1B. Genomic alteration frequency of the PTPN1 was highest in CC compared to all the other TCGA cancers, and a high expression (RNA and protein) is also observed in CC that correlated well to a poor overall survival (OS). Furthermore, PTP1B and related proteins were enriched in different biological processes and signaling pathways related to carcinogenesis including epithelial-mesenchymal transition. Overall, PTP1B identified as a potential target in obesity-linked CC and sweet potato might exert its protective action by targeting the PTP1B. Sweet potato compounds (e.g., pelargonidin and luteolin) interacted with the catalytic P loop and the WPD loop of the PTP1B. Furthermore, MD simulation study ascertained that luteolin has the highest affinity against the PTP1B, whereas pelargonidin and quercetin showed good binding affinity too, thus can be explored further.

In Silico and In Vitro Assessment of Antimicrobial and Antibiofilm Activity of Some 1,3-Oxazole-Based Compounds and Their Isosteric Analogues

In this paper, we report on the antimicrobial activity assessment of 49 compounds previously synthesized as derivatives of alanine or phenylalanine that incorporate a 4-(4-X-phenylsulfonyl)phenyl fragment (X = H, Cl, or Br), namely 21 acyclic compounds (6 × N-acyl-α-amino acids, 1 × N-acyl-α-amino acid ester, and 14 × N-acyl-α-amino ketones) and 28 pentatomic heterocycles from the oxazole-based compound class (6 × 4H-1,3-oxazol-5-ones, 16 × 5-aryl-1,3-oxazoles, and 6 × ethyl 1,3-oxazol-5-yl carbonates). Both in silico and in vitro qualitative and quantitative assays were used to investigate the antimicrobial potential of these derivatives against planktonic and biofilm-embedded microbial strains. Some of the tested compounds showed promising antimicrobial and antibiofilm activity depending on their chemical scaffold and lipophilic character.

Prediction of ACE-I Inhibitory Peptides Derived from Chickpea (Cicer arietinum L.): In Silico Assessments Using Simulated Enzymatic Hydrolysis, Molecular Docking and ADMET Evaluation

Chickpea (Cicer arietinum L.) peptides have shown in vitro potential to inhibit the angiotensin I-converting enzyme (ACE-I). However, the potential molecular interactions between chickpea peptides (CP) and ACE-I as well as their ADMET (absorption/distribution/metabolism/excretion/toxicity) characteristics remain unknown. Thus, our aim was to study the in silico interactions of CP with ACE-I and the CP ADMET characteristics. Legumin and provicilin sequences were submitted to in silico analysis to search for ACE-I inhibitory peptides. Simulated enzymatic hydrolysis was performed using the BIOPEP-UWM database, and the ACE-I inhibitory peptides generated (EC50 ≤ 200 μM) were selected to perform molecular docking and ADMET analysis. After hydrolysis, 59 out of 381 peptides with ACE-I inhibitory potential were released. Based on A and B parameters, the legumin peptides showed better ACE-I inhibitory potential than the provicilin ones. CP mainly interact with residues from pocket S1 (Ala354/Glu384) and S2 (His353/His513) through hydrogen bonds (distances < 3.0 Å) and hydrophobic interactions (binding energy from −5.7 to −9.2 kcal/mol). Through ADMET analysis, CP showed optimal values for inhibiting ACE-I in vivo. ACE-I inhibitory peptides from legumin and provicilin can bind strongly and tightly to the active site of ACE-I. Further studies to evaluate in vivo the antihypertensive effects of CP are warranted.

Structural Elucidation of Rift Valley Fever Virus L Protein towards the Discovery of Its Potential Inhibitors

Rift valley fever virus (RVFV) is the causative agent of a viral zoonosis that causes a significant clinical burden in domestic and wild ruminants. Major outbreaks of the virus occur in livestock, and contaminated animal products or arthropod vectors can transmit the virus to humans. The viral RNA-dependent RNA polymerase (RdRp; L protein) of the RVFV is responsible for viral replication and is thus an appealing drug target because no effective and specific vaccine against this virus is available. The current study reported the structural elucidation of the RVFV-L protein by in-depth homology modeling since no crystal structure is available yet. The inhibitory binding modes of known potent L protein inhibitors were analyzed. Based on the results, further molecular docking-based virtual screening of Selleckchem Nucleoside Analogue Library (156 compounds) was performed to find potential new inhibitors against the RVFV L protein. ADME (Absorption, Distribution, Metabolism, and Excretion) and toxicity analysis of these compounds was also performed. Besides, the binding mechanism and stability of identified compounds were confirmed by a 50 ns molecular dynamic (MD) simulation followed by MM/PBSA binding free energy calculations. Homology modeling determined a stable multi-domain structure of L protein. An analysis of known L protein inhibitors, including Monensin, Mycophenolic acid, and Ribavirin, provide insights into the binding mechanism and reveals key residues of the L protein binding pocket. The screening results revealed that the top three compounds, A-317491, Khasianine, and VER155008, exhibited a high affinity at the L protein binding pocket. ADME analysis revealed good pharmacodynamics and pharmacokinetic profiles of these compounds. Furthermore, MD simulation and binding free energy analysis endorsed the binding stability of potential compounds with L protein. In a nutshell, the present study determined potential compounds that may aid in the rational design of novel inhibitors of the RVFV L protein as anti-RVFV drugs.

