Potential Active Molecules Research Articles

Advancing Bioactivity Prediction through Molecular Docking and Self-Attention.

Bioactivity refers to the ability of a substance to induce biological effects within living systems, often describing the influence of molecules, drugs, or chemicals on organisms. In drug discovery, predicting bioactivity streamlines early-stage candidate screening by swiftly identifying potential active molecules. The popular deep learning methods in bioactivity prediction primarily model the ligand structure-bioactivity relationship under the premise of Quantitative Structure-Activity Relationship (QSAR). However, bioactivity is determined by multiple factors, including not only the ligand structure but also drug-target interactions, signaling pathways, reaction environments, pharmacokinetic properties, and species differences. Our study first integrates drug-target interactions into bioactivity prediction using protein-ligand complex data from molecular docking. We devise a Drug-Target Interaction Graph Neural Network (DTIGN), infusing interatomic forces into intermolecular graphs. DTIGN employs multi-head self-attention to identify native-like binding pockets and poses within molecular docking results. To validate the fidelity of the self-attention mechanism, we gather ground truth data from crystal structure databases. Subsequently, we employ these limited native structures to refine bioactivity prediction via semi-supervised learning. For this study, we establish a unique benchmark dataset for evaluating bioactivity prediction models in the context of protein-ligand complexes, showcasing the superior performance of our method (with an average improvement of 27.03%) through comparison with 9 leading deep learning-based bioactivity prediction methods.

Structure-based virtual screening towards the discovery of novel thrombin inhibitors with Anti-HCC activities.

Hepatic carcinoma (HCC) is one of the most lethal malignant tumors in the world, and new treatment regimens for this disease are urgently needed. Studies have shown that thrombin stimulates tumor progression by forming fibrin and activating platelets. Dabigatran etexilate, a thrombin inhibitor, can inhibit the activity of thrombin and prevent the proliferation and metastasis of HCC in cells and nude mice. The present study was designed to find thrombin inhibitors with novel skeletons, and further confirm the correlation between thrombin inhibition and HCC prevention to identify potential anti-HCC drug leads. The potential thrombin inhibitors were firstly screened in the Topscience Database, and 20 potential active molecules were found by molecular docking. The effect of these molecules on thrombin inhibition, coagulation and tumor proliferation were evaluated, and the definite activity of ZXX-4 was identified. Further in vivo assays in nude mice showed that ZXX-4 inhibited tumor proliferation in nude mice, reduced tumor metastasis, and enhanced the clinical efficacy of first-line drug sorafenib for the treatment of HCC. ZXX-4 can be further explored as an anti-tumor lead compound with a novel skeleton, and inhibition of thrombin can serve as a potential treatment strategy for HCC.

A Review of Therapeutic Strategies against Cardiac Fibrosis: From Classical Pharmacology to Novel Molecular, Epigenetic, and Biotechnological Approaches.

Cardiovascular diseases are the first cause of death worldwide, with a heavy social and economic impact. They include a wide range of pathological conditions, among which cardiac fibrosis represents a common pathogenetic hallmark. The fibrotic process is driven by cardiac mesenchymal stromal cells, namely fibroblasts, which become activated, proliferate, and differentiate into myofibroblasts in response to several stimuli, in the end secreting extracellular matrix proteins, and mediating cardiac tissue remodelling and stiffening. A specific therapy for the exclusive treatment of cardiac fibrosis is still lacking. Given the growing quest for reducing the burden of cardiovascular diseases, there is increasing interest in the search for new effective anti-fibrotic therapies. In this review, we will briefly summarize the limited pharmacological therapies known to act, at least in part, against cardiac fibrosis. Then we will present novel potential active molecules, molecular targets, and biotechnological approaches emerged in the last decade, as possible future therapeutic strategies for cardiac fibrosis, with a specific focus on targeting fibroblast activation and function.

Isolation and Identification of Pennogenin Tetraglycoside from Cestrum nocturnum (Solanaceae) and Its Antifungal Activity against Fusarium kuroshium, Causal Agent of Fusarium Dieback.

