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

Drug repurposing against main protease and RNA-dependent RNA polymerase of SARS-CoV-2 using molecular docking, MM-GBSA calculations and molecular dynamics

A virus called severe acute respiratory distress syndrome coronavirus type 2 (SARS‐CoV‐2) is the causing organism of coronavirus disease 2019 (COVID-19), which has severely affected human life and threatened public health. The pandemic took millions of lives worldwide and caused serious negative effects on human society and the economy. SARS-CoV-2 main protease (Mpro) and RNA-dependent RNA polymerase (RdRp) are interesting targets due to their crucial role in viral replication and growth. Since there is only one approved therapy for COVID-19, drug repurposing is a promising approach to finding molecules with potential activity against COVID-19 in a short time and at minimal cost. In this study, virtual screening was performed on the ChEMBL library containing 9923 FDA-approved drugs, using various docking filters with different accuracy. The best drugs with the highest docking scores were further examined for molecular dynamics (MD) studies and MM-GBSA calculations. The results of this study suggest that nadide, cangrelor and denufosol are promising potential candidates against COVID-19. Further in vitro, preclinical and clinical studies of these candidates would help to discover safe and effective anti-COVID-19 drugs.

Network Pharmacology and Molecular Docking Analysis of the Mechanism Underlying Yikunyin's Therapeutic Effect on Menopausal Syndrome

Objective

Yikunyin is an empirical prescription that exhibits good efficacy in the clinical treatment of menopausal syndrome; however, its underlying mechanism remains unclear. This study investigates the mechanism implicated in the therapeutic effect of Yikunyin by identifying its hub genes, central pathways, and key active ingredients.

Method

The active ingredients and targets of Yikunyin were obtained from the Traditional Chinese Medicine Systems Pharmacology database, whereas the targets related to menopausal syndrome were obtained from GeneCards, PharmGKB, Therapeutic Target Database (TTD), and Comparative Toxicogenomics Database (CTD). To reveal the pharmacological mechanism, the component-target and the intersecting protein-protein interaction (PPI) networks were constructed, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. Finally, molecular docking was carried out to assess the strength of binding between the key active ingredients and key targets.

Results

A total of 418 targets and 121 active ingredients were identified in Yikunyin. The intersection of Yikunyin's 418 targets with the 2822 targets related to menopausal syndrome shows that there are 247 common targets that can be considered potential targets of Yikunyin in the treatment of menopausal syndrome. The topology analysis of the constructed PPI network conducted using the Cytoscape software shows that there are 15 hub genes implicated in the therapeutic effect of Yikunyin: AKT1, PRKCA, TLR9, CXCL10, PRKCD, PARP1, ABCB1, TP53, CAV1, MAPK8, PPARA, GRB2, EGFR, IL-6, and JAK2. Moreover, the key active components acting on these genes are paeoniflorin, luteolin, quercetin, beta-sitosterol, and kaempferol. GO and KEGG analyses indicate that Yikunyin can treat menopausal syndrome by regulating cellular response to chemical stress (GO:0062197), cellular response to oxidative stress (GO:0034599), phosphatase binding (GO:0019902), cytokine receptor binding (GO:0005126), PI3K-Akt signaling (hsa04151), lipid and atherosclerosis (hsa05417), and hepatitis B (hsa05161). Finally, the results of molecular docking suggest that the key active ingredients and key targets can bind well, with binding energies of less than −5 kJ/mol.

Conclusion

The research conducted herein reveals that Yikunyin treats menopausal syndrome by targeting AKT1 and IL-6 and by regulating the PI3K-Akt signaling pathway. Moreover, it provides a new idea for understanding the therapeutic effects of traditional Chinese medicines.

Elastase inhibitory activity of quinoline Analogues: Synthesis, kinetic mechanism, cytotoxicity, chemoinformatics and molecular docking studies

Herein, we have synthesized quinoline united various Schiff base derivatives (Q1-Q13) and systematically characterized them using diverse analytical practices such as ¹H NMR, ¹³C NMR, FT-IR and LC-MS respectively. All of the target compounds that have been synthesized were tested for elastase inhibition, and the findings were compared to the standard drug oleanolic acid. Among the entire series, compound Q11 (IC₅₀ = 0.897 ± 0.015 µM) exhibit most promising elastase inhibitory activity than oleanolic acid (Standard) having an IC₅₀ value of 13.426 ± 0.015 µM. Also, the utmost effectivecompound Q11 was used for kinetic mechanism investigation based on in-vitro data, from which it has been concluded that compound Q11 inhibits elastase competitively. Furthermore, utilizing the MTT test approach, the most effective compounds were assessed for cytotoxicity on B16F10 melanoma cells. From the cytotoxicity experiment, the most potent compound did not display any hazardous response against B16F10 melanoma cells despite being treated at high concentrations. Additionally, the molecular docking study was settled to govern the binding interaction pattern among an enzyme and inhibitors.

QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the Treatment of Schizophrenia

Forty-four bicyclo ((aryl) methyl) benzamides, acting as glycine transporter type 1 (GlyT1) inhibitors, are developed using molecular modeling techniques. QSAR models generated by multiple linear and non-linear regressions affirm that the biological inhibitory activity against the schizophrenia disease is strongly and significantly correlated with physicochemical, geometrical and topological descriptors, in particular: Hydrogen bond donor, polarizability, surface tension, stretch and torsion energies and topological diameter. According to in silico ADMET properties, the most active ligands (L6, L9, L30, L31 and L37) are the molecules having the highest probability of penetrating the central nervous system (CNS), but the molecule 32 has the highest probability of being absorbed by the gastrointestinal tract. Molecular docking results indicate that Tyr124, Phe43, Phe325, Asp46, Phe319 and Val120 amino acids are the active sites of the dopamine transporter (DAT) membrane protein, in which the most active ligands can inhibit the glycine transporter type 1 (GlyT1). The results of molecular dynamics (MD) simulation revealed that all five inhibitors remained stable in the active sites of the DAT protein during 100 ns, demonstrating their promising role as candidate drugs for the treatment of schizophrenia.

Differential Multimolecule Fingerprint for Similarity Search─Making Use of Active and Inactive Compound Sets in Virtual Screening

In conventional fingerprint methods, the similarity between two molecules is calculated using the Tanimoto index as a numerical criterion. Thus, the query molecules in virtual screening should be most representative of the wanted compound class at hand. In the concept introduced here, all available active molecules form a multimolecule fingerprint in which the appearing features are weighted according to their respective frequency. The features of inactive molecules are treated likewise and the resulting values are subtracted from those of the active ones. The obtained differential multimolecule fingerprint (DMMFP) is thus specific for the respective class of compounds. To account for the noninteger representation within this fingerprint, a modified Sørensen-Dice coefficient is used to compute the similarity. Potentially active molecules yield positive scores, whereas presumably inactive ones are denoted by negative values. The concept was applied to Angiotensin-converting enzyme (ACE) inhibitors, β2-adrenoceptor ligands, leukotriene A4 hydrolase inhibitors, dopamine D3 antagonists, and cytochrome CYP2C9 substrates, for which experimental binding affinities are known and was tested against decoys from DUD-E and a further background database consisting of molecules from the dark chemical matter, which comprises compounds that appear as frequent hitters across multiple assays. Using the 166 publicly available keys of the MACCS fingerprint and the larger PubChem fingerprint, actives were recovered with very high sensitivity. Furthermore, three marketed ACE inhibitors as well as the carbonic anhydrase II inhibitor dorzolamide were detected in the dark chemical matter data set. For comparison, the DMMFP was also used with a Bayesian classifier, for which the specificity (correctly classified inactives) and likewise the accuracy was superior. Conversely, the similarity score produced by the Sørensen-Dice coefficient showed its potential for the early recognition of (potentially) active molecules.

Clinical Evidence and Potential Mechanisms of Complementary Treatment of Ling Gui Zhu Gan Formula for the Management of Serum Lipids and Obesity

Objective. This study aims to evaluate the clinical effects of Ling Gui Zhu Gan formula (LGZG), a famous TCM formula, for the management of serum lipids and obesity and preliminarily elucidates the bioactive components and the potential mechanism. Methods. Cluster analysis was adopted to investigate the TCM herbs and their frequency of occurrence for treating hyperlipidemia and obesity in an academic experience database of Chinese famous TCM doctors (http://www.gjmlzy.com:83). Then, relevant randomized controlled trials (RCTs) about LGZG supplementation in improving lipid levels and obesity were retrieved and analyzed. Lastly, the integration of network pharmacology, as well as greedy algorithms, which are theoretically well founded for the set cover in computer science, was exploited to identify the bioactive components of LGZG and to reveal potential mechanisms for attenuation or reversal of hyperlipidemia and obesity. Results. Based on the cluster analysis of 104 cases in TCM academic experience database, four TCM herbs in LGZG showed high-use frequency for treating hyperlipidemia and obesity. Meta-analysis on 19 randomized controlled trials (RCTs) with 1716 participants indicated that LGZG supplementation significantly decreased the serum levels of total triglycerides, total cholesterol, low-density lipoprotein cholesterol, BMI, and body weight and increased high-density lipoprotein cholesterol, compared with clinical control groups. No serious adverse effect was detected in all studies. Twenty-one bioactive components of LGZG, mainly flavonoids (i.e., naringenin, kaempferol, and kumatakenin), saponins (i.e., hederagenin), and fatty acids (i.e., eicosenoic acid), had the potential benefits possibly by regulating multiple targets such as PTPN1, CYP19A1, and ESR2, as well as a few complex pathways including the TNF signaling pathway, PPAR signaling pathway, arachidonic acid metabolism, fat digestion, and absorption. Conclusion. The present study has proved the clinical value of LGZG as a complementary treatment for attenuation or reversal of hyperlipidemia and obesity. More high-quality clinical and experimental studies in the future are demanded to verify its effects and the precise mechanism of action.

Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus

Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure–activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R2 = 0.991 and Q2 = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R2 = 0.915 and Q2 = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.

