The application of in silico drug-likeness predictions in pharmaceutical research

Computational approaches for evaluation of isobavachin as potential inhibitor against t877a and w741l mutations in prostate cancer

Prostate cancer is the World's second most common cancer, with the fifth-highest male mortality rate. Point mutations such as T877A and W741L are frequently seen in advanced prostate cancer patients, conferring drug-resistance and hence driving cancer growth. Such occurrence of drug resistance in prostate cancer necessitates designing of suitable ligands to ensure better interactions with the receptors which can block the progression of the disease. The present study focus on the modification of plant-derived flavonoids that might act as inhibitors against such point mutations namely, T877A and W741L. In T877A mutation threonine is substituted by alanine at the 877 codon and W741L mutation, tryptophan is substituted by lysine at the 741 codon in prostate cancer. The study revolved on the aspect of the evaluation of Isobavachin and its derivatives as a potential agent to tackle such point mutations by using the in silico approach. A total of 98 molecular dockings were performed to find the ligand-receptor complexes with the lowest binding energy employing Autodock Software to conduct the blind and site-specific docking. Additionally, ligands were screened for Drug-likeness and toxicity using several tools yielding eight possible drug candidates. Based on the results of Molecular Docking, Drug-likeness, and ADMET testing, ten structures, including six complexes and three receptors were subjected to molecular dynamics simulation of 100 ns covering RMSD, RMSF, Rg, and MM/PBSA. Based on the simulation results, Isobavachin, IsoMod4, and IsoMod7 were concluded to be stable and exhibited potential properties for developing a novel drug to combat prostate cancer and its associated drug-resistance.

Molecular Docking, Synthesis, and Tyrosinase Inhibition Activity of Acetophenone Amide: Potential Inhibitor of Melanogenesis

Tyrosinase and its related proteins are responsible for pigmentation disorders, and inhibiting tyrosinase is an established strategy to treat hyperpigmentation. The carbonyl scaffolds can be effective inhibitors of tyrosinase activity, and the fact that both benzoic and cinnamic acids are safe natural substances with such a scaffolded structure, it was speculated that hydroxyl-substituted benzoic and cinnamic acid derivatives may exhibit potent tyrosinase inhibitory activity. These moieties were incorporated into new chemotypes that displayed in vitro inhibitory effect against mushroom tyrosinase with a view to explore antimelanogenic ingredients. The most active compound, 2-((3-acetylphenyl)amino)-2-oxoethyl(E)-3-(2,4-dihydroxyphenyl)acrylate (5c), inhibited mushroom tyrosinase with an IC50 of $0.0020\pm 0.0002\mu \text{M}$ , while 2-((3-acetylphenyl)amino)-2-oxoethyl 2,4-dihydroxybenzoate (3c) had an IC50 of $27.35\pm 3.6\mu \text{M}$ in comparison to the positive control arbutin and kojic acid with a tyrosinase inhibitory activity of IC50 of $191.17\pm 5.5\mu \text{M}$ and IC50 of $16.69\pm 2.8\mu \text{M}$ , respectively. Analysis of enzyme kinetics revealed that 5c is a competitive and reversible inhibitor with dissociation constant (Ki) value 0.0072 μM. In silico docking studies with mushroom tyrosinase (PDB ID 2Y9X) predicted possible binding modes in the enzymatic pocket for these compounds. The orthohydroxyl of the cinnamic acid moiety of 5c is predicted to form hydrogen bond with the active site side chain carbonyl of Asn 260 (2.16 Å) closer to the catalytic site Cu ions. The acetyl carbonyl is picking up another hydrogen bond with Asn 81 (1.90 Å). The inhibitor 5c passed the panassay interference (PAINS) alerts. This study presents the potential of hydroxyl-substituted benzoic and cinnamic acids and could be beneficial for various cosmetic formulations.

Discovery and computational studies of 2-phenyl-benzoxazole acetamide derivatives as promising P2Y14R antagonists with anti-gout potential

The P2Y₁₄ nucleotide receptor, a subtype of P2Y receptors, is implicated in many human inflammatory diseases. Based on the identification of favorable residues of two screening hits in the almost symmetrical P2Y₁₄ binding domain, we describe the structural optimization of previously identified virtual screening hits 6 and 7 that result in the development of P2Y₁₄R antagonists with a novel 2-phenyl-benzoxazole acetamide chemical scaffold. Notably, compound 52 showed potent P2Y₁₄R antagonistic activity (IC₅₀ = 2 nM), and a stronger inhibitory effect on MSU-induced inflammatory in vitro, better than a previously described P2Y₁₄R antagonist PPTN. In vivo evaluation demonstrated that compound 52 also had satisfactory inhibitory activity on the inflammatory response of gout flares in mice. Moreover, P2Y₁₄R antagonist 52 decreased paw swelling and inflammatory cell infiltration through cAMP/NLRP3/GSDMD signaling pathways in MSU-induced acute gouty arthritis mice. The discussions on the binding mechanism that employ MM/GBSA free energy calculations/decompositions also provide some useful clues for further structural designing of compound 52. Taken together, 2-phenyl-benzoxazole acetamide derivative 52 with potent P2Y₁₄R antagonistic activity and in vivo potency could be a promising strategy for gout therapy and deserves further optimization.

Drug-likeness scoring based on unsupervised learning

Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learning model provides drug-likeness scores that well separate distributions with increasing mean values in the order of datasets composed of molecules at a later step in a drug development process, whereas the classification model predicted a polarized distribution with two extreme values for all datasets presumably due to the overconfident prediction for unseen data. Thus, this new concept offers a pragmatic tool for drug-likeness scoring and further can be applied to other biochemical applications.

Interpreting the Physicochemical Meaning of a Molecular Descriptor Which Is Predictive of Amorphous Solid Dispersion Formation in Polyvinylpyrrolidone Vinyl Acetate

A molecular descriptor known as R3m (the R-GETAWAY third-order autocorrelation index weighted by the atomic mass) was previously identified as capable of grouping members of an 18-compound library of organic molecules that successfully formed amorphous solid dispersions (ASDs) when co-solidified with the co-polymer polyvinylpyrrolidone vinyl acetate (PVPva) at two concentrations using two preparation methods. To clarify the physical meaning of this descriptor, the R3m calculation is examined in the context of the physicochemical mechanisms of dispersion formation. The R3m equation explicitly captures information about molecular topology, atomic leverage, and molecular geometry, features which might be expected to affect the formation of stabilizing non-covalent interactions with a carrier polymer, as well as the molecular mobility of the active pharmaceutical ingredient (API) molecule. Molecules with larger R3m values tend to have more atoms, especially the heavier ones that form stronger non-covalent interactions, generally, more irregular shapes, and more complicated topology. Accordingly, these molecules are more likely to remain dispersed within PVPva. Furthermore, multiple linear regression modeling of R3m and more interpretable descriptors supported these conclusions. Finally, the utility of the R3m descriptor for predicting the formation of ASDs in PVPva was tested by analyzing the commercially available products that contain amorphous APIs dispersed in the same polymer. All of these analyses support the conclusion that the information about the API geometry, size, shape, and topological connectivity captured by R3m relates to the ability of a molecule to interact with and remain dispersed within an amorphous PVPva matrix.

Computational Docking Study of Calanolides as Potential Inhibitors of SARS-CoV-2 Main Protease

Despite the nationwide effort provided to combat the COVID-19 pandemic, we have yet to approve a specific antiviral treatment against the SARS-CoV-2. We have studied the molecular interactions between two anti-HIV-1 natural drugs, +(-) calanolide A and -(-) calanolide B, and the active site of 3CLpro through a computational docking method. Our promising results show that the two compounds of this study are potential inhibitors of the SARS-CoV-2 3CLpro through strong binding to its catalytic dyad. Considering its progress in clinical trials as an anti-HIV-1 treatment, we suggest that +(-) calanolide A is a good candidate for the treatment of COVID-19.