Antifungal assay-guided fractionation of the methanolic crude extract of Cestrum nocturnum (Solanaceae), popular known as ‘lady of the night’, led the isolation and identification of the steroidal saponin named pennogenin tetraglycoside, which was identified for the first time in this plant species by spectroscopic means. The crude extract, fractions and pennogenin tetraglycoside exhibited mycelial growth inhibition of Fusarium solani and F. kuroshium. F. solani is a cosmopolitan fungal phytopathogen that affects several economically important crops. However, we highlight the antifungal activity displayed by pennogenin tetraglycoside against F. kuroshium, since it is the first plant natural product identified as active for this phytopathogen. This fungus along with its insect symbiont known as Kuroshio shot hole borer (Euwallacea kuroshio) are the causal agents of the plant disease Fusarium dieback that affects more than 300 plant species including avocado (Persea americana) among others of ecological relevance. Scanning electron microscopy showed morphological alterations of the fungal hyphae after exposure with the active fractions and 12 phenolic compounds were also identified by mass spectrometry dereplication as part of potential active molecules present in C. nocturnum leaves.

In silico study of medicinal plants with cyclodextrin inclusion complex as the potential inhibitors against SARS-CoV-2 main protease (Mpro) and spike (S) receptor.

The current outbreak of novel coronavirus disease (COVID-19) causes an alarming number of deaths in 221 countries around the world. Nowadays, there is no specific and effective drug regimen for curing COVID-19. Since the COVID-19 pandemic, several medicinal plants with promising results in the previous SARS-CoV could be used to treat SARS-CoV-2 infected patients. This work assesses proven medicinal plants as potential inhibitors against SARS-CoV-2 main protease (Mpro) and spike (S) receptors by employing in silico methods. Molecular docking studies and 3D structure-based pharmacophore modeling were performed to identify the molecular interactions of potential active molecules with the Mpro and (S) receptor of SARS-CoV-2. The drug-likeness and ADME properties were also predicted to support the drug-like nature of the selected active molecules. The results indicated that the most favorable ligand was Terrestriamide with (ΔG: ─8.70 kcal/mol; Ki: 0.417 μM) and (ΔG: ─7.02 kcal/mol; Ki: 7.21 μM) for Mpro and (S) receptor, respectively. Terrestriamide is also supported with a high drug-likeness value and appropriate ADME profile. Furthermore, to improve drug delivery, the cyclodextrin inclusion complex was calculated based on semi-empirical quantum mechanical methods. Terrestriamide/γ−cyclodextrin is the most favorable pathway of inclusion complex formation and could be used to treat COVID-19.

Uncovering the pharmacological mechanism of the effects of the Banxia-Xiakucao Chinese Herb Pair on sleep disorder by a systems pharmacology approach

Sleep disorder (SD) has a high incidence and seriously affects quality of life, mental health and even the manifestation of physical diseases. The combination of Pinellia ternata (Chinese name: banxia) and Prunella vulgaris (Chinese name: xiakucao), known as the Banxia–Xiakucao Chinese herb pair (BXHP), is a proven Chinese herbal medicine that has been used to treat SD for thousands of years due to its significant clinical effects. However, its active pharmacological components and sedative–hypnotic mechanisms have not been fully elucidated. Thus, the present study used a systematic pharmacological approach to develop pharmacokinetic screens and target predictions via construction of a protein–protein interaction network and annotation database for SD-related and putative BXHP-related targets. Visualization, screening and integrated discovery enrichment analyses were conducted. The BXHP chemical database contains 166 compounds between the two herbal ingredients, and of these, 22 potential active molecules were screened by pharmacokinetic evaluation. The targets of 114 of the active molecules were predicted, and 34 were selected for further analysis. Finally, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses suggested that BXHP can reduce inflammatory responses. and mediate immune-related and central nervous system neurotransmitters via regulation of multiple targets and pathways. The use of a systematic pharmacology-based approach in the present study further elucidated the mechanisms of action underlying BXHP for the treatment of SD from a holistic perspective and sheds light on the systemic mechanisms of action of Chinese herbal medicines in general.

Techniques and Strategies for Potential Protein Target Discovery and Active Pharmaceutical Molecule Screening in a Pandemic.

Viruses remain a major challenge in the fierce fight against diseases. There have been many pandemics caused by various viruses throughout the world over the years. Recently, the global outbreak of COVID-19 has had a catastrophic impact on human health and the world economy. Antiviral drug treatment has become another essential means to overcome pandemics in addition to vaccine development. How to quickly find effective drugs that can control the development of a pandemic is a hot issue that still needs to be resolved in medical research today. To accelerate the development of drugs, it is necessary to target the key target proteins in the development of the pandemic, screen active molecules, and develop reliable methods for the identification and characterization of target proteins based on the active ingredients of drugs. This article discusses key target proteins and their biological mechanisms in the progression of COVID-19 and other major epidemics. We propose a model based on these foundations, which includes identifying potential core targets, screening potential active molecules of core targets, and verifying active molecules. This article summarizes the related innovative technologies and methods. We hope to provide a reference for the screening of drugs related to pandemics and the development of new drugs.

