Drug-likeness Prediction Research Articles

Identification of potential inhibitors of thymidylate synthase (TS) (PDB ID: 6QXH) and nuclear factor kappa-B (NF–κB) (PDB ID: 1A3Q) from Capsicum annuum (bell pepper) towards the development of new therapeutic drugs against colorectal cancer (CRC)

Abstract Colorectal cancer is the third most deadly cancer globally. Drug resistance and attendant side effects make the available standard anti-colorectal cancer drugs against target receptors inefficient. Phytochemicals from medicinal plants are safer, cheaper, effective, and heal diseases from the cellular level. This study is aimed at identifying potential inhibitors of thymidylate synthase (TS) and nuclear factor kappa-B (NF–κB) target receptors from Capsicum annuum towards the development of new therapeutic drugs against colorectal cancer via in silico approach. One hundred and fifty (150) ligands previously reported from Capsicum annuum were downloaded from the PubChem database and were subjected to chemo-informatics analyses such as ADMET, drug-likeness, oral bioavailability, bioactivity, and PASS prediction to ascertain their therapeutic and safety profile before docking. The ligands that passed the analyses were docked against TS and NF–κB in duplicate using a creditable docking tool (PyRx). Raltitrexed and emetine were used as the standard drug inhibitors for TS and NF–κB, respectively. The results obtained from this study showed that feruloyl-beta-D-glucose (8.45 kcal/mol), 5-O-caffeoylquinic acid (−8.40 kcal/mol), 5-O-caffeoylquinic acid methyl ester (−7.89 kcal/mol), feruloyl hexoside (−7.40 kcal/mol), O-glucopyranoside (−7.55 kcal/mol), and quercetin (−7.00 kcal/mol) shared the same binding pocket with TS while feruloyl-beta-D-glucose (−7.00 kcal/mol), chlorogenic acid (−6.90 kcal/mol), 5-O-caffeoylquinic acid (−6.90 kcal/mol) and feruloyl hexoside (−6.50 kcal/mol) shared the same pocket with NF–κB. These compounds were selected as best hits due to their excellent inhibitory efficiency and chemoinformatic profiles. Thus, the compounds may function as prospective lead compounds for developing a new anti-colorectal cancer drug.

HPLC-MS/MS analysis, antioxidant and α-amylase inhibitory activities of the endemic plant Ferula tunetana using in vitro and in silico methods

Ferula species have occupied great value in traditional medicine in various countries. The aim of the present paper was to assess for the first time to the phytochemical profile of Ferula tunetana Pomel ex Batt. (F. tunetana) seed and leaf extracts using HPLC-MS/MS, as well as to evaluate their antioxidant and anti-α-amylase activities. Therefore, total phenolic and flavonoid amounts of the studied extracts were determined and 38 compounds were detected and quantified (phenolic acids, flavonoids, non-phenolic acid and stilbenes). In addition of their antioxidant potential against 1,1-diphenyl-2-picrylhydrazyl (DPPH) and Ferric Reducing Antioxidant Power (FRAP) assays, in vitro and in silico studies were adopted to evaluate α-amylase inhibitory properties of F. tunetana extracts. Ethyl acetate (EtOAc) seed extract showed the highest total phenolic content, antioxidant potential (IC50 = 19.00 ± 0.37 μg/mL) in terms of free radical scavenging, also for α-amylase inhibition activity with IC50 values of 17.39 ± 0.92 μg/mL. The molecular docking analysis and the drug-likeness prediction of the major selected compounds explained the significant anti-α-amylase activity of the studied extracts. The obtained analysis and results suggest that F. tunetana extracts could be promising candidates to develop a new generation of efficient and especially safe antidiabetic treatments.

Structure-based virtual screening of chemical libraries as potential MELK inhibitors and their therapeutic evaluation against breast cancer

New targeted therapy for triple negative breast cancer (TNBC) is an urgent need, as advanced disease responds poorly to conventional chemotherapy. Genomic and proteomic studies are currently investigating new genes and proteins as promising therapeutic targets. One of such therapeutic targets is a cell cycle regulatory kinase; Maternal Embryonic Leucine Zipper Kinase (MELK), overexpressed in TNBC and correlated with cancer development. We performed molecular docking for virtual screening of chemical libraries (phytochemicals/synthetic drugs) against MELK protein structure and identified 8 phytoconstituents (isoxanthorin, emodin, gamma-coniceine, quercetin, tenuazonic acid, isoliquiritigenin, kaempferol, and Nobiletin) and 8 synthetic drugs (tetrahydrofolic acid, alfuzosin, lansoprazole, ketorolac, ketoprofen, variolin B, orantinib, and firestein) as potential hits interacting with the active site residues of MELK based on bound poses, hydrogen bond, hydrophobic interactions and MM/GBSA binding free energies. ADME and drug-likeness prediction further identified few hits with high drug-likeness properties and were further tested for anti-tumorigenic potential. Two phytochemicals isoliquiritigenin and emodin demonstrated growth inhibitory effects on TNBC MDA-MB-231 cells while much lower effect was observed on non-tumorigenic MCF-10A mammary epithelial cells. Treatment with both molecules downregulated MELK expression, induced cell cycle arrest, accumulated DNA damage and enhanced apoptosis. The study identified isoliquiritigenin and emodin as potential MELK inhibitors and provides a basis for subsequent experimental validation and drug development against cancer.