Not Drug-like, but Like Drugs: Cnidaria Natural Products

Phylum Cnidaria has been an excellent source of natural products, with thousands of metabolites identified. Many of these have not been screened in bioassays. The aim of this study was to explore the potential of 5600 Cnidaria natural products (after excluding those known to derive from microbial symbionts), using a systematic approach based on chemical space, drug-likeness, predicted toxicity, and virtual screens. Previous drug-likeness measures: the rule-of-five, quantitative estimate of drug-likeness (QED), and relative drug likelihoods (RDL) are based on a relatively small number of molecular properties. We augmented this approach using reference drug and toxin data sets defined for 51 predicted molecular properties. Cnidaria natural products overlap with drugs and toxins in this chemical space, although a multivariate test suggests that there are some differences between the groups. In terms of the established drug-likeness measures, Cnidaria natural products have generally lower QED and RDL scores than drugs, with a higher prevalence of metabolites that exceed at least one rule-of-five threshold. An index of drug-likeness that includes predicted toxicity (ADMET-score), however, found that Cnidaria natural products were more favourable than drugs. A measure of the distance of individual Cnidaria natural products to the centre of the drug distribution in multivariate chemical space was related to RDL, ADMET-score, and the number of rule-of-five exceptions. This multivariate similarity measure was negatively correlated with the QED score for the same metabolite, suggesting that the different approaches capture different aspects of the drug-likeness of individual metabolites. The contrasting of different drug similarity measures can help summarise the range of drug potential in the Cnidaria natural product data set. The most favourable metabolites were around 210-265 Da, quite often sesquiterpenes, with a moderate degree of complexity. Virtual screening against cancer-relevant targets found wide evidence of affinities, with Glide scores <-7 in 19% of the Cnidaria natural products.

In Vitro Evaluation of the Anti-Diabetic Potential of Aqueous Acetone Helichrysum petiolare Extract (AAHPE) with Molecular Docking Relevance in Diabetes Mellitus

Diabetes mellitus (DM) is a chronic metabolic condition that can lead to significant complications and a high fatality rate worldwide. Efforts are ramping up to find and develop novel α-glucosidase and α-amylase inhibitors that are both effective and potentially safe. Traditional methodologies are being replaced with new techniques that are less complicated and less time demanding; yet, both the experimental and computational strategies are viable and complementary in drug discovery and development. As a result, this study was conducted to investigate the in vitro anti-diabetic potential of aqueous acetone Helichrysum petiolare and B.L Burtt extract (AAHPE) using a 2-NBDG, 2-(N-(7-Nitrobenz-2-oxa-1,3-diazol-4-yl) amino)-2-deoxy-d-glucose uptake assay. In addition, we performed molecular docking of the flavonoid constituents identified and quantified by liquid chromatography-mass spectrometry (LC-MS) from AAHPE with the potential to serve as effective and safe α-amylase and α-glucosidase inhibitors, which are important in drug discovery and development. The results showed that AAHPE is a potential inhibitor of both α-amylase and α-glucosidase, with IC₅₀ values of 46.50 ± 6.17 (µg/mL) and 37.81 ± 5.15 (µg/mL), respectively. This is demonstrated by a significant increase in the glucose uptake activity percentage in a concentration-dependent manner compared to the control, with the highest AAHPE concentration of 75 µg/mL of glucose uptake activity being higher than metformin, a standard anti-diabetic drug, in the insulin-resistant HepG2 cell line. The molecular docking results displayed that the constituents strongly bind α-amylase and α-glucosidase while achieving better binding affinities that ranged from ΔG = -7.2 to -9.6 kcal/mol (compared with acarbose ΔG = -6.1 kcal/mol) for α-amylase, and ΔG = -7.3 to -9.0 kcal/mol (compared with acarbose ΔG = -6.3 kcal/mol) for α-glucosidase. This study revealed the potential use of the H. petiolare plant extract and its phytochemicals, which could be explored to develop potent and safe α-amylase and α-glucosidase inhibitors to treat postprandial glycemic levels in diabetic patients.

Curcumin Analogues as a Potential Drug against Antibiotic Resistant Protein, β-Lactamases and L, D-Transpeptidases Involved in Toxin Secretion in Salmonella typhi: A Computational Approach

Typhoid fever caused by the bacteria Salmonella typhi gained resistance through multidrug-resistant S. typhi strains. One of the reasons behind β-lactam antibiotic resistance is -lactamase. L, D-Transpeptidases is responsible for typhoid fever as it is involved in toxin release that results in typhoid fever in humans. A molecular modeling study of these targeted proteins was carried out by various methods, such as homology modeling, active site prediction, prediction of disease-causing regions, and by analyzing the potential inhibitory activities of curcumin analogs by targeting these proteins to overcome the antibiotic resistance. The five potent drug candidate compounds were identified to be natural ligands that can inhibit those enzymes compared to controls in our research. The binding affinity of both the Go-Y032 and NSC-43319 were found against β-lactamase was −7.8 Kcal/mol in AutoDock, whereas, in SwissDock, the binding energy was −8.15 and −8.04 Kcal/mol, respectively. On the other hand, the Cyclovalone and NSC-43319 had an equal energy of −7.60 Kcal/mol in AutoDock, whereas −7.90 and −8.01 Kcal/mol in SwissDock against L, D-Transpeptidases. After the identification of proteins, the determination of primary and secondary structures, as well as the gene producing area and homology modeling, was accomplished. The screened drug candidates were further evaluated in ADMET, and pharmacological properties along with positive drug-likeness properties were observed for these ligand molecules. However, further in vitro and in vivo experiments are required to validate these in silico data to develop novel therapeutics against antibiotic resistance.

Molybdenum Carbonyl Complexes with Benzimidazole Derivatives Against SARS-CoV-2 by Molecular Docking and DFT/TDDFT Methods

Benzimidazole derivative molecules attract attention of scientists due to their bioactivities. The dramatic changes in recorded activities according to the type and position of the substituents motivate synthesis and analysis of new molecules. Commercial benzimidazole-based molecules have been used in therapeutic procedures. It is known that the activities of metal complexes with benzimidazole derivative ligands have different activities when compared to the benzimidazole main structure. Nowadays, one of the most important health problems is COVID-19, which caused the pandemic that we are still experiencing. Although vaccine studies are important to overcome acute problems, regarding the possible post-vaccination adverse effects, the need for new drugs against the virus is obvious. Considering the urgency and the limited facilities during the pandemic, preliminary in silico studies of candidate molecules are essential. In this study, {[bis-(N-benzylbenzimidazole)] tetracarbonylmolybdenum}, {[bis-(N-4-chlorobenzylbenzimidazole)] tetracarbonylmolybdenum} and {[bis-(N-4-methoxybenzylbenzimidazole)] tetracarbonylmolybdenum} were synthesized and characterized. The optimization and the structural analysis of these molecules were performed by DFT/TDDFT methods. The molecules were docked into SARS coronavirus main peptidase (PDB ID: 2gtb), COVID-19 main protease in complex with Z219104216 (PDB ID: 5r82), COVID-19 main protease in complex with an inhibitor N3 (PDB ID: 6lu7) and Papain-like protease of SARS-CoV-2 (PDB ID: 6w9c) crystal structures for evaluation of their anti-viral activity.