Identification of Novel Src Inhibitors: Pharmacophore-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations

Src plays a crucial role in many signaling pathways and contributes to a variety of cancers. Therefore, Src has long been considered an attractive drug target in oncology. However, the development of Src inhibitors with selectivity and novelty has been challenging. In the present study, pharmacophore-based virtual screening and molecular docking were carried out to identify potential Src inhibitors. A total of 891 molecules were obtained after pharmacophore-based virtual screening, and 10 molecules with high docking scores and strong interactions were selected as potential active molecules for further study. Absorption, distribution, metabolism, elimination and toxicity (ADMET) property evaluation was used to ascertain the drug-like properties of the obtained molecules. The proposed inhibitor–protein complexes were further subjected to molecular dynamics (MD) simulations involving root-mean-square deviation and root-mean-square fluctuation to explore the binding mode stability inside active pockets. Finally, two molecules (ZINC3214460 and ZINC1380384) were obtained as potential lead compounds against Src kinase. All these analyses provide a reference for the further development of novel Src inhibitors.

Systems pharmacology unravels the synergic target space and therapeutic potential of Rhodiola rosea L. for non-small cell lung cancer

BackgroundLung cancer is the most common and mortal cancer worldwide. Rhodiola rosea L. (RR), a well-known traditional Chinese medicine (TCM), has been turned out to be effective in anti-lung cancer therapy, but its molecular mechanism of action has not been clearly understood. PurposeIn this study, we aimed to elucidate the possible molecular mechanism underlying the effect of RR against non-small cell lung cancer (NSCLC) by systems pharmacology. MethodsThe effects of RR on NSCLC were examined in Lewis lung carcinoma (LLC) tumor-bearing mice models. The possible molecular mechanism was unraveled by systems pharmacology, which includes pharmacokinetics evaluation, active compounds screening, target prediction and network analysis. Cell proliferation was examined by cell counting kit-8 (CCK-8) assay; cell apoptosis was detected by flow cytometry; protein and proinflammatory cytokines expression were evaluated by Western blot and qRT-PCR. ResultsIn vivo, RR significantly inhibited the tumor growth and prolonged the survival of the tumor bearing mice. In silico, we identified 19 potential active molecules (e.g., salidroside and rhodiosin), 112 targets (e.g., COX-2 and AKT) and 27 pathways (e.g., PI3K/AKT signaling pathway and NF-κB signaling pathway) for RR. Additionally, targets analysis and networks construction further revealed that RR exerted anti-cancer effects by regulating apoptosis, angiogenesis and inflammation. In vitro, salidroside could significantly decrease expression of pro-angiogenic factors (e.g., VEGF and eNOS) and proinflammatory cytokines (e.g., COX-2, iNOS and TNF-α). Also, Bcl-2, an anti-apoptotic protein was decreased whereas Bax, a pro-apoptotic protein, was increased. Further flow cytometry analysis showed that salidroside could induce apoptosis in H1975 cells. ConclusionsMechanistically, the antitumor effect of RR on NSCLC was responsible for the synergy among anti-inflammatory, anti-angiogenic and pro-apoptotic.

Stepwise rapid tracking strategy to identify active molecules from Ixeris sonchifolia Hance based on “‘affinity mass spectrometry-atomic force microscopy imaging’” technology

In this study, a novel stepwise rapid tracking strategy was reported to identify the active molecules from Ixeris sonchifolia Hance (IsH) in the treatment of coronary heart disease (CHD) based on “affinity mass spectrometry (MS)-atomic force microscopy (AFM) imaging” technology. First, vascular endothelial growth factor receptor 2 (VEGFR2) of the vascular endothelial growth factor (VEGF) signal transduction pathway located on the cell membrane was revealed to be the core target protein in CHD treatment through network pharmacology and bioinformatics. In addition, affinity MS screening based on VEGFR2 identified isochlorogenic acid A and luteolin-7-O-glucuronide as having stronger affinity with VEGFR2. Then, the active molecule was elucidated based on the observation that its actions accompanied the molecular morphological changes by AFM imaging and it could act on the binding pocket of VEGFR2 through molecular docking which further demonstrated the analysis and inference of AFM imaging. The methyl thiazolyl tetrazolium (MTT) assay finally confirmed that the active molecules specifically combined with the potential core target protein to protect the viability of cardiomyocytes, which identified the main potential active molecules in IsH for the treatment of CHD and provided a possible mechanism for the protective role of the drug. The technology established in this study could facilitate the rapid tracing of potential active molecules in traditional Chinese medicine (TCM), which would provide further a reference for research on quality, molecular mechanisms and new drugs.