Anti-quorum sensing effects of batatasin III: in vitro and in silico studies

The spread of multidrug resistant bacteria has fueled the development of new antibiotics to combat bacterial infections. Disrupting the quorum sensing (QS) mechanism with biomolecules is a promising approach against bacterial infections. Plants used in Traditional Chinese Medicine (TCM) represent a valuable resource for the identification of QS inhibitors. In this study, the in vitro anti-QS activity of 50 TCM-derived phytochemicals against the biosensor Chromobacterium violaceum CV026 was tested. Among the 50 phytochemicals, 7-methoxycoumarin, flavone, batatasin III, resveratrol, psoralen, isopsoralen, and rhein inhibited violacein production and showed good QS inhibitory effects. Batatasin III was selected as the best QS inhibitor based on drug-likeness, physicochemical properties, toxicity, and bioactivity score prediction analyses using SwissADME, PreADMET, ProtoxII, and Molinspiration. At 30 μg/ mL, Batatasin III inhibited violacein production and biofilm formation in C. violaceum CV026 by more than 69% and 54% respectively without affecting bacterial growth. The in vitro cytotoxicity evaluation by MTT assay demonstrated that batatasin III reduced the viability of 3T3 mouse fibroblast cells to 60% at 100 μg/mL. Furthermore, molecular docking studies showed that batatasin III has strong binding interactions with the QS-associated proteins CViR, LasR, RhlR, PqsE, and PqsR. Molecular dynamic simulation studies showed that batatasin III has strong binding interactions with 3QP1, a structural variant of CViR protein. The binding free energy value of batatasin III-3QP1 complex was −146.295 ± 10.800 KJ/mol. Overall results suggested that batatasin III could serve as a lead molecule that could be developed into a potent QS inhibitor. Communicated by Ramaswamy H. Sarma

Imidazole-Based Alkaloids from Marine Sponges (Leucetta and Clathrina) as Potential Inhibitors Targeting SARS-CoV-2 Main Protease: An In Silico Approach

Imidazole-based compounds form a prominent class of heterocyclic compounds, displaying diverse applications, especially with regards to its biological and pharmacological activities. Molecular docking, simulations, and drug-likeness prediction were performed on 45 imidazole-based alkaloids from two species of marine sponges (Leucetta and Clathrina). The study seeks to identify possible inhibitors of the SARS-CoV-2 Main Protease in an effort to battle the prevailing pandemic which has been caused by the widespread infections of the SAR-CoV-2 virus in its varied mutated forms. Computational analysis with MOE 2015.10 program reveals that, among the imidazole-based alkaloids, Naamidines have a high affinity for the target protein (PDB ID:6W63), even interacting with the catalytic dyad, as compared to its non-covalent inhibitor X77. Among all the top-scoring ligands, Naamidine H produced the highest binding score of −8.87078 kcal/mol. MD simulation studies with NAMD confirms the stability of the interactions of Naamidines with the target protein. MM-GBSA calculations were performed on the top binding ligands which further confirms the binding affinity of the top-scoring ligands. Computational and pharmacological investigations in this study proposes Naamidines, as effective inhibitors of Mpro. Naamidine I, Naamidine E, and Pyronaamidine could be potential anti-viral candidates against SAR-CoV-2.

Synthesis of benzylidene-indandione derivatives as quantification of amyloid fibrils

The formation of amyloid fibrils due to its association with fatal diseases, including Alzheimer's, has been investigated by many researchers. These common diseases, mostly become verified when it is too late to be treated. Currently, no cure is available for neurodegenerative diseases, and the process of diagnosing amyloid fibrils in the early stages, while there are fewer amyloid fibrils, has become an issue of interest. To do so, determining new probes with the highest binding affinity to the lowest number of amyloid fibrils is necessary. In this study, we proposed to employ new synthesized benzylidene-indandione derivatives as amyloid fibrils fluorescent detection probes. Native soluble proteins of insulin, bovine serum albumin (BSA), BSA amorphous aggregation, and insulin amyloid fibrils were used to evaluate our compounds' specificity to the amyloid structure. While ten synthesized compounds were examined individually, four of them including 3d, 3g, 3i, and 3j showed a high binding affinity with selectivity and specificity to amyloid fibrils, and their binding properties were also confirmed with in silico analysis. The drug-likeness prediction results for selected compounds by Swiss ADME server shows a satisfactory percentage of blood-brain barrier (BBB) permeability and gastrointestinal (GI) absorption for the compounds 3g, 3i, and 3j. More evaluation is needed to determine all properties of compounds in vitro and in vivo.