Elucidating the Mechanism of Action of Salvia miltiorrhiza for the Treatment of Acute Pancreatitis Based on Network Pharmacology and Molecular Docking Technology

Using network pharmacology and molecular docking, this study investigated the molecular mechanisms by which the active components in Salvia miltiorrhiza can alleviate acute pancreatitis. Initially, the active components of Salvia miltiorrhiza and the targets collected from the GeneCards database were screened based on the platform of systematic pharmacology analysis of traditional Chinese medicine. Subsequently, the active components were intersected with the disease targets. Also, interactions among the targets were computed using the STRING database. Biological function and pathway enrichment were analyzed using the Cluster Profiler package in the R software. Protein-protein interaction and component target pathway network were constructed using the Cytoscape software. Ultimately, the key targets and their corresponding components in the network were verified using the AutoDock Vina software. The results showed Salvia miltiorrhiza had 111 targets for acute pancreatitis. The biological process (BP) analysis showed that the active components of Salvia miltiorrhiza induced a drug response, positive regulation of transcription by RNA polymerase II promoter, signal transduction, positive regulation of cell proliferation, and negative regulation of apoptosis. Furthermore, the KEGG enrichment analysis screened 118 (P < 0.05) signaling pathways, such as the pathways related to cancer, neuroactive ligand-receptor interaction, PI3K-Akt signaling pathway, and cAMP signaling pathway, to name a few. Finally, molecular docking showed that the active components of Salvia miltiorrhiza had a good binding affinity with their corresponding target proteins. Through network pharmacology, this study predicted the potential pharmacodynamic material basis and the mechanisms by which Salvia miltiorrhiza can treat acute pancreatitis. Moreover, this study provided a scientific basis for mining the pharmacodynamic components of Salvia miltiorrhiza and expanding the scope of its clinical use.

Natural-product-inspired design and synthesis of two series of compounds active against Trypanosoma cruzi: Insights into structure-activity relationship, toxicity, and mechanism of action

Chemical scaffolds of natural products have historically been sources of inspiration for the development of novel molecules of biological relevance, including hit and lead compounds. To identify new compounds active against Trypanosoma cruzi, we designed and synthesized 46 synthetic derivatives based on the structure of two classes of natural products: tetrahydrofuran lignans (Series 1) and oxazole alkaloids (Series 2). Compounds were screened in vitro using a cellular model of T. cruzi infection. In the first series of compounds, 11 derivatives of hit compound 5 (EC₅₀ = 1.1 µM) were found to be active; the most potent (7, 8, and 13) had EC₅₀ values of 5.1-34.2 µM. In the second series, 17 analogs were found active at 50 µM; the most potent compounds (47, 49, 59, and 63) showed EC₅₀ values of 24.2-49.1 µM. Active compounds were assessed for selectivity, hemocompatibility, synergistic potential, effects on mitochondrial membrane potential, and inhibitory effect on trypanothione reductase. All active compounds showed low toxicity against uninfected THP-1 cells and human erythrocytes. The potency of compounds 5 and 8 increased steadily in combination with benznidazole, indicating a synergistic effect. Furthermore, compounds 8, 47, 49, 59, and 63 inhibited parasitic mitochondria in a dose-dependent manner. Although increased reactive oxygen species levels might lead to mitochondrial effects, the results indicate that the mechanism of action of the compounds is not dependent on trypanothione reductase inhibition. In silico calculation of chemical descriptors and principal component analysis showed that the active compounds share common chemical features with other trypanocidal molecules and are predicted to have a good ADMET profile. Overall, the results suggest that the compounds are important candidates to be further studied for their potential against T. cruzi.

The rise of deep learning and transformations in bioactivity prediction power of molecular modeling tools

The search and design for the better use of bioactive compounds are used in many experiments to best mimic compounds' functions in the human body. However, finding a cost-effective and timesaving approach is a top priority in different disciplines. Nowadays, artificial intelligence (AI) and particularly deep learning (DL) methods are widely applied to improve the precision and accuracy of models used in the drug discovery process. DL approaches have been used to provide more opportunities for a faster, efficient, cost-effective, and reliable computer-aided drug discovery. Moreover, the increasing biomedical data volume in areas, like genome sequences, medical images, protein structures, etc., has made data mining algorithms very important in finding novel compounds that could be drugs, uncovering or repurposing drugs and improving the area of genetic markers-based personalized medicine. Furthermore, deep neural networks (DNNs) have been demonstrated to outperform other techniques such as random forests and SVMs for QSAR studies and ligand-based virtual screening. Despite this, in QSAR studies, the quality of different data sources and potential experimental errors has greatly affected the accuracy of QSAR predictions. Therefore, further researches are still needed to improve the accuracy, selectivity, and sensitivity of the DL approach in building the best models of drug discovery.

Network Pharmacology and Molecular Docking-Based Prediction of the Mechanism of Qianghuo Shengshi Decoction against Rheumatoid Arthritis

Qianghuo Shengshi decoction (QHSSD) is a classical Chinese medicine formula, which is used in clinical practice for the treatment of rheumatoid arthritis (RA) in China. However, the pharmacological mechanism of QHSSD on RA has remained unclear by now. We collected and screened active compounds and its potential targets by the pharmacology platform of Chinese herbal medicines. In addition, the therapeutic targets of RA were obtained and selected from databases. Network construction analyzed that 128 active compounds may act on 87 candidate targets and identified a total of 18 hub targets. GO annotation and KEGG enrichment investigated that the action mechanism underlying the treatment of RA by QHSSD might be involved in cell proliferation, angiogenesis, anti-inflammation, and antioxidation. Finally, molecular docking verification showed that TP53, VEGFA, TNF, EGFR, and NOS3 may be related to the RA treatment and molecular dynamics simulation showed the stability of protein-ligand interactions. In this work, QHSSD might exert therapeutic effect through a multicomponent, multitarget, and multipathway in RA from a holistic aspect, which provides basis for its mechanism of action and subsequent experiments.

Data Mining and Systems Pharmacology to Elucidate Effectiveness and Mechanisms of Chinese Medicine in Treating Primary Liver Cancer

OBJECTIVE

To identify specific Chinese medicines (CM) that may benefit patients with primary liver cancer (PLC), and to explore the mechanism of action of these medicines.

METHODS

In this retrospective, singlecenter study, prescription information from PLC patients was used in combination with Traditional Chinese Medicine Inheritance Supports System to identify the specific core drugs. A system pharmacology approach was employed to explore the mechanism of action of these medicines.

RESULTS

Taking CM more than 6 months was significantly associated with improved survival outcomes. In total, 77 putative targets and 116 bioactive ingredients of the core drugs were identified and included in the analysis (P<0.05). A total of 1,036 gene ontology terms were found to be enriched in PLC. A total of 75 pathways identified from Kyoto Encyclopedia of Genes and Genomes were also enriched in this disease, including fluid shear stress, interleukin-17 signaling, signaling between advanced glycan end products and their receptors, cellular senescence, tumor necrosis factor signaling, p53 signaling, cell cycle signaling, steroid hormone biosynthesis, T-helper 17 cell differentiation, and metabolism of xenobiotics by cytochrome. Docking studies suggested that the ingredients in the core drugs exert therapeutic effects in PLC by modulating c-Jun and interleukin-6.

CONCLUSIONS

Receiving CM for 6 months or more improves survival for the patients with PLC. The core drugs that really benefit for PLC patients likely regulates the tumor microenvironment and tumor itself.