Identification of potential platelet-derived growth factor receptor α inhibitors by computational screening and binding simulations

Platelet-derived growth factor receptor α (PDGFRα) is considered as a promising target for the treatment of fibrotic diseases. In this study, two types of pharmacophore model, which generated by ligand-based and receptor-based method, were put forward to identify novel chemical entities as PDGFRα inhibitors. It was found that some pharmacophore characteristics established by the two approaches overlap each other. In order to elucidate detailed interactions, representative molecules were selected to predict the conformations and binding modes of the molecules by molecular docking method. The calculation results of binding free energy illustrated that the van der Waals energy and nonpolar solvation were the most prominent contribution to the interactions between the inhibitors and PDGFRα. To further verify the accuracy of the docking results and the stability of the complexes system, the binding modes of two potent PDGFRα inhibitors were examined by 100 ns molecular dynamics simulations. Herein, we reported the basic structural requirements of PDGFRα inhibitors for the first time through molecular docking and molecular dynamics simulations. Subsequently, the two pharmacophore models were used for virtual screening to query potential active molecules from Food and Drug Administration approved database. The hit molecules here might provide additional scaffolds for further optimization of PDGFRα inhibitors.

A multiple classifier system identifies novel cannabinoid CB2 receptor ligands

Drugs have become an essential part of our lives due to their ability to improve people’s health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have undesirable side effects, making the pharmaceutical industry strive to discover new drugs and active compounds. The development of drugs is an expensive process, which typically starts with the detection of candidate molecules (screening) after a protein target has been identified. To this end, the use of high-performance screening techniques has become a critical issue in order to palliate the high costs. Therefore, the popularity of computer-based screening (often called virtual screening or in silico screening) has rapidly increased during the last decade. A wide variety of Machine Learning (ML) techniques has been used in conjunction with chemical structure and physicochemical properties for screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently (iii) Multiple Classifier Systems (MCS). Here, we apply an MCS for virtual screening (D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine (1,834,362 compounds), was virtually screened to identify 48,232 potential active molecules using D2-MCS. Identified molecules were ranked to select 21 promising novel compounds for in vitro evaluation. Experimental validation confirmed six highly active hits (> 50% displacement at 10 µM and subsequent Ki determination) and an additional five medium active hits (> 25% displacement at 10 µM). Hence, D2-MCS provided a hit rate of 29% for highly active compounds and an overall hit rate of 52%.

Synthesis of benzopyran derivatives as PPARα and/or PPARγ activators

We describe the synthesis of 26 compounds, small polycerasoidol analogs, that are Lipinski’s rule-of-five compliant. In order to confirm key structural features to activate PPARα and/or PPARγ, we have adopted structural modifications in the following parts: (i) the benzopyran core (hydrophobic nucleus) by benzopyran-4-one, dihydrobenzopyran or benzopyran-4-ol; (ii) the side chain at 2-position by shortening to C3, C4 and C5-carbons versus C-9-carbons of polycerasoidol; (iii) the carboxylic group (polar head) by oxygenated groups (hydroxyl, acetoxy, epoxide, ester, aldehyde) or non-oxygenated motifs (allyl and alkyl). Benzopyran-4-ones 6, 12, 13 and 17 as well as dihydrobenzopyrans 22, 24 and 25 were able to activate hPPARα, whereas benzopyran-4-one (7) with C5-carbons in the side chain exhibited hPPARγ agonism. According to our previous docking studies, SAR confirm that the hydrophobic nucleus (benzopyran-4-one or dihydrobenzopyran) is essential to activate PPARα and/or PPARγ, and the flexible linker (side alkyl chain) should containg at least C5-carbon atoms to activate PPARγ. By contrast, the polar head (“carboxylic group”) tolerated several oxygenated groups but also non-oxygenated motifs. Taking into account these key structural features, small polycerasoidol analogs might provide potential active molecules useful in the treatment of dyslipidemia and/or type 2 diabetes.