IDL-PPBopt: A Strategy for Prediction and Optimization of Human Plasma Protein Binding of Compounds via an Interpretable Deep Learning Method

The prediction and optimization of pharmacokinetic properties are essential in lead optimization. Traditional strategies mainly depend on the empirical chemical rules from medicinal chemists. However, with the rising amount of data, it is getting more difficult to manually extract useful medicinal chemistry knowledge. To this end, we introduced IDL-PPBopt, a computational strategy for predicting and optimizing the plasma protein binding (PPB) property based on an interpretable deep learning method. At first, a curated PPB data set was used to construct an interpretable deep learning model, which showed excellent predictive performance with a root mean squared error of 0.112 for the entire test set. Then, we designed a detection protocol based on the model and Wilcoxon test to identify the PPB-related substructures (named privileged substructures, PSubs) for each molecule. In total, 22 general privileged substructures (GPSubs) were identified, which shared some common features such as nitrogen-containing groups, diamines with two carbon units, and azetidine. Furthermore, a series of second-level chemical rules for each GPSub were derived through a statistical test and then summarized into substructure pairs. We demonstrated that these substructure pairs were equally applicable outside the training set and accordingly customized the structural modification schemes for each GPSub, which provided alternatives for the optimization of the PPB property. Therefore, IDL-PPBopt provides a promising scheme for the prediction and optimization of the PPB property and would be helpful for lead optimization of other pharmacokinetic properties.

HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer

MOTIVATION

Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple comprehensive ADMET systems that adopt advanced machine learning models have been developed, providing services to estimate multiple endpoints. However, those ADMET systems usually suffer from weak extrapolation ability. First, due to the lack of labelled data for each endpoint, typical machine learning models perform frail for the molecules with unobserved scaffolds. Second, most systems only provide fixed built-in endpoints and cannot be customised to satisfy various research requirements. To this end, we develop a robust and endpoint extensible ADMET system, HelixADMET (H-ADMET). H-ADMET incorporates the concept of self-supervised learning to produce a robust pre-trained model. The model is then fine-tuned with a multi-task and multi-stage framework to transfer knowledge between ADMET endpoints, auxiliary tasks, and self-supervised tasks.

RESULTS

Our results demonstrate that H-ADMET achieves an overall improvement of 4%, compared with existing ADMET systems on comparable endpoints. Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customised ADMET endpoints, meeting various demands of drug research and development requirements.

AVAILABILITY

H-ADMET is freely accessible at https://paddlehelix.baidu.com/app/drug/admet/train.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Quantitative and Qualitative Analysis of the Anti-Proliferative Potential of the Pyrazole Scaffold in the Design of Anticancer Agents

The current work presents an objective overview of the impact of one important heterocyclic structure, the pyrazole ring, in the development of anti-proliferative drugs. A set of 1551 pyrazole derivatives were extracted from the National Cancer Institute (NCI) database, together with their growth inhibition effects (GI%) on the NCI’s panel of 60 cancer cell lines. The structures of these derivatives were analyzed based on the compounds’ averages of GI% values across NCI-60 cell lines and the averages of the values for the outlier cells. The distribution and the architecture of the Bemis–Murcko skeletons were analyzed, highlighting the impact of certain scaffold structures on the anti-proliferative effect’s potency and selectivity. The drug-likeness, chemical reactivity and promiscuity risks of the compounds were predicted using AMDETlab. The pyrazole ring proved to be a versatile scaffold for the design of anticancer drugs if properly substituted and if connected with other cyclic structures. The 1,3-diphenyl-pyrazole emerged as a useful scaffold for potent and targeted anticancer candidates.

A Comprehensive Review on the Chemical Properties, Plant Sources, Pharmacological Activities, Pharmacokinetic and Toxicological Characteristics of Tetrahydropalmatine

Tetrahydropalmatine (THP), a tetrahydroproberine isoquinoline alkaloid, is widely present in some botanical drugs, such as Stephania epigaea H.S. Lo (Menispermaceae; Radix stephaniae epigaeae), Corydalis yanhusuo (Y.H.Chou & Chun C.Hsu) W.T. Wang ex Z.Y. Su and C.Y. Wu (Papaveraceae; Corydalis rhizoma), and Phellodendron chinense C.K.Schneid (Berberidaceae; Phellodendri chinensis cortex). THP has attracted considerable attention because of its diverse pharmacological activities. In this review, the chemical properties, plant sources, pharmacological activities, pharmacokinetic and toxicological characteristics of THP were systematically summarized for the first time. The results indicated that THP mainly existed in Papaveraceae and Menispermaceae families. Its pharmacological activities include anti-addiction, anti-inflammatory, analgesic, neuroprotective, and antitumor effects. Pharmacokinetic studies showed that THP was inadequately absorbed in the intestine and had rapid clearance and low bioavailability in vivo, as well as self-microemulsifying drug delivery systems, which could increase the absorption level and absorption rate of THP and improve its bioavailability. In addition, THP may have potential cardiac and neurological toxicity, but toxicity studies of THP are limited, especially its long-duration and acute toxicity tests. In summary, THP, as a natural alkaloid, has application prospects and potential development value, which is promising to be a novel drug for the treatment of pain, inflammation, and other related diseases. Further research on its potential target, molecular mechanism, toxicity, and oral utilization should need to be strengthened in the future.

Interpretable-ADMET: a web service for ADMET prediction and optimization based on deep neural representation

MOTIVATION

In the process of discovery and optimization of lead compounds, it is difficult for non-expert pharmacologists to intuitively determine the contribution of substructure to a particular property of a molecule.

RESULTS

In this work, we develop a user-friendly web service, named interpretable-absorption, distribution, metabolism, excretion and toxicity (ADMET), which predict 59 ADMET-associated properties using 90 qualitative classification models and 28 quantitative regression models based on graph convolutional neural network and graph attention network algorithms. In interpretable-ADMET, there are 250 729 entries associated with 59 kinds of ADMET-associated properties for 80 167 chemical compounds. In addition to making predictions, interpretable-ADMET provides interpretation models based on gradient-weighted class activation map for identifying the substructure, which is important to the particular property. Interpretable-ADMET also provides an optimize module to automatically generate a set of novel virtual candidates based on matched molecular pair rules. We believe that interpretable-ADMET could serve as a useful tool for lead optimization in drug discovery.