Identification of Potential Bioactive Ingredients and Mechanisms of the Guanxin Suhe Pill on Angina Pectoris by Integrating Network Pharmacology and Molecular Docking

The Guanxin Suhe pill (GSP), a traditional Chinese medicine, has been widely used to treat angina pectoris (AP) in Chinese clinical practice. However, research on the bioactive ingredients and underlying mechanisms of GSP in AP remains scarce. In this study, a system pharmacology approach integrating gastrointestinal absorption (GA) evaluation, drug-likeness (DL) evaluation, target exploration, protein-protein-interaction analysis, Gene Ontology (GO) enrichment analysis, network construction, and molecular docking was adopted to explore its potential mechanisms. A total of 481 ingredients from five herbs were collected, and 242 were qualified based on GA and DL evaluation. Target exploration identified 107 shared targets between GSP and AP. Protein-protein interaction identified VEGFA (vascular endothelial growth factor A), TNF (tumor necrosis factor), CCL2 (C-C motif chemokine ligand 2), FN1 (fibronectin 1), MMP9 (matrix metallopeptidase 9), PTGS2 (prostaglandin-endoperoxide synthase 2), IL10 (interleukin 10), CXCL8 (C-X-C motif chemokine ligand 8), IL6 (interleukin 6), and INS (insulin) as hub targets for GSP, which were involved in the inflammatory process, ECM proteolysis, glucose metabolism, and lipid metabolism. GO enrichment identified top pathways in the biological processes, molecular functions, and cell components, explaining GSP's potential AP treatment mechanism. Positive regulation of the nitric oxide biosynthetic process and the response to hypoxia ranked highest of the biological processes; core targets that GSP can regulate in these two pathways were PTGS2 and NOS2, respectively. Molecular docking verified the interactions between the core genes in the pathway and the active ingredients. The study lays a foundation for further experimental research and clinical application.

Exploring the Mechanism of Action of Herbal Medicine (Gan-Mai-Da-Zao Decoction) for Poststroke Depression Based on Network Pharmacology and Molecular Docking

BACKGROUND

Poststroke depression (PSD) is the most common and serious neuropsychiatric complication occurring after cerebrovascular accidents, seriously endangering human health while also imposing a heavy burden on society. Nevertheless, it is difficult to control disease progression. Gan-Mai-Da-Zao Decoction (GMDZD) is effective for PSD, but its mechanism of action in PSD is unknown. In this study, we explored the mechanism of action of GMDZD in PSD treatment using network pharmacology and molecular docking. Material and methods. We obtained the active components of all drugs and their targets from the public database TCMSP and published articles. Then, we collected PSD-related targets from the GeneCards and OMIM databases. Cytoscape 3.8.2 was applied to construct PPI and composite target disease networks. In parallel, the DAVID database was used to perform GO and KEGG enrichment analyses to determine the biological processes enriched in the treatment-related drugs in vivo. Finally, molecular docking was used to verify the association between the main active ingredients and their targets.

RESULTS

The network pharmacological analysis of GMDZD in PSD revealed 107 active ingredients with important biological effects, including quercetin, luteolin, kaempferol, naringenin, and isorhamnetin. In total, 203 potential targets for the treatment of this disease were screened, including STAT3, JUN, TNF, TPT53, AKT1, and EGFR. These drugs are widely enriched in a series of signaling pathways, such as TNF, HIF-1, and toll-like receptor. Moreover, molecular docking analysis showed that the core active components were tightly bound to their core targets, further confirming their anti-PSD effects.

CONCLUSION

This prospective study was based on the integrated analysis of large data using network pharmacology technology to explore the feasibility of GMDZD for PSD treatment that was successfully validated by molecular docking. It reflects the multicomponent and multitarget characteristics of Chinese medicine and, more importantly, brings hope for the clinical treatment of PSD.

A review on machine learning approaches and trends in drug discovery

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Network Pharmacology Combined with Bioinformatics to Investigate the Mechanisms and Molecular Targets of Astragalus Radix-Panax notoginseng Herb Pair on Treating Diabetic Nephropathy

Background

Astragalus Radix (AR)-Panax notoginseng (PN), a classical herb pair, has shown significant effects in treating diabetic nephropathy (DN). However, the intrinsic mechanism of AR-PN treating DN is still unclear. This study aims to illustrate the mechanism and molecular targets of AR-PN treating DN based on network pharmacology combined with bioinformatics.

Materials and Methods

The Traditional Chinese Medicine Systems Pharmacology database was used to screen bioactive ingredients of AR-PN. Subsequently, putative targets of bioactive ingredients were predicted utilizing the DrugBank database and converted into genes on UniProtKB database. DN-related targets were retrieved via analyzing published microarray data (GSE30528) from the Gene Expression Omnibus database. Protein-protein interaction networks of AR-PN putative targets and DN-related targets were established to identify candidate targets using Cytoscape 3.8.0. GO and KEGG enrichment analyses of candidate targets were reflected using a plugin ClueGO of Cytoscape. Molecular docking was performed using AutoDock Vina software, and the results were visualized by Pymol software. The diagnostic capacity of hub genes was verified by receiver operating characteristic (ROC) curves.

Results

Twenty-two bioactive ingredients and 189 putative targets of AR-PN were obtained. Eight hundred and fifty differently expressed genes related to DN were screened. The PPI network showed that 115 candidate targets of AR-PN against DN were identified. GO and KEGG analyses revealed that candidate targets of AR-PN against DN were mainly involved in the apoptosis, oxidative stress, cell cycle, and inflammation response, regulating the PI3K-Akt signaling pathway, cell cycle, and MAPK signaling pathway. Moreover, MAPK1, AKT1, GSK3B, CDKN1A, TP53, RELA, MYC, GRB2, JUN, and EGFR were considered as the core potential therapeutic targets. Molecular docking demonstrated that these core targets had a great binding affinity with quercetin, kaempferol, isorhamnetin, and formononetin components. ROC curve analysis showed that AKT1, TP53, RELA, JUN, CDKN1A, and EGFR are effective in discriminating DN from controls.

Conclusions

AR-PN against DN may exert its renoprotective effects via various bioactive chemicals and the related pharmacological pathways, involving multiple molecular targets, which may be a promising herb pair treating DN. Nevertheless, these results should be further validated by experimental evidence.

Synthesis, spectral characterization, and biological studies of 3,5-disubstituted-1,3,4-oxadiazole-2(3H)-thione derivatives

The reaction of 3,4-dichlorophenyl-1,3,4-oxadiazole-2( 3H )-thione with piperidine derivatives via Mannich reaction was used to generate eleven novel compounds in moderate to good yields. Synthesized molecules were characterized according to their structure with ¹H NMR, ¹³C NMR and FT-IR spectral foundations, which were compatible with literature informations. Antimicrobial activity and cytotoxicity studies were done by disc diffusion and NCI-60 sulphordamine B assay methods. The antimicrobial test results revealed that synthesized compounds have better activity against gram-positive species than gram-negative ones. A total analysis of the antibacterial, antifungal, and antiyeast activity revealed that newly synthesized compounds were really active against Bacillus cereus , Bacillus ehimensis, and Bacillus thuringiensis species . For cytotoxicity, among three different cancer cell lines (HCT116, MCF7, HUH7) compounds 5c, 5d, 5e, 5f, 5g, 5i, 5j and 5k were seemed especially effective on HUH7 cancer cell line via moderate to good activity. More significantly, against liver carcinoma cell line (HUH7) most of the compounds of the series ( 5c-5g and 5i-5j ) have better IC₅₀ values (IC₅₀= 18.78 µM) than 5-Florouracil (5-FU) and also compound 5d possessed 10.1 µM value, which represents good druggable cytotoxic activity. Further, the molecules were also screened for in silico chemoinformatic and toxicity data to gather the predicted bioavailibity and safety measurements.