Phytochemical Characterization, and Antioxidant and Antimicrobial Properties of Agitated Cultures of Three Rue Species: Ruta chalepensis, Ruta corsica, and Ruta graveolens

The in vitro cultures of the following three species of the genus Ruta were investigated: R. chalepensis, R. corsica, and R. graveolens. The dynamics of biomass growth and accumulation of secondary metabolites in the 3-, 4-, 5-, 6-, and 7-week growth cycle were analysed. The antioxidant capacity of the methanol extracts obtained from the biomass of the in vitro cultures was also assessed by different in vitro assays: 1,1-diphenyl-2-picrylhydrazil (DPPH), reducing power, and Fe²⁺ chelating activity assays. Moreover, a preliminary screening of the antimicrobial potential of the extracts was performed. The extracts were phytochemically characterized by high-performance liquid chromatography (HPLC), which highlighted the presence of linear furanocoumarins (bergapten, isoimperatorin, isopimpinellin, psoralen, and xanthotoxin) and furoquinoline alkaloids (γ-fagarine, 7-isopentenyloxy-γ-fagarine, and skimmianine). The dominant group of compounds in all the cultures was coumarins (maximum content 1031.5 mg/100 g DW (dry weight), R. chalepensis, 5-week growth cycle). The results of the antioxidant tests showed that the extracts of the three species had varied antioxidant capacity: in particular, the R. chalepensis extract exhibited the best radical scavenging activity (IC₅₀ = 1.665 ± 0.009 mg/mL), while the R. graveolens extract displayed the highest chelating property (IC₅₀ = 0.671 ± 0.013 mg/mL). Finally, all the extracts showed good activity against Staphylococcus aureus with MIC values of 250 μg/mL for the R. corsica extract and 500 μg/mL for both R. graveolens and R. chalepensis extracts.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Novel Design of Neuropeptide-Based Drugs with β-Sheet Breaking Potential in Amyloid-Beta Cascade: Molecular and Structural Deciphers

Our work discusses the investigation of 75 peptide-based drugs with the potential ability to break the β-sheet structures of amyloid-beta peptides from senile plaques. Hence, this study offers a unique insight into the design of neuropeptide-based drugs with β-sheet breaker potential in the amyloid-beta cascade for Alzheimer’s disease (AD). We started with five peptides (¹⁵QKLVFF²⁰, ¹⁶KLVFF²⁰, ¹⁷LVFF²⁰, ¹⁶KLVF¹⁹ and ¹⁵QKLV¹⁸), to which 14 different organic acids were attached at the N-terminal. It was necessary to evaluate the physiochemical features of these sequences due to the biological correlation with our proposal. Hence, the preliminary analysis of different pharmacological features provided the necessary data to select the peptides with the best biocompatibility for administration purposes. Our approaches demonstrated that the peptides ¹⁷LVFF²⁰, NA-¹⁷LVFF²⁰, ¹⁶KLVF¹⁹ and NA-¹⁶KLVF¹⁹ (NA-nicotinic acid) have the ability to interfere with fibril formation and hence improve the neuro and cognitive functions. Moreover, the peptide conjugate NA-¹⁶KLVF¹⁹ possesses attractive pharmacological properties, demonstrated by in silico and in vitro studies. Tandem mass spectrometry showed no fragmentation for the spectra of ¹⁶KLVF¹⁹. Such important results suggest that under the action of protease, the peptide cleavage does not occur at all. Additionally, circular dichroism confirmed docking simulations and showed that NA-¹⁶KLVF¹⁹ may improve the β-sheet breaker mechanism, and thus the entanglement process of amyloid-beta peptides can be more effective.

Exploring nature's bounty: identification of Withania somnifera as a promising source of therapeutic agents against COVID-19 by virtual screening and in silico evaluation

Coronaviruses are etiological agents of extreme human and animal infection resulting in abnormalities primarily in the respiratory tract. Presently, there is no defined COVID-19 intervention and clinical trials of prospective therapeutic agents are still in the nascent stage. Withania somnifera (L.) Dunal (WS), is an important medicinal plant in Ayurveda. The present study aimed to evaluate the antiviral potential of selected WS phytoconstituents against the novel SARS-CoV-2 target proteins and human ACE2 receptor using in silico methods. Most of the phytoconstituents displayed good absorption and transport kinetics and were also found to display no associated mutagenic or adverse effect(s). Molecular docking analyses revealed that most of the WS phytoconstituents exhibited potent binding to human ACE2 receptor, SAR-CoV and SARS-CoV-2 spike glycoproteins as well as the two main SARS-CoV-2 proteases. Most of the phytoconstituents were predicted to undergo Phase-I metabolism prior to excretion. All phytoconstituents had favorable bioactivity scores with respect to various receptor proteins and target enzymes. SAR analysis revealed that the number of oxygen atoms in the withanolide backbone and structural rearrangements were crucial for effective binding. Molecular simulation analyses of SARS-CoV-2 spike protein and papain-like protease with Withanolides A and B, respectively, displayed a stability profile at 300 K and constant RMSDs of protein side chains and Cα atoms throughout the simulation run time. In a nutshell, WS phytoconstituents warrant further investigations in vitro and in vivo to unravel their molecular mechanism(s) and modes of action for their future development as novel antiviral agents against COVID-19.

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.