AVAILABILITY AND IMPLEMENTATION

Interpretable-ADMET is available at http://cadd.pharmacy.nankai.edu.cn/interpretableadmet/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Systematic optimization of host-directed therapeutic targets and preclinical validation of repositioned antiviral drugs

Inhibition of host protein functions using established drugs produces a promising antiviral effect with excellent safety profiles, decreased incidence of resistant variants and favorable balance of costs and risks. Genomic methods have produced a large number of robust host factors, providing candidates for identification of antiviral drug targets. However, there is a lack of global perspectives and systematic prioritization of known virus-targeted host proteins (VTHPs) and drug targets. There is also a need for host-directed repositioned antivirals. Here, we integrated 6140 VTHPs and grouped viral infection modes from a new perspective of enriched pathways of VTHPs. Clarifying the superiority of nonessential membrane and hub VTHPs as potential ideal targets for repositioned antivirals, we proposed 543 candidate VTHPs. We then presented a large-scale drug-virus network (DVN) based on matching these VTHPs and drug targets. We predicted possible indications for 703 approved drugs against 35 viruses and explored their potential as broad-spectrum antivirals. In vitro and in vivo tests validated the efficacy of bosutinib, maraviroc and dextromethorphan against human herpesvirus 1 (HHV-1), hepatitis B virus (HBV) and influenza A virus (IAV). Their drug synergy with clinically used antivirals was evaluated and confirmed. The results proved that low-dose dextromethorphan is better than high-dose in both single and combined treatments. This study provides a comprehensive landscape and optimization strategy for druggable VTHPs, constructing an innovative and potent pipeline to discover novel antiviral host proteins and repositioned drugs, which may facilitate their delivery to clinical application in translational medicine to combat fatal and spreading viral infections.

Design of Rational JAK3 Inhibitors Based on the Parent Core Structure of 1,7-Dihydro-Dipyrrolo [2,3-b:3',2'-e] Pyridine

JAK3 differs from other JAK family members in terms of tissue distribution and functional properties, making it a promising target for autoimmune disease treatment. However, due to the high homology of these family members, targeting JAK3 selectively is difficult. As a result, exploiting small changes or selectively boosting affinity within the ATP binding region to produce new tailored inhibitors of JAK3 is extremely beneficial. PubChem CID 137321159 was used as the lead inhibitor in this study to preserve the characteristic structure and to collocate it with the redesigned new parent core structure, from which a series of 1,7-dihydro-dipyrrolo [2,3-b:3′,2′-e] pyridine derivatives were obtained using the backbone growth method. From the proposed compounds, 14 inhibitors of JAK3 were found based on the docking scoring evaluation. The RMSD and MM/PBSA methods of molecular dynamics simulations were also used to confirm the stable nature of this series of complex systems, and the weak protein−ligand interactions during the dynamics were graphically evaluated and further investigated. The results demonstrated that the new parent core structure fully occupied the hydrophobic cavity, enhanced the interactions of residues LEU828, VAL836, LYS855, GLU903, LEU905 and LEU956, and maintained the structural stability. Apart from this, the results of the analysis show that the binding efficiency of the designed inhibitors of JAK3 is mainly achieved by electrostatic and VDW interactions and the order of the binding free energy with JAK3 is: 8 (−70.286 kJ/mol) > 11 (−64.523 kJ/mol) > 6 (−51.225 kJ/mol) > 17 (−42.822 kJ/mol) > 10 (−40.975 kJ/mol) > 19 (−39.754 kJ/mol). This study may provide a valuable reference for the discovery of novel JAK3 inhibitors for those patients with immune diseases.

Identification of Potential WSB1 Inhibitors by AlphaFold Modeling, Virtual Screening, and Molecular Dynamics Simulation Studies

WD40 repeat and SOCS box containing 1 (WSB1) consists of seven WD40 repeat structural domains at the N-terminal end and one SOCS box structural domain at the C-terminal end. WSB1 promotes cancer progression by affecting the Von Hippel–Lindau tumor suppressor protein (pVHL) and upregulating hypoxia inducible factor-1α (HIF-1α) target gene expression. However, the crystal structure of WSB1 has not been reported, which is not beneficial to the research on WSB1 inhibitors. Therefore, we focused on specific small molecule inhibitors of WSB1. This study applied virtual screening and molecular dynamics simulations; finally, 20 compounds were obtained. Among them, compound G490-0341 showed the best stable structure and was a promising composite for further development of WSB1 inhibitors.

The Inhibitors of CDK4/6 from a Library of Marine Compound Database: A Pharmacophore, ADMET, Molecular Docking and Molecular Dynamics Study

Background: CDK4/6 (Cyclin-dependent kinases 4/6) are the key promoters of cell cycle transition from G1 phase to S phase. Thus, selective inhibition of CDK4/6 is a promising cancer treatment. Methods: A total of 52,765 marine natural products were screened for CDK4/6. To screen out better natural compounds, pharmacophore models were first generated, then the absorption, distribution, metabolism, elimination, and toxicity (ADMET) were tested, followed by molecular docking. Finally, molecular dynamics simulation was carried out to verify the binding characteristics of the selected compounds. Results: Eighty-seven marine small molecules were screened based on the pharmacophore model. Then, compounds 41369 and 50843 were selected according to the ADMET and molecular docking score for further kinetic simulation evaluation. Finally, through molecular dynamics analysis, it was confirmed that compound 50843 maintained a stable conformation with the target protein, so it has the opportunity to become an inhibitor of CDK4/6. Conclusion: Through structure-based pharmacophore modeling, ADMET, the molecular docking method and molecular dynamics (MD) simulation, marine natural compound 50843 was proposed as a promising marine inhibitor of CDK4/6.