Cheminformatic Characterization of Natural Antimicrobial Products for the Development of New Lead Compounds

The growing antimicrobial resistance (AMR) of pathogenic organisms to currently prescribed drugs has resulted in the failure to treat various infections caused by these superbugs. Therefore, to keep pace with the increasing drug resistance, there is a pressing need for novel antimicrobial agents, especially from non-conventional sources. Several natural products (NPs) have been shown to display promising in vitro activities against multidrug-resistant pathogens. Still, only a few of these compounds have been studied as prospective drug candidates. This may be due to the expensive and time-consuming process of conducting important studies on these compounds. The present review focuses on applying cheminformatics strategies to characterize, prioritize, and optimize NPs to develop new lead compounds against antimicrobial resistance pathogens. Moreover, case studies where these strategies have been used to identify potential drug candidates, including a few selected open-access tools commonly used for these studies, are briefly outlined.

A chemoinformatic analysis of atoms, scaffolds and functional groups in natural products

In the quest to know why natural products (NPs) have often been considered as privileged scaffolds for drug discovery purposes, many investigations into the differences between NPs and synthetic compounds have been carried out. Several attempts to answer this question have led to the investigation of the atomic composition, scaffolds and functional groups (FGs) of NPs, in comparison with synthetic drugs analysis. This chapter briefly describes an atomic enumeration method for chemical libraries that has been applied for the analysis of NP libraries, followed by a description of the main differences between NPs of marine and terrestrial origin in terms of their general physicochemical properties, most common scaffolds and “drug-likeness” properties. The last parts of the work describe an analysis of scaffolds and FGs common in NP libraries, focusing on huge NP databases, e.g. those in the Dictionary of Natural Products (DNP), NPs from cyanobacteria and the largest chemical class of NP – terpenoids.

A Biophysical Insight of Camptothecin Biodistribution: Towards a Molecular Understanding of Its Pharmacokinetic Issues

Camptothecin (CPT) is a potent anticancer drug, and its putative oral administration is envisioned although difficult due to physiological barriers that must be overcome. A comprehensive biophysical analysis of CPT interaction with biointerface models can be used to predict some pharmacokinetic issues after oral administration of this or other drugs. To that end, different models were used to mimic the phospholipid composition of normal, cancer, and blood–brain barrier endothelial cell membranes. The logD values obtained indicate that the drug is well distributed across membranes. CPT-membrane interaction studies also confirm the drug’s location at the membrane cooperative and interfacial regions. The drug can also permeate membranes at more ordered phases by altering phospholipid packing. The similar logD values obtained in membrane models mimicking cancer or normal cells imply that CPT has limited selectivity to its target. Furthermore, CPT binds strongly to serum albumin, leaving only 8.05% of free drug available to be distributed to the tissues. The strong interaction with plasma proteins, allied to the large distribution (VD_SS = 5.75 ± 0.932 L·Kg⁻¹) and tendency to bioaccumulate in off-target tissues, were predicted to be pharmacokinetic issues of CPT, implying the need to develop drug delivery systems to improve its biodistribution.

In Silico and In Vivo Studies on the Mechanisms of Chinese Medicine Formula (Gegen Qinlian Decoction) in the Treatment of Ulcerative Colitis

Ulcerative colitis (UC) is a chronic inflammatory bowel disease, and Gegen Qinlian Decoction (GQD), a Chinese botanical formula, has exhibited beneficial efficacy against UC. However, the mechanisms underlying the effect of GQD still remain to be elucidated. In this study, network pharmacology approach and molecular docking in silico were applied to uncover the potential multicomponent synergetic effect and molecular mechanisms. The targets of ingredients in GQD were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM) database, while the UC targets were retrieved from Genecards, therapeutic target database (TTD) and Online Mendelian Inheritance in Man (OMIM) database. The topological parameters of Protein-Protein Interaction (PPI) data were used to screen the hub targets in the network. The possible mechanisms were investigated with gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecular docking was used to verify the binding affinity between the active compounds and hub targets. Network pharmacology analysis successfully identified 77 candidate compounds and 56 potential targets. The targets were further mapped to 20 related pathways to construct a compound-target-pathway network and an integrated network of GQD treating UC. Among these pathways, PI3K-AKT, HIF-1, VEGF, Ras, and TNF signaling pathways may exert important effects in the treatment of UC via inflammation suppression and anti-carcinogenesis. In the animal experiment, treatment with GQD and sulfasalazine (SASP) both ameliorated inflammation in UC. The proinflammatory cytokines (TNF-α, IL-1β, and IL-6) induced by UC were significantly decreased by GQD and SASP. Moreover, the protein expression of EGFR, PI3K, and phosphorylation of AKT were reduced after GQD and SASP treatment, and there was no significance between the GQD group and SASP group. Our study systematically dissected the molecular mechanisms of GQD on the treatment of UC using network pharmacology, as well as uncovered the therapeutic effects of GQD against UC through ameliorating inflammation via downregulating EGFR/PI3K/AKT signaling pathway and the pro-inflammatory cytokines such as TNF-α, IL-1β and IL-6.

Natural Chain-Breaking Antioxidants and Their Synthetic Analogs as Modulators of Oxidative Stress

Oxidative stress is associated with the increased production of reactive oxygen species or with a significant decrease in the effectiveness of antioxidant enzymes and nonenzymatic defense. The penetration of oxygen and free radicals in the hydrophobic interior of biological membranes initiates radical disintegration of the hydrocarbon “tails” of the lipids. This process is known as “lipid peroxidation”, and the accumulation of the oxidation products as peroxides and the aldehydes and acids derived from them are often used as a measure of oxidative stress levels. In total, 40 phenolic antioxidants were selected for a comparative study and analysis of their chain-breaking antioxidant activity, and thus as modulators of oxidative stress. This included natural and natural-like ortho-methoxy and ortho-hydroxy phenols, nine of them newly synthesized. Applied experimental and theoretical methods (bulk lipid autoxidation, chemiluminescence, in silico methods such as density functional theory (DFT) and quantitative structure–activity relationship ((Q)SAR) modeling) were used to clarify their structure–activity relationship. Kinetics of non-inhibited and inhibited lipid oxidation in close connection with inhibitor transformation under oxidative stress is considered. Special attention has been paid to chemical reactions resulting in the initiation of free radicals, a key stage of oxidative stress. Effects of substituents in the side chains and in the phenolic ring of hydroxylated phenols and biphenols, and the concentration were discussed.

Molecular structure, NBO analysis of the hydrogen-bonded interactions, spectroscopic (FT-IR, FT-Raman), drug likeness and molecular docking of the novel anti COVID-2 molecule (2E)-N-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide (Dimer) - quantum chemical approach

Prospective antiviral molecule (2E)-N-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide has been probed using Fourier transform infrared (FTIR), FT-Raman and quantum chemical computations. The geometry equilibrium and natural bond orbital analysis have been carried out with density functional theory employing Becke, 3-parameter, Lee-Yang-Parr method with the 6-311G++(d,p) basis set. The vibrational assignments pertaining to different modes of vibrations have been augmented by normal coordinate analysis, force constant and potential energy distributions. Drug likeness and oral activity have been carried out based on Lipinski's rule of five. The inhibiting potency of 2(2E)-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide has been investigated by docking simulation against SARS-CoV-2 protein. The optimized geometry shows a planar structure between the chromone and the side chain. Differences in the geometries due to the substitution of the electronegative atom and intermolecular contacts due to the chromone and hydrazinecarbothioamide were analyzed. NBO analysis confirms the presence of two strong stable hydrogen bonded NH⋯O intermolecular interactions and two weak hydrogen bonded CH⋯O interactions. The red shift in NH stretching frequency exposed from IR substantiates the formation of NH⋯O intermolecular hydrogen bond and the blue shift in CH stretching frequency substantiates the formation of CH⋯O intermolecular hydrogen bond. Drug likeness, absorption, distribution, metabolism, excretion and toxicity property gives an idea about the pharmacokinetic properties of the title molecule. The binding energy of the nonbonding interaction with Histidine 41 and Cysteine 145, present a clear view that 2(2E)-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide can irreversibly interact with SARS-CoV-2 protease.