Synthesis of benzylidene-benzofuranone derivatives as probes for detection of amyloid fibrils in cells

Aggregated protein is the common cause of a wide variety of neurodegenerative diseases, such as Alzheimer’s disease (AD), Parkinson’s disease, etc. It is proven that protein aggregation like amyloid β (Aβ) is one of the critical factors causing AD and, its diagnosis in the early stages of the disease is important for the treatment or prevention of AD. To have a better understanding of protein aggregation and its pathologies, there is a huge need to design and develop new and more trustworthy probe molecules for in vitro amyloid quantification and in vivo amyloid imaging. In this study, 17 new biomarker compounds, have been synthesized from benzofuranone derivatives, to detect and identify amyloid in vitro (dye-binding assay) as well as in the cell by staining method. According to the results, some of these synthetic derivatives can be considered suitable identifiers and quantifiers to detect amyloid fibrils in vitro. Compared to thioflavin T, 4 probes out of 17 probes have shown good results in selectivity and detectability of Aβ depositions, and their binding properties were also confirmed with in silico analysis. The drug-likeness prediction results for selected compounds by the Swiss ADME server show a satisfactory percentage of blood-brain barrier (BBB) permeability and gastrointestinal (GI) absorption. Among all of them, compound 10 was able to show better binding properties than others, and in vivo study showed that this compound was capable of detecting intracellular amyloid. Communicated by Ramaswamy H. Sarma

2-[(2-Amino-6-methylpyrimidin-4-yl)sulfanyl]-N-arylacetamides: Discovery of a new class of anti-tubercular agents and prospects for their further structural modification

The synthesis of 2-[(2-amino-6-methylpyrimidin-4-yl)sulfanyl]-N-arylacetamides 6a-j was encouraged by their antibacterial activity and drug-likeness predictions. Of the compounds, two bearing 4‑isopropylphenyl 6c and 2,5‑dichlorophenyl 6i moieties were found to be threefold more potent than the first-line tuberculosis drug ethambutol. A molecular docking study revealed that compound 6c may selectively bind to cyclopropane mycolic acid synthase 1, an enzyme essential for the construction of the tuberculosis bacteria cell wall. Keeping this in mind, a recently developed ligand-based virtual screening strategy combining the molecular similarity search and docking approaches was adopted to identify more potent analogs of the parent compound. As a result, a series of new ligands 18p-w with phenyl-substituted azinyl amide groups were in silico discovered. Due to their high binding affinities to the enzyme and improved toxicity profiles, the ligands are undoubtedly worth future synthetic efforts.

Solvent potential effects (topological aspects, electron excitation), spectral characterization and biological attributes of NLO active 1-(2,4-dinitrophenyl)-2-((E)-3-phenylallylidene) hydrazine: Multiple anti tuberculosis agent

Density functional theory (DFT) method has many applications in relation to biological as well as NLO behaviour. It emerged as the most economically viable method for studying molecular structure. DNP2PAH is a synthesized compound with hydroxy derivatives that has undergone experimental and computational evaluation by DFT while being employed in a solvation research inquiry with a variety of solvents (protic, aprotic and non-polar solvents). DNP2PAH has been optimized and parameters were correlated for gas and solvents solvents [C2H3N, DMSO(aprotic), C2H6O, CH3OH(protic) and non-polar solvent (C6H6)]. FT-IR, FT-Raman, and UV–Vis spectrum has been computed and described in relation to practical analyses. It is revealed that observed and computational spectra are very similar. NBO technique utilized second order Fock matrix to assess the donor–acceptor correlations. The prominent interactions π*(C11-C12) - π*(C13-C14) with the highest stabilization energy of 208.65 kcal/ mol. Among solvents, benzene has the least softness (0.35496 (eV)) and the greatest energy gap (2.81721 (eV)). Global descriptors and molecular reactivity of the molecule were significantly changed by the liquids. Substance being studied possesses exceptional NLO (non-linear optical) properties. ELF, LOL and RDG enacted for both the gas phases and solvents. Whereas to convince the biological properties drug-likeness and biological score prediction were utilized. Additionally, this substance follows Lipinski's rule of five, which suggests that, in theory, taking an oral dose of such substances shouldn't be problematic. Results of molecular docking exemplified that the ligand under study is potent drug for anti-tuberculosis and anti-mycobacterium.