Artemisia alleviates AGE-induced liver complications via MAPK and RAGE signaling pathways modulation: a combinatorial study

Artemisia herba-alba (AHA) is a traditionally used plant to treat various diseases, including diabetes and metabolic dysfunctions. Plant extracts are generally explored empirically without a deeper assessment of their mechanism of action. Here, we describe a combinatorial study of biochemical, molecular, and bioinformatic (metabolite-protein pharmacology network) analyses to elucidate the mechanism of action of AHA and shed light on its multilevel effects in the treatment of diabetes-related advanced glycation end-products (AGE)-induced liver damages. The extract's polyphenols and flavonoids content were measured and then identified via LC-Q-TOF-MS/MS. Active compounds were used to generate a metabolite-target interaction network via Swiss Target Prediction and other databases. The extract was tested for its antiglycation and aggregation properties. Next, THLE-2 liver cells were challenged with AGEs, and the mechanistic markers were measured [TNF-α, IL-6, nitric oxide, total antioxidant capacity, lipid peroxidation (LPO), and caspase 3]. Metabolite and network screening showed the involvement of AHA in diabetes, glycation, liver diseases, aging, and apoptosis. Experimental confirmation showed that AHA inhibited protein modification and AGE formation. Additionally, AHA reduced inflammatory mediators (IL-6, TNFα), oxidative stress markers (NO, LPO), and apoptosis (Caspase 3). On the other hand, cellular total antioxidant capacity was restored to normal levels. The combinatorial study showed that AHA regulates AGE-induced liver damages through MAPK-AKT and AGE-RAGE signaling pathways. This report highlights the combination of experimental and network pharmacology for the exact elucidation of AHA mechanism of action as a multitarget option in the therapy of diabetes and AGEs-related diseases.

Integrated virtual screening and molecular dynamics simulation revealed promising drug candidates of p53-MDM2 interaction

In the vast majority of malignancies, the p53 tumor suppressor pathway is compromised. In some cancer cells, high levels of MDM2 polyubiquitinate p53 and mark it for destruction, thereby leading to a corresponding downregulation of the protein. MDM2 interacts with p53 via its hydrophobic pocket, and chemical entities that block the dimerization of the protein-protein complex can restore p53 activity. Thus far, only a few chemical compounds have been reported as potent arsenals against p53-MDM2. The Protein Data Bank has crystallogaphic structures of MDM2 in complex with certain compounds. Herein, we have exploited one of the complexes in the identification of new p53-MDM2 antagonists using a hierarchical virtual screening technique. The initial stage was to compile a targeted library of structurally appropriate compounds related to a known effective inhibitor, Nutlin 2, from the PubChem database. The identified 57 compounds were subjected to virtual screening using molecular docking to discover inhibitors with high binding affinity for MDM2. Consequently, five compounds with higher binding affinity than the standard emerged as the most promising therapeutic candidates. When compared to Nutlin 2, four of the drug candidates (CID_140017825, CID_69844501, CID_22721108, and CID_22720965) demonstrated satisfactory pharmacokinetic and pharmacodynamic profiles. Finally, MD simulation of the dynamic behavior of lead-protein complexes reveals the stability of the complexes after a 100,000 ps simulation period. In particular, when compared to the other three leads, overall computational modeling found CID_140017825 to be the best pharmacological candidate. Following thorough experimental trials, it may emerge as a promising chemical entity for cancer therapy.

Synthesis, spectral analysis, DFT calculations, biological potential and molecular docking studies of indole appended pyrazolo-triazine

A series of novel 5-(3,5-disubstituted-1H-indol-2-yl)-2,3-dimethyl-1-phenyl-2,6-dihydro-1H-pyrazolo[4,3-e][1,2,4]triazines (3a-l) were synthesized in single step from 3,5-disubstituted indole-2-carbohydrazide and 4-aminoantipyrine under acidic conditions with excellent yields. The various spectroscopic methods were used to prove the formation of all these products. The compounds 3a, 3b, 3e, 3f, 3i and 3j exhibited excellent antibacterial and antifungal activities with an MIC value of 3.125 µg/ml against the tested pathogens and anti-tuberculosis inhibitory potential against M. tuberculosis which is equivalent to standard drug. The antidiabetic activity of the compounds 3a and 3b showed the maximum potential as glucosidase inhibitors with IC₅₀ = 47.21 μg/ml and IC₅₀ = 48.36 μg/ml, respectively. The physicochemical characteristics like ADMET, drug-likeness and bioactivity scores for these molecules were also disclosed. To comprehend the electronic behavior of compound 3a, density functional theory estimations at the DFT/B3LYP level via 6-31G++ (d, p) have been carried out to replicate the structure and geometry. The first-order hyperpolarizability calculation was used to calculate the nonlinear visual feature of compound 3a. The charge transfer interface among the structure is elucidated by the estimated HOMO-LUMO analysis. Further, molecular docking studies were carried out for synthesized compounds with human maltase-glucoamylase (PDB: 2QMJ).

Natural Compound ZINC12899676 Reduces Porcine Epidemic Diarrhea Virus Replication by Inhibiting the Viral NTPase Activity

Porcine epidemic diarrhea virus (PEDV) is an alphacoronavirus (α-CoV) that causes high mortality in suckling piglets, leading to severe economic losses worldwide. No effective vaccine or commercial antiviral drug is readily available. Several replicative enzymes are responsible for coronavirus replication. In this study, the potential candidates targeting replicative enzymes (PLP2, 3CLpro, RdRp, NTPase, and NendoU) were screened from 187,119 compounds in ZINC natural products library, and seven compounds had high binding potential to NTPase and showed drug-like property. Among them, ZINC12899676 was identified to significantly inhibit the NTPase activity of PEDV by targeting its active pocket and causing its conformational change, and ZINC12899676 significantly inhibited PEDV replication in IPEC-J2 cells. It first demonstrated that ZINC12899676 inhibits PEDV replication by targeting NTPase, and then, NTPase may serve as a novel target for anti-PEDV.