Cytotoxicity of Moringa oleifera fruits on human liver cancer and molecular docking analysis of bioactive constituents against caspase-3 enzyme

Moringa oleifera is an excellent source of phenolics and flavonoids comprise various pharmacological activities. The fourth widespread leading cause of the patients' death is liver cancer. This study was formulated to perform the antiproliferative activity of Moringa oleifera fruit (MOF) extract on human liver cancer HepG2 cells and computational validation of cell death. HepG2 cell line was treated with 25, 50, 75, 100, and 200 µg/ml of MOF extract for 48 hr, and antiproliferative activity was analyzed using MTT assay, nuclear condensation, annexin V-FITC/PI double stain, ROS generation, and apoptosis executioner enzyme caspase-3. MOF extract reduced the cell viability significantly (p ˂ .05) by increasing cellular apoptosis which was confirmed by annexin V-FITC/PI staining assay. In addition, MOF stimulated intracellular ROS production and subsequently induced caspase-3 activity depending upon dose. In silico analysis revealed the good binding interaction between amino acid residues of caspase-3 (PDB ID: 1GFW) protein and selected active constituents of MOF. PASS analyses of the phytoconstituents showed no violation of Lipinski's rule of five. Analysis of drug-likeness and toxicity measurement exhibited drug-like candidates with no predicted toxicity. In conclusion, this study showed the potential anticancer activity of MOF extract which may be valuable source for anticancer drug development. PRACTICAL APPLICATIONS: Moringa oleifera fruit extract induced the anti-proliferative activity against human hepatocellular carcinoma HepG2 cells through ROS-mediated apoptosis and activation of caspase-3 enzyme. Structure-based virtual screening study between bioactive components of Moringa oleifera fruits and apoptosis executioner caspase-3 enzyme has validated the anti-proliferative activity of Moringa oleifera fruit extract. Interestingly, active phytoconstituents of Moringa oleifera fruit exhibited drug-like candidates with no predicted toxicity. Thus, Moringa oleifera fruit could be used as valuable source for anticancer drug development against human liver cancer with relatively non-toxic to healthy cells.

Computer-assisted evaluation of plant-derived β-secretase inhibitors in Alzheimer’s disease

Background

Alzheimer’s disease (AD) is a progressive neurodegenerative age-related dementia that results in memory loss of elderly people. Many hypotheses have been formally articulated till now to decipher the pathogenesis of this disease. According to the compelling amyloidogenic hypothesis, β-secretase is a key regulatory enzyme in AD development and is therefore considered as one of the major targets for the development of drugs to treat AD. In this study, 40 plant-derived phytocompounds, proven to have β-secretase inhibitory activity in different laboratory experiments, were evaluated using computational approaches in order to identify the best possible β-secretase inhibitor(s).

Results

Amentoflavone (IFD score: − 7.842 Kcal/mol), Bilobetin (IFD score: − 7.417 Kcal/mol), and Ellagic acid (IFD score: − 6.923 Kcal/mol) showed highest β-secretase inhibitory activities with high binding affinity among all the selected phytocompounds and interacted with key amino acids, i.e., Asp32, Tyr71, and Asp228 in the catalytic site of β-secretase. Moreover, these three molecules exhibited promising results in different drug potential assessment experiments and displayed signs of correlation with significant pharmacological and biological activities.

Conclusion

Amentoflavone, Biolbetin, and Ellagic acid could be investigated further in developing β-secretase-dependent drug for the effective treatment of AD. However, additional in vivo and in vitro experiments might be required to strengthen the findings of this experiment.

Automation and data-driven design of polymer therapeutics

Polymers are uniquely suited for drug delivery and biomaterial applications due to tunable structural parameters such as length, composition, architecture, and valency. To facilitate designs, researchers may explore combinatorial libraries in a high throughput fashion to correlate structure to function. However, traditional polymerization reactions including controlled living radical polymerization (CLRP) and ring-opening polymerization (ROP) require inert reaction conditions and extensive expertise to implement. With the advent of air-tolerance and automation, several polymerization techniques are now compatible with well plates and can be carried out at the benchtop, making high throughput synthesis and high throughput screening (HTS) possible. To avoid HTS pitfalls often described as "fishing expeditions," it is crucial to employ intelligent and big data approaches to maximize experimental efficiency. This is where the disruptive technologies of machine learning (ML) and artificial intelligence (AI) will likely play a role. In fact, ML and AI are already impacting small molecule drug discovery and showing signs of emerging in drug delivery. In this review, we present state-of-the-art research in drug delivery, gene delivery, antimicrobial polymers, and bioactive polymers alongside data-driven developments in drug design and organic synthesis. From this insight, important lessons are revealed for the polymer therapeutics community including the value of a closed loop design-build-test-learn workflow. This is an exciting time as researchers will gain the ability to fully explore the polymer structural landscape and establish quantitative structure-property relationships (QSPRs) with biological significance.

Network pharmacology and molecular docking study on the active ingredients of qidengmingmu capsule for the treatment of diabetic retinopathy

Diabetic retinopathy (DR) is a leading cause of irreversible blindness globally. Qidengmingmu Capsule (QC) is a Chinese patent medicine used to treat DR, but the molecular mechanism of the treatment remains unknown. In this study, we identified and validated potential molecular mechanisms involved in the treatment of DR with QC via network pharmacology and molecular docking methods. The results of Ingredient-DR Target Network showed that 134 common targets and 20 active ingredients of QC were involved. According to the results of enrichment analysis, 2307 biological processes and 40 pathways were related to the treatment effects. Most of these processes and pathways were important for cell survival and were associated with many key factors in DR, such as vascular endothelial growth factor-A (VEGFA), hypoxia-inducible factor-1A (HIF-1Α), and tumor necrosis factor-α (TNFα). Based on the results of the PPI network and KEGG enrichment analyses, we selected AKT1, HIF-1α, VEGFA, TNFα and their corresponding active ingredients for molecular docking. According to the molecular docking results, several key targets of DR (including AKT1, HIF-1α, VEGFA, and TNFα) can form stable bonds with the corresponding active ingredients of QC. In conclusion, through network pharmacology methods, we found that potential biological mechanisms involved in the alleviation of DR by QC are related to multiple biological processes and signaling pathways. The molecular docking results also provide us with sound directions for further experiments.