Dissection of Pharmacological Mechanism of Chinese Herbal Medicine Yihuo Huatan Formula on Chronic Obstructive Pulmonary Disease: A Systems Pharmacology-Based Study

Chronic obstructive pulmonary disease (COPD) is one of the most common respiratory diseases. Yihuo Huatan Formula (YHF), as a proven Chinese Herbal Medicine (CHM), has been verified to be effective in the treatment of stable COPD through years’ of practice. Nevertheless, its working mechanism is still unclear. We sought to systematically decipher the mechanism of YHF for treating stable COPD using systems pharmacology-based method that integrates pharmacokinetic screening, target prediction, network analyses, GO and KEGG enrichment analyses. Firstly, a total of 1267 chemicals out of 15 herbal components were included in YHF chemical database. Among them, 180 potential active molecules were screened out through pharmacokinetic evaluation. Then 258 targets of the active molecules were predicted, of which 84 were chosen for further analyses. Finally, the network analyses and GO and KEGG enrichment methods suggested a therapeutic effect of YHF on the alleviation of airway inflammation, decrease of mucus secretion, maintenance of immune homeostasis and benefit of COPD comorbidities, by regulating multiple targets and pathways. The systems pharmacology-based approach helps to understand the underlying working mechanism of YHF in stable COPD from a holistic perspective, and offers an exemplification for systematically uncovering the action mechanisms of CHM.

Study on Mechanism of the Reduning Injection on the Influenza Virus Using Network Pharmacology Method

The mechanisms of the Reduning injection on the influenza virus were studied by computer methods at the molecular level, including docking and network analysis. A neuraminidase (NA) activity assay was used to verify the predicted results in vitro. Results show that most of the compounds in the Reduning injection exhibit good drug-like properties. The mechanism of the Reduning injection may be that it affects viral entry, RNA synthesis, or viral release, which limits influenza virus replication. Potential active molecules of the Reduning injection were found by analyzing the network parameters of compound-target interaction networks. The experimental results suggest that of the potential active molecules, luteolin displayed significant inhibitory activity toward the influenza A virus, while quercetin proved less effective, which supports the predicted results.

Bioassays and field immersion tests: a comparison of the antifouling activity of copper-free poly(methacrylic)-based coatings containing tertiary amines and ammonium salt groups

This paper focuses on the activity spectrum of three dimethylalkyl tertiary amines as potential active molecules and the corresponding ammonium salt-based antifouling (AF) paints. Bioassays (using marine bacteria, microalgae and barnacles) and field tests were combined to assess the AF activity of coatings. Bioassay results demonstrated that the ammonium salt-based paints did not inhibit the growth of microorganisms (except the dimethyldodecylammonium-based coatings) and that the tertiary amines were potent towards bacteria, diatoms, and barnacle larvae at non-toxic concentrations (therapeutic ratio, LC50/EC50, < 1). The results from field tests indicated that the ammonium salt-based coatings inhibited the settlement of macrofouling and the dimethylhexadecylammonium-based coatings provided protection against slime in comparison with PVC blank panels. Thus, results from laboratory assays did not fully concur with the AF activity of the paints in the field trial.

Computational Pharmacology of Jingzhi Tougu Xiaotong Granule in Preventing and Treating Osteoarthritis

The pharmacological mechanism of 514 compounds contained in a Chinese medicine,Jingzhi Tougu Xiaotong Granule (JZTGXTG) was studied by computational pharmacological methods including analysis of molecular similarity,chemical space,molecular docking,network technology,and the predictions of absorption,distribution,metabolism,excretion and toxicity (ADME/T).Results show that compounds in JZTGXTG have the diverse structural properties and most of these compounds have good drug-like properties in their chemical spaces.The possible action mechanism is illuminated and potential active-molecules of JZTGXTG are found by studying the interactions between 514 compounds of JZTGXTG and 35 drug targets related to osteoarthritis disease and the distribution of 514 compounds in drug-target space.By analyzing network parameters of JZTGXTG compound-target interaction network and drug-target interaction network including network heterogeneity and characteristic path length,the results reveal the molecular mechanism of multi-drugs,multi-targets,and multi-pathways in JZTGXTG.The results are helpful for understanding the complicated mechanism of JZTGXTG.