Identification of Bioactive Components of Stephania epigaea Lo and Their Potential Therapeutic Targets by UPLC-MS/MS and Network Pharmacology

Stephania epigaea, an important traditional folk medicinal plant, elucidating its bioactive compound profiles and their molecular mechanisms of action on human health, would better understand its traditional therapies and guide their use in preclinical and clinical. This study aims to detect the critical therapeutic compounds, predict their targets, and explore potential therapeutic molecular mechanisms. This work first determined metabolites from roots, stems, and flowering twigs of S. epigaea by a widely targeted metabolomic analysis assay. Then, the drug likeness of the compounds and their pharmacokinetic profiles were screened by the ADMETlab server. The target proteins of active compounds were further analyzed by PPI combing with GO and KEGG cluster enrichment analysis. Finally, the interaction networks between essential compounds, targets, and disease-associated pathways were constructed, and the essential compounds binding to their possible target proteins were verified by molecular docking. Five key target proteins (EGFR, HSP90AA1, SRC, TNF, and CASP3) and twelve correlated metabolites, including aknadinine, cephakicine, homostephanoline, and N-methylliriodendronine associated with medical applications of S. epigaea, were identified, and the compounds and protein interactions were verified. The key active ingredients are mainly accumulated in the root, which indicates that the root is the main medicinal tissue. This study demonstrated that S. epigaea might exert the desired disease efficacy mainly through twelve components interacting via five essential target proteins. EGFR is the most critical one, which deserves further verification by biological studies.

A Tau Pathogenesis-Based Network Pharmacology Approach for Exploring the Protections of Chuanxiong Rhizoma in Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common cause of neurodegenerative dementia and one of the top medical concerns worldwide. Currently, the approved drugs to treat AD are effective only in treating the symptoms, but do not cure or prevent AD. Although the exact causes of AD are not understood, it is recognized that tau aggregation in neurons plays a key role. Chuanxiong Rhizoma (CR) has been widely reported as effective for brain diseases such as dementia. Thus, we explored the protections of CR in AD by a tau pathogenesis–based network pharmacology approach. According to ultra-HPLC with triple quadrupole mass spectrometry data and Lipinski’s rule of five, 18 bioactive phytochemicals of CR were screened out. They were shown corresponding to 127 tau pathogenesis–related targets, among which VEGFA, IL1B, CTNNB1, JUN, ESR1, STAT3, APP, BCL2L1, PTGS2, and PPARG were identified as the core ones. We further analyzed the specific actions of CR-active phytochemicals on tau pathogenesis from the aspects of tau aggregation and tau-mediated toxicities. It was shown that neocnidilide, ferulic acid, coniferyl ferulate, levistilide A, Z-ligustilide, butylidenephthalide, and caffeic acid can be effective in reversing tau hyperphosphorylation. Neocnidilide, senkyunolide A, butylphthalide, butylidenephthalide, Z-ligustilide, and L-tryptophan may be effective in promoting lysosome-associated degradation of tau, and levistilide A, neocnidilide, ferulic acid, L-tryptophan, senkyunolide A, Z-ligustilide, and butylidenephthalide may antagonize tau-mediated impairments of intracellular transport, axon and synaptic damages, and neuron death (especially apoptosis). The present study suggests that acting on tau aggregation and tau-mediated toxicities is part of the therapeutic mechanism of CR against AD.

Identification of potent inhibitors of NEK7 protein using a comprehensive computational approach

NIMA related Kinases (NEK7) plays an important role in spindle assembly and mitotic division of the cell. Over expression of NEK7 leads to the progression of different cancers and associated malignancies. It is becoming the next wave of targets for the development of selective and potent anti-cancerous agents. The current study is the first comprehensive computational approach to identify potent inhibitors of NEK7 protein. For this purpose, previously identified anti-inflammatory compound i.e., Phenylcarbamoylpiperidine-1,2,4-triazole amide derivatives by our own group were selected for their anti-cancer potential via detailed Computational studies. Initially, the density functional theory (DFT) calculations were carried out using Gaussian 09 software which provided information about the compounds' stability and reactivity. Furthermore, Autodock suite and Molecular Operating Environment (MOE) software's were used to dock the ligand database into the active pocket of the NEK7 protein. Both software performances were compared in terms of sampling power and scoring power. During the analysis, Autodock results were found to be more reproducible, implying that this software outperforms the MOE. The majority of the compounds, including M7, and M12 showed excellent binding energies and formed stable protein-ligand complexes with docking scores of - 29.66 kJ/mol and - 31.38 kJ/mol, respectively. The results were validated by molecular dynamics simulation studies where the stability and conformational transformation of the best protein-ligand complex were justified on the basis of RMSD and RMSF trajectory analysis. The drug likeness properties and toxicity profile of all compounds were determined by ADMETlab 2.0. Furthermore, the anticancer potential of the potent compounds were confirmed by cell viability (MTT) assay. This study suggested that selected compounds can be further investigated at molecular level and evaluated for cancer treatment and associated malignancies.

Knowledge-based BERT: a method to extract molecular features such as computational chemists

Molecular property prediction models based on machine learning algorithms have become important tools to triage unpromising lead molecules in the early stages of drug discovery. Compared with the mainstream descriptor- and graph-based methods for molecular property predictions, SMILES-based methods can directly extract molecular features from SMILES without human expert knowledge, but they require more powerful algorithms for feature extraction and a larger amount of data for training, which makes SMILES-based methods less popular. Here, we show the great potential of pre-training in promoting the predictions of important pharmaceutical properties. By utilizing three pre-training tasks based on atom feature prediction, molecular feature prediction and contrastive learning, a new pre-training method K-BERT, which can extract chemical information from SMILES like chemists, was developed. The calculation results on 15 pharmaceutical datasets show that K-BERT outperforms well-established descriptor-based (XGBoost) and graph-based (Attentive FP and HRGCN+) models. In addition, we found that the contrastive learning pre-training task enables K-BERT to 'understand' SMILES not limited to canonical SMILES. Moreover, the general fingerprints K-BERT-FP generated by K-BERT exhibit comparative predictive power to MACCS on 15 pharmaceutical datasets and can also capture molecular size and chirality information that traditional binary fingerprints cannot capture. Our results illustrate the great potential of K-BERT in the practical applications of molecular property predictions in drug discovery.