Isatin-based virtual high throughput screening, molecular docking, DFT, QM/MM, MD and MM-PBSA study of novel inhibitors of SARS-CoV-2 main protease

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a rapidly growing health care emergency across the world. One of the viral proteases called main protease or Mpro, plays a crucial role in the replication of SARS-CoV-2. As the structure of Mpro of SARS-CoV-2 is similar to the Mpro of SARS-CoV-1 (responsible for SARS outbreak between 2002 and 2004), we hypothesize that the inhibitors of SARS-CoV-1 Mpro can also inhibit the Mpro of SARS-CoV-2. To test this hypothesis, a total of 79 isatin derivatives, which inhibited Mpro activity under in vitro conditions, were selected from the literature, and then screened through AutoDock Vina. The chemical features of the top 5 isatin derivatives with low binding energies (-8.5 to -8.2 kcal/mol) were used to screen similar types of compounds from several small-molecule libraries containing 15856508 compounds. A total of 1,609 compounds with similarity score ≥ 6 were screened and then subjected to docking as well as ADME analysis. Among the compounds screened, 4 ligands form Zinc drug-like library (ZINC000008848565, ZINC000009513563, ZINC000036759789 and ZINC000046053855) showed good ADMET properties, low binding energy (-8.4 to -8.6 kcal/mol), low interaction energy (-72.62 to -50.01 kcal/mol) and high structural stability with Mpro. Hence, the selected ligands might serve as the lead candidates for further wet laboratory validation, optimization and development.Communicated by Ramaswamy H. Sarma.

Network Pharmacology and Pharmacological Evaluation Reveals the Mechanism of the Sanguisorba Officinalis in Suppressing Hepatocellular Carcinoma

Background: Sanguisorba Officinalis L. (SO) is a well-known traditional Chinese medicine (TCM), commonly applied to treat complex diseases, such as anticancer, antibacterial, antiviral, anti-inflammatory, anti-oxidant and hemostatic effects. Especially, it has been reported to exert anti-tumor effect in various human cancers. However, its effect and pharmacological mechanism on hepatocellular carcinoma (HCC) remains unclear. Methods: In this study, network pharmacology approach was applied to characterize the underlying mechanism of SO on HCC. Active compounds and potential targets of SO, as well as related genes of HCC were obtained from the public databases, the potential targets and signaling pathways were determined by protein-protein interaction (PPI), gene ontology (GO) and pathway enrichment analyses. And the compound-target and target-pathway networks were constructed. Subsequently, in vitro experiments were also performed to further verify the anticancer effects of SO on HCC. Results: By using the comprehensive network pharmacology analysis, 41 ingredients in SO were collected from the corresponding databases, 12 active ingredients screened according to their oral bioavailability and drug-likeness index, and 258 potential targets related to HCC were predicted. Through enrichment analysis, SO was found to show its excellent therapeutic effects on HCC through several pathways, mainly related to proliferation and survival via the EGFR, PI3K/AKT, NFκB and MAPK signaling pathways. Additionally, in vitro, SO was found to inhibit cell proliferation, induce apoptosis and down-regulate cell migration and invasion in various HCC cells. Moreover, western blot analysis showed that SO treatment down-regulated the expression of p-EGFR, p-PI3K, p-AKT, p-NFκB and p-MAPK proteins in HepG2 cells. These results validated that SO exerted its therapeutic effects on HCC mainly by the regulation of cell proliferation and survival via the EGFR/MAPK and EGFR/PI3K/AKT/NFκB signaling pathways. Conclusion: Taken together, this study, revealed the anti-HCC effects of SO and its potential underlying therapeutic mechanisms in a multi-target and multi-pathway manner.

Graph neural networks for automated de novo drug design

The goal of de novo drug design is to create novel chemical entities with desired biological activities and pharmacokinetics (PK) properties. Over recent years, with the development of artificial intelligence (AI) technologies, data-driven methods have rapidly gained in popularity in this field. Among them, graph neural networks (GNNs), a type of neural network directly operating on the graph structure data, have received extensive attention. In this review, we introduce the applications of GNNs in de novo drug design from three aspects: molecule scoring, molecule generation and optimization, and synthesis planning. Furthermore, we also discuss the current challenges and future directions of GNNs in de novo drug design.

Anticancer activities of phenolic compounds from Moringa oleifera leaves: in vitro and in silico mechanistic study

Background

Moringa oleifera is a common vegetable in many countries since ancient times, possesses numerous phenolic compounds having a wide array of biological activities. It possesses anticancer activity that can be used to develop new drugs for treatment of various types of cancers. The current study was conducted to evaluate the composition of phenolic compounds and in vitro and in silico anticancer activities of M. oleifera leaves extracts. The leaves of M. oleifera were subjected to extraction for solvent fraction using n-hexane, chloroform, ethyl acetate, butanol, and aqueous solvents. The solvent fractions were tested for anticancer activity in vitro against Hela cancer cell line and screened for phenolic compounds through reversed-phase high-performance liquid chromatography. The molecular docking approach was employed to check binding conformations of phytochemicals against the target protein.

Result

The result revealed that all the solvent fractions possess in vitro anticancer activity against Hela cancer cell line. The n-hexane fraction showed a 50% reduction in Hela cancer cell viability at 416 μg mL−1 as compared to control. The extracts of solvent-fraction contained 10 phenolic compounds viz. quercetin, gallic acid, sinapic acid, vanillic acid, 4-hydroxy-3-methoxy benzoic acid, p-coumaric acid, m-coumaric acid, 4-hydroxy-3-methoxy cinnamic acid, caffeic acid, and syringic acid. Molecular docking studies revealed that the ligands bind within the active site of target protein have good binding energy values.

Conclusion

This study shows that M. oleifera leaves may have the potential to inhibit cancer cell growth and improving human health in addition to food ingredient innovations. Based on in vitro and in silico results, the phytochemicals from M. oleifera leaves can be used as leading drugs to treat cancer.

Graphical abstract

Efficient Prediction of In Vitro Piroxicam Release and Diffusion From Topical Films Based on Biopolymers Using Deep Learning Models and Generative Adversarial Networks

The purpose of this study was to simultaneously predict the drug release and skin permeation of Piroxicam (PX) topical films based on Chitosan (CTS), Xanthan gum (XG) and its Carboxymethyl derivatives (CMXs) as matrix systems. These films were prepared by the solvent casting method, using Tween 80 (T80) as a permeation enhancer. All of the prepared films were assessed for their physicochemical parameters, their in vitro drug release and ex vivo skin permeation studies. Moreover, deep learning models and machine learning models were applied to predict the drug release and permeation rates. The results indicated that all of the films exhibited good consistency and physicochemical properties. Furthermore, it was noticed that when T80 was used in the optimal formulation (F8) based on CTS-CMX3, a satisfactory drug release pattern was found where 99.97% of PX was released and an amount of 1.18 mg/cm² was permeated after 48 h. Moreover, Generative Adversarial Network (GAN) efficiently enhanced the performance of deep learning models and DNN was chosen as the best predictive approach with MSE values equal to 0.00098 and 0.00182 for the drug release and permeation kinetics, respectively. DNN precisely predicted PX dissolution profiles with f₂ values equal to 99.99 for all the formulations.

Study on the Potential Mechanism of Fructus Tribuli in the Treatment of Hypertensive Vascular Remodeling Based on Network Pharmacology and Molecular Docking

Background

Hypertensive vascular remodeling (HVR) is the pathophysiological basis of hypertension, which is also an important cause of vascular disease and target organ damage. Treatment with Fructus Tribuli (FT), a traditional Chinese medicine, has a positive effect on HVR. However, the pharmacological mechanisms of FT are still unclear. Therefore, this study aimed to reveal the potential mechanisms involved in the effects of FT on HVR based on network pharmacology and molecular docking.

Materials and Methods

We selected the active compounds and targets of FT according to the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the Swiss Target Prediction database, and the targets of HVR were collected from the Online Mendelian Inheritance in Man (OMIM), GeneCards, and DrugBank databases. The protein-protein interaction network (PPI) was established using the STRING database. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and network analysis were performed to further explore the potential mechanisms. Finally, molecular docking methods were used to evaluate the affinity between the active compounds and the main target.