Machine Learning Analysis of Cocaine Addiction Informed by DAT, SERT, and NET-Based Interactome Networks

Cocaine addiction is a psychosocial disorder induced by the chronic use of cocaine and causes a large number of deaths around the world. Despite decades of effort, no drugs have been approved by the Food and Drug Administration (FDA) for the treatment of cocaine dependence. Cocaine dependence is neurological and involves many interacting proteins in the interactome. Among them, the dopamine (DAT), serotonin (SERT), and norepinephrine (NET) transporters are three major targets. Each of these targets has a large protein-protein interaction (PPI) network, which must be considered in the anticocaine addiction drug discovery. This work presents DAT, SERT, and NET interactome network-informed machine learning/deep learning (ML/DL) studies of cocaine addiction. We collected and analyzed 61 protein targets out of 460 proteins in the DAT, SERT, and NET PPI networks that have sufficiently large existing inhibitor datasets. Utilizing autoencoder (AE) and other ML/DL algorithms, including gradient boosting decision tree (GBDT) and multitask deep neural network (MT-DNN), we built predictive models for these targets with 115 407 inhibitors to predict drug repurposing potential and possible side effects. We further screened their absorption, distribution, metabolism, and excretion, and toxicity (ADMET) properties to search for leads having potential for developing treatments for cocaine addiction. Our approach offers a new systematic protocol for artificial intelligence (AI)-based anticocaine addiction lead discovery.

Discovery and Optimization of 6-(1-Substituted pyrrole-2-yl)-s-triazine Containing Compounds as Antibacterial Agents

Antimicrobial drug resistance is a major health issue plaguing healthcare worldwide and leading to hundreds of thousands of deaths globally each year. Tackling this problem requires discovery and development of new antibacterial agents. In this study, we discovered novel 6-(1-substituted pyrrole-2-yl)-s-triazine containing compounds that potently inhibited the growth of Staphylococcus aureus regardless of its methicillin-resistant status, displaying minimum inhibitory concentration (MIC) values as low as 1 μM. The presence of a single imidazole substituent was critical to the antibacterial activity of these compounds. Some of the compounds also inhibited several nontubercular mycobacteria. We have shown that these molecules are potent bacteriostatic agents and that they are nontoxic to mammalian cells at relevant concentrations. Further development of these compounds as novel antimicrobial agents will be aimed at expanding our armamentarium of antibiotics.

In Silico Identification and Mechanism Exploration of Active Ingredients against Stroke from An-Gong-Niu-Huang-Wan (AGNHW) Formula

An-Gong-Niu-Huang-Wan (AGNHW) is a well-known formula for treating cerebrovascular diseases, with roles including clearing away heat, detoxification, and wake-up consciousness. In recent years, AGNHW has been commonly used for the treatment of ischemic stroke, but the mechanism by which AGNHW relieves stroke has not been clearly elucidated. In the current study, we developed a multiple systems pharmacology-based framework to identify the potential antistroke ingredients in AGNHW and explore the underlying mechanisms of action (MOA) of AGNHW against stroke from a holistic perspective. Specifically, we performed a network-based method to identify the potential antistroke ingredients in AGNHW by integrating the drug-target network and stroke-associated genes. Furthermore, the oxygen-glucose deprivation/reoxygenation (OGD/R) model was used to validate the anti-inflammatory effects of the key ingredients by determining the levels of inflammatory cytokines, including interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α. The antiapoptotic effects of the key ingredients were also confirmed in vitro. Integrated pathway analysis of AGNHW revealed that it might regulate three biological signaling pathways, including IL-17, TNF, and PI3K-AKT, to play a protective role in stroke. Moreover, 30 key antistroke ingredients in AGNHW were identified via network-based in silico prediction and were confirmed to have known neuroprotective effects. After drug-like property evaluation and pharmacological validation in vitro, scutellarein (SCU) and caprylic acid (CA) were selected for further antistroke investigation. Finally, systems pharmacology-based analysis of CA and SCU indicated that they might exert antistroke effects via the apoptotic signaling pathway and inflammatory response, which was further validated in an in vitro stroke model. Overall, the current study proposes an integrative systems pharmacology approach to identify antistroke ingredients and demonstrate the underlying pharmacological MOA of AGNHW in stroke, which provides an alternative strategy to investigate novel traditional Chinese medicine formulas for complex diseases.

An efficient curriculum learning-based strategy for molecular graph learning

Computational methods have been widely applied to resolve various core issues in drug discovery, such as molecular property prediction. In recent years, a data-driven computational method-deep learning had achieved a number of impressive successes in various domains. In drug discovery, graph neural networks (GNNs) take molecular graph data as input and learn graph-level representations in non-Euclidean space. An enormous amount of well-performed GNNs have been proposed for molecular graph learning. Meanwhile, efficient use of molecular data during training process, however, has not been paid enough attention. Curriculum learning (CL) is proposed as a training strategy by rearranging training queue based on calculated samples' difficulties, yet the effectiveness of CL method has not been determined in molecular graph learning. In this study, inspired by chemical domain knowledge and task prior information, we proposed a novel CL-based training strategy to improve the training efficiency of molecular graph learning, called CurrMG. Consisting of a difficulty measurer and a training scheduler, CurrMG is designed as a plug-and-play module, which is model-independent and easy-to-use on molecular data. Extensive experiments demonstrated that molecular graph learning models could benefit from CurrMG and gain noticeable improvement on five GNN models and eight molecular property prediction tasks (overall improvement is 4.08%). We further observed CurrMG’s encouraging potential in resource-constrained molecular property prediction. These results indicate that CurrMG can be used as a reliable and efficient training strategy for molecular graph learning.