Results

Seventeen active compounds of FT  and 164 potential targets for the treatment of HVR were identified. Component-target and PPI networks were constructed, and 12 main active components and 33 main targets were identified by analyzing the topological parameters. Additionally, GO analysis indicated that the potential targets were enriched in 483 biological processes, 52 cellular components, and 110 molecular functions. KEGG analysis revealed that the potential targets were correlated with 122 pathways, such as the HIF-1 signaling pathway, ErbB signaling pathway, and VEGF signaling pathway. Finally, molecular docking showed that the 12 main active components had a good affinity for the top five main targets.

Conclusion

This study demonstrated the multiple compounds, targets, and pathway characteristics of FT in the treatment of HVR. The network pharmacology method provided a novel research approach to analyze potential mechanisms.

Emodin Inhibits the Proliferation of MCF-7 Human Breast Cancer Cells Through Activation of Aryl Hydrocarbon Receptor (AhR)

Natural products have proved to be a promising source for the development of potential anticancer drugs. Emodin, a natural compound from Rheum palmatum, is used to treat several types of cancers, including lung, liver, and pancreatic. However, there are few reports regarding its use in the treatment of breast cancer. Thus, the therapeutic effect and mechanism of emodin on MCF-7 human breast cancer cells were investigated in this study. Morphological observations and cell viability were evaluated to determine the anti-proliferation activity of emodin. Network pharmacology and molecular docking were performed to screen the potential targets. Western blot analysis was used to explore a potential antitumor mechanism. The results showed that emodin (50–100 μmol/L) could significantly inhibit the proliferation of MCF-7 cells in a time and dose-dependent manner. Furthermore, virtual screening studies indicated that emodin was a potent aryl hydrocarbon receptor (AhR) agonist in chemotherapy for breast cancer. Finally, when MCF-7 cells were treated with emodin (100 μmol/L) for 24 h, the AhR and cytochrome P450 1A1 (CYP1A1) protein expression levels were significantly upregulated compared with the control group. Our study indicated that emodin exhibited promising antitumor activity in MCF-7 cells, likely through activation of the AhR-CYP1A1 signaling pathway. These findings lay a foundation for the application of emodin in breast cancer treatment.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

Quantum chemical insight into molecular structure, NBO analysis of the hydrogen-bonded interactions, spectroscopic (FT-IR, FT-Raman), drug likeness and molecular docking of the novel anti COVID-19 molecule 2-[(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluorophenyl)acetamide - dimer

Novel antiviral active molecule 2- [(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluoro- phenyl)acetamide has been synthesised and characterized by FT-IR and FT-Raman spectra. The equilibrium geometry, natural bond orbital calculations and vibrational assignments have been carried out using density functional B3LYP method with the 6-311G++(d,p) basis set. The complete vibrational assignments for all the vibrational modes have been supported by normal coordinate analysis, force constants and potential energy distributions. A detailed analysis of the intermolecular interactions has been performed based on the Hirshfeld surfaces. Drug likeness has been carried out based on Lipinski's rule and the absorption, distribution, metabolism, excretion and toxicity of the title molecule has been calculated. Antiviral potency of 2- [(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluoro-phenyl) acetamide has been investigated by docking against SARS-CoV-2 protein. The optimized geometry shows near-planarity between the phenyl ring and the pyrimidine ring. Differences in the geometries due to the substitution of the most electronegative fluorine atom and intermolecular contacts due to amino pyrimidine were analyzed. NBO analysis reveals the formation of two strong stable hydrogen bonded N-H···N intermolecular interactions and weak intramolecular interactions C-H···O and N-H···O. The Hirshfeld surfaces and consequently the 2D-fingerprint confirm the nature of intermolecular interactions and their quantitative contributions towards the crystal packing. The red shift in N-H stretching frequency exposed from IR substantiate the formation of N-H···N intermolecular hydrogen bond. Drug likeness and absorption, distribution, metabolism, excretion and toxicity properties analysis gives an idea about the pharmacokinetic properties of the title molecule. The binding energy -8.7 kcal/mol of the nonbonding interaction present a clear view that 2- [(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluoro- phenyl) acetamide can irreversibly interact with SARS-CoV-2 protease.

Exploration of the Potential Mechanism of Calculus Bovis in Treatment of Primary Liver Cancer by Network Pharmacology

AIM AND OBJECTIVE

Calculus Bovis (CB) has been employed to treat diseases for a long time. It has been identified to play significant anti-inflammatory and anti-tumor roles. However, the mechanism of treating primary liver cancer (PLC) remains to be revealed. This study aims to clarify the molecules and mechanisms of CB in treating PLC.

MATERIALS AND METHODS

After oral bioavailability (OB) and drug-likeness (DL) screening, 15 small molecules were identified as the potential ingredients against PLC. Following this, related targets network constructions and pathways were applied to clarify the mechanism of CB in treating PLC. An in vitro experiment was carried out to identify the function of CB in treating PLC.

RESULTS

Eleven compounds of CB were identified that play an anti-PLC role, including oleanolic acid, ergosterol, ursolic acid, etc. The potential targets which were observed include IL6, MAPK-8, VEGFA, Caspase-3, etc. Further analysis showed that the mechanism of CB in the treatment of PLC involved apoptosis-related pathways and immune-related pathways.

CONCLUSION

In summary, the current study combines network pharmacology and in vitro experiments to reveal the mechanism of CB against PLC. We concluded that 11 ingredients of CB have an anti-PLC effect. Furthermore, CB plays a key role in treating PLC mainly by apoptosisrelated pathways and immune-related pathways. Our experiment verifies that CB promotes the apoptosis of SMMC-7721.

Virtual screening of phytoconstituents from miracle herb nigella sativa targeting nucleocapsid protein and papain-like protease of SARS-CoV-2 for COVID-19 treatment

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel etiological agent of coronavirus disease 2019 (COVID-19). Nigella sativa, commonly known as black seed or black cumin, has been a historical and traditional plant since thousands of years. Based on their therapeutic efficacy, the chief components of terpenoids and flavonoids were selected from N. sativa seeds and seed oil. This study was designed to check the antiviral efficacy of N. sativa main phytoconstituents against five potential targets of SARS-CoV-2 using in silico structure-based virtual screening approach. Out of twenty five phytocomponents, ten components showed best binding affinity against two viral proteins viz. N-terminal RNA binding domain (NRBD; PDB ID: 6M3M) of nucleocapsid protein and papain-like protease (PL-_PRO; PDB ID: 6W9C) of SARS-CoV-2 using AutoDock 4.2.6, AutoDock Vina and iGEMDOCK. PASS analyses of all ten phytocomponents using Lipinski's Rule of five showed promising results. Further, druglikeness and toxicity assessment using OSIRIS Data Warrior v5.2.1 software exhibited the feasibility of phytocomponents as drug candidates with no predicted toxicity. Molecular dynamics simulation study of NRBD of SARS-CoV-2 nucleocapsid protein-alpha-spinasterol complex and PL-_PRO-cycloeucalenol complex displayed strong stability at 300 K. Both these complexes exhibited constant root mean square deviation (RMSDs) of protein side chains and Cα atoms throughout the simulation run time. Interestingly, PL-_PRO and NRBD are key proteins in viral replication, host cell immune evasion and viral assembly. Thus, NRBD and PL-_PRO have the potential to serve as therapeutic targets for N. sativa phytoconstituents in drug discovery process against COVID-19.