Availability: The source code is available in https://github.com/gu-yaowen/CurrMG.

Design, synthesis and antibacterial evaluation of pleuromutilin derivatives

We report herein the design, synthesis, and structure-activity relationship studies of pleuromutilin derivatives containing urea/thiourea functionalities. The antibacterial activities of these new pleuromutilin derivatives were evaluated in vitro against Gram-positive pathogens (GPPs) (Staphylococcus aureus, Staphylococcus epidermidis and Enterococcus faecium) and Mycoplasma pneumoniae by the broth dilution method. Most of the targeted compounds exhibit good potency in inhibiting the growth of pathogens including Methicillin-susceptible S. aureus (MSSA, ATCC29213, MIC: 0.0625-16 μg/mL), Methicillin-resistant S. aureus (MRSA, ATCC43300, MIC: 0.125-16 μg/mL) and M. pneumoniae (ATCC15531 MIC: 0.125-1 μg/mL, ATCC29342 MIC: 0.0625-0.25 μg/mL and drug resistant strain MIC: 0.5-2 μg/mL). In particular, the compounds 6m and 6n containing phenyl-urea group showed excellent activity with the MIC value less than 0.0625 μg/mL against S. aureus ATCC29213. The compound 6h exhibited better activity than tiamulin against Methicillin-resistant S. aureus ATCC43300.

Novel benzothiazole‒urea hybrids: Design, synthesis and biological activity as potent anti-bacterial agents against MRSA

Novel benzothiazole‒urea hybrids were designed, synthesized and evaluated their anti-bacterial activity. They only exhibited anti-bacterial activity against Gram-positive bacteria, including clinical methicillin-resistant S. aureus (MRSA), compounds 5f, 5i, 8e, 8k and 8l exhibited potent activity (MIC = 0.39 and 0.39/0.78 μM against SA and MRSA, respectively). Crystal violet assay showed that compounds 5f, 8e and 8l not only inhibited the formation of biofilms but also eradicated preformed biofilms. Compound 8l had membrane disruption, little propensity to induce resistance, benign safety and in vivo anti-MRSA efficacy in a mouse model of abdominal infection. Therefore, our data demonstrated the potential to advance benzothiazole‒urea hybrids as a new class of antibiotics.

Molecular docking analysis of estrogen receptor binding phytocomponents identified from the ethyl acetate extract of Salicornia herbacea (L)

It is of interest to evaluate the secondary metabolites using high performance thin layer chromatography (HPTLC) finger printing and Gas chromatography-Mass spectroscopy (GC-MS) in S. herbaceaextract. The powdered plant material extracted using different solvents were used for the qualitative analysis of alkaloids, flavonoids, terpenoids and saponins followed by HPTLC finger printing and GC-MS analysis. The components identified in the GC-MS were docked with estrogen receptor (ER) to identify the binding specificity of isolated compounds. The ethyl acetate extract of S. herbaceashowed the presence of high number of secondary metabolites when compared to other solvent system. The qualitative analysis of the plant material also showed the presence of carbohydrates, protein, amino acid, phenol, flavonoids, terpenoids, glycosides, saponins and steroids. The HPTLC finger printing analysis revealed the existence of alkaloid, flavonoid, terpenoid and saponin compounds and GC-MS. GC-MS was performed to identify the phytocomponents constituents in the extract. 8 phytocompounds were identified to analyse binding with ER. The binding affinity score (-6.8 kcal/mol) and interacting ER residues (28) the phyto compound di-n-octyl phthalate showed best docking score with ER α than the standard drugs lasofoxifene, and 4-hydroxytamoxifen. The binding affinity and number of interacting ER residues was -6.9 kcal/mol; 10 and -6.2; 11, respectively. The results identified the presence of ER antagonist in S. herbaceaand warrants further investigation to explore for treating ER regulated diseases.

Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications

The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world at a rapid speed. Among COVID-19 patients, SARS-CoV-2-associated acute respiratory distress syndrome (ARDS) is the main contribution to the high ratio of morbidity and mortality. However, clinical manifestations between SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS are quite common, and their therapeutic treatments are limited because the intricated pathophysiology having been not fully understood. In this study, to investigate the pathogenic mechanism of SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS, first, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining. With the help of host-pathogen RNA sequencing (RNA-Seq) data, real HPI-GWGEN of COVID-19-associated ARDS and non-viral ARDS were obtained by system modeling, system identification, and Akaike information criterion (AIC) model order selection method to delete the false positives in candidate HPI-GWGEN. For the convenience of mitigation, the principal network projection (PNP) approach is utilized to extract core HPI-GWGEN, and then the corresponding core signaling pathways of COVID-19-associated ARDS and non-viral ARDS are annotated via their core HPI-GWGEN by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-associated ARDS and non-viral ARDS, we identified essential biomarkers as drug targets of pathogenesis by comparing the core signal pathways between COVID-19-associated ARDS and non-viral ARDS. The deep neural network of the drug–target interaction (DNN-DTI) model could be trained by drug–target interaction databases in advance to predict candidate drugs for the identified biomarkers. We further narrowed down these predicted drug candidates to repurpose potential multiple-molecule drugs by the filters of drug design specifications, including regulation ability, sensitivity, excretion, toxicity, and drug-likeness. Taken together, we not only enlighten the etiologic mechanisms under COVID-19-associated ARDS and non-viral ARDS but also provide novel therapeutic options for COVID-19-associated ARDS and non-viral ARDS.

DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development

Drug-likeness is a vital consideration when selecting compounds in the early stage of drug discovery. A series of drug-like properties are needed to predict the drug-likeness of a given compound and provide useful guidelines to increase the likelihood of converting lead compounds into drugs. Experimental physicochemical properties, pharmacokinetic/toxicokinetic properties and maximum dosages of approved small-molecule drugs from multiple text-based unstructured data resources have been manually assembled, curated, further digitized and processed into structured data, which are deposited in the Database of Digital Properties of approved Drugs (DDPD). DDPD 1.0 contains 30 212 drug property entries, including 2250 approved drugs and 32 properties, in a standardized value/unit format. Moreover, two analysis tools are provided to examine the drug-likeness features of given molecules based on the collected property data of approved drugs. Additionally, three case studies are presented to demonstrate how users can utilize the database. We believe that this database will be a valuable resource for the drug discovery and development field.

Database URL: http://www.inbirg.com/ddpd

IP-Se-06, a Selenylated Imidazo[1,2-a]pyridine, Modulates Intracellular Redox State and Causes Akt/mTOR/HIF-1α and MAPK Signaling Inhibition, Promoting Antiproliferative Effect and Apoptosis in Glioblastoma Cells

Glioblastoma multiforme (GBM) is a notably lethal brain tumor associated with high proliferation rate and therapeutic resistance, while currently effective treatment options are still lacking. Imidazo[1,2-a]pyridine derivatives and organoselenium compounds are largely used in medicinal chemistry and drug development. This study is aimed at further investigating the antitumor mechanism of IP-Se-06 (3-((2-methoxyphenyl)selanyl)-7-methyl-2-phenylimidazol[1,2-a]pyridine), a selenylated imidazo[1,2-a]pyridine derivative in glioblastoma cells. IP-Se-06 exhibited high cytotoxicity against A172 cells (IC₅₀ = 1.8 μM) and selectivity for this glioblastoma cell. The IP-Se-06 compound has pharmacological properties verified in its ADMET profile, especially related to blood-brain barrier (BBB) permeability. At low concentration (1 μM), IP-Se-06 induced intracellular redox state modulation with depletion of TrxR and GSH levels as well as inhibition of NRF2 protein. IP-Se-06 also decreased mitochondrial membrane potential, induced cytochrome c release, and chromatin condensation. Furthermore, IP-Se-06 induced apoptosis by decreasing levels of Bcl-xL while increasing levels of γ-H2AX and p53 proteins. Treatment with IP-Se-06 induced cell cycle arrest and showed antiproliferative effect by inhibition of Akt/mTOR/HIF-1α and ERK 1/2 signaling pathways. In addition, IP-Se-06 displayed significant inhibition of p38 MAPK and p-p38, leading to inhibition of inflammasome complex proteins (NLRP3 and caspase-1) in glioblastoma cells. These collective findings demonstrated that IP-Se-06 is a bioactive molecule that can be considered a candidate for the development of a novel drug for glioblastoma treatment.

Discovery of novel microtubule stabilizers targeting taxane binding site by applying molecular docking, molecular dynamics simulation, and anticancer activity testing

Disruption of the dynamic equilibrium of microtubules can induce cell cycle arrest in G2/M phase and apoptosis. Hence, discovery of novel tubulin polymerization inhibitors is very necessary and an important task in drug research and development for treatment of various tumors. In this investigation, 50 compounds were screened as microtubule stabilizers targeting the taxane site by combination of molecular docking methods. Among these hits, hits 19 and 38 with novel scaffolds exhibited the highest anti-proliferative activity with IC₅₀ ranging from 9.50 to 13.81 μM in four cancer cell lines. The molecular dynamics simulations confirmed that tubulin and two hits could form stable systems. Meanwhile, the mechanism of the interactions between tubulin and two hits at simulated physiological conditions were probed. The in vitro tubulin polymerization assay revealed hits 19 and 38 were able to promote tubulin polymerization in a dose-dependent manner. Further, the immunofluorescence assay suggested that hits 19 and 38 could accelerate microtubule assembly in A549 and HeLa cells. Finally, studies on antitumor activity indicated that hits 19 and 38 induced G2/M phase cell cycle arrest and apoptosis, and inhibited cancer cell motility and migration in A549 and HeLa cells. Importantly, hit38 exhibited better anti-tubulin and anti-cancer activity than hit19 in A549 and HeLa cells. Therefore, these results suggest that hit38 represents a promising microtubule stabilizer for treating cancer and deserves further investigation.

Network pharmacology prediction and molecular docking-based strategy to explore the potential mechanism of Huanglian Jiedu Decoction against sepsis

BACKGROUND

Huanglian Jiedu Decoction (HLJDD) is a classical herbal formula with potential efficacy in the treatment of sepsis. However, the main components and potential mechanisms of HLJDD remain unclear. This study aims to initially clarify the potential mechanism of HLJDD in the treatment of sepsis based on network pharmacology and molecular docking techniques.

METHODS

The principal components and corresponding protein targets of HLJDD were searched on TCMSP, BATMAN-TCM and ETCM and the compound-target network was constructed by Cytoscape3.8.2. Sepsis targets were searched on OMIM and DisGeNET databases. The intersection of compound target and disease target was obtained and the coincidence target was imported into STRING database to construct a PPI network. We further performed GO and KEGG enrichment analysis on the targets. Finally, molecular docking study was approved for the core target and the active compound.

RESULTS

There are 257 nodes and 792 edges in the component target network. The compounds with a higher degree value are quercetin, kaempferol, and wogonin. The protein with a higher degree in the PPI network is JUN, RELA, TNF. GO and KEGG analysis showed that HLJDD treatment of sepsis mainly involves positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptosis process, response to hypoxia and other biological processes. The signaling pathways mainly include PI3K-AKT, MAPK, TNF signaling pathway. The molecular docking results showed that quercetin, kaempferol and wogonin have higher affinity with JUN, RELA and TNF.

CONCLUSION

This study reveals the active ingredients and potential molecular mechanism of HLJDD in the treatment of sepsis, and provides a reference for subsequent basic research.