Acacia Honey-derived Bioactive Compounds Exhibit Induction of p53-dependent Apoptosis in the MCF-7 Human Breast Cancer Cell Line

Background Research studies have focused on discovering new anti-proliferative and pro-apoptotic agents derived from natural products from which honey constitutes a prominent candidate. The Acacia honey (AH) is known to display anticancer activity, but the mechanisms of action are still not well defined. Objectives Using in vitro and computational approaches, we aimed to assess the interaction among selected bioactive compounds derived from AH, with the apoptotic protein p53, which could trigger apoptosis. Materials and Methods The phytocompounds of AH were investigated via gas chromatography–mass spectrophotometry analysis. The cytotoxic effect and induced apoptosis on the MCF-7 breast cancer cell line were assessed by 3-(4,5-dimethylthiazolyl-2)-2,5 diphenyltetrazolium bromide and acridine orange-ethidium bromide staining approaches. The molecular docking analysis between AH compounds and p53 was carried out. Results The drug-likeness prediction revealed that most of the identified compounds meet Lipinski’s rules. We demonstrate that AH exerts an interesting cytotoxic effect in a dose-dependent manner against the MCF-7 cell line with IC50 5.053µg/mL. Significant cell alterations and notable induced apoptosis were detected when cells were treated with AH. The molecular docking analysis revealed that melezitose is among the most important potential bioactive compounds that interact with p53 leading to apoptosis. The binding affinity was −8.1 kcal/mol, and the closest molecular interactions in the active site included 10 residues, which could explain the potential biological activity. Conclusion This work sheds light on AH as a significant source of bioactive chemicals with potential for promoting apoptosis that may be exploited as an alternative therapy for breast cancer.

Highly Accessible Computational Prediction and In Vivo/In Vitro Experimental Validation: Novel Synthetic Phenyl Ketone Derivatives as Promising Agents against NAFLD via Modulating Oxidoreductase Activity.

Nonalcoholic fatty liver disease (NAFLD) has reached epidemic proportions with no pharmacological treatment approved. Several highly accessible computational tools were employed to predict the activities of twelve novel compounds prior to actual chemical synthesis. We began our work by designing two or three hydroxyl groups appended to the phenyl ketone core, followed by prediction of drug-likeness and targets. Most predicted targets for each compound overlapped with NAFLD targets (≥80%). Enrichment analysis showed that these compounds might regulate oxidoreductase activity. Then, these compounds were synthesized and confirmed by IR, MS, 1H, and 13C NMR. Their cell viability demonstrated that twelve compounds exhibited appreciable potencies against NAFLD (EC50 values ≤ 13.5 μM). Furthermore, the most potent compound 5f effectively prevented NAFLD progression as evidenced by the change in histological features. 5f significantly reduced total cholesterol and triglyceride levels in vitro/in vivo, and the effects of 5f were significantly stronger than those of the control drug. The proteomic data showed that oxidoreductase activity was the most significantly enriched, and this finding was consistent with docking results. In summary, this validated presynthesis prediction approach was cost-saving and worthy of popularization. The novel synthetic phenyl ketone derivative 5f holds great therapeutic potential by modulating oxidoreductase activity to counter NAFLD.

In Silico Analysis and ADMET Prediction of Flavonoid Compounds from Syzigium cumini var. album on α-Glucosidase Receptor for Searching Anti-Diabetic Drug Candidates

Background: One of the causes of death is diabetes. Anti-diabetic drugs currently available do not work optimally because some have been reported to have side effect and resistance. Objective: This study aimed to flavonoid compounds from Syzygium cumini var. album with the greatest anti-diabetic activity and lower toxicity than acarbose. Materials and Methods: This research is an in silico study of nine flavonoid compounds from Syzygium cumini var. album, starting with PASS online was used to predict the activity spectrum of substances, drug-likeness prediction using DruLiTo, ADMET prediction (absorption, distribution, metabolism, excretion, and toxicity) using pkCSM online. Molecular docking was carried out by the AutoDock 4.2.6 program on α-glucosidase targeting. Visualization is done with the Discovery Studio Visualizer software. Results: From the data obtained, D-(+)-Catechin has a high affinity for α-glucosidase with a free energy of binding (ΔG) -5.94 kcal/mol and an inhibition constant (Ki) of 44270 nm. Conclusion: Based on the results of the study, it can be concluded that the flavonoid compounds from Syzygium cumini var. album has the potential as a promising anti-diabetic drug candidate, where the best candidate is D- (+)-Catechin. However, further studies of flavonoid compounds from Syzygium cumini var. album are needed.

INVESTIGATION OF ANTI-SARS COV-2 ACTIVITY OF SOME TETRAHYDRO CURCUMIN DERIVATIVES: AN IN SILICO STUDY

Objective: In the current study, an in silico approach has been utilized to investigate the anti-SARS CoV 2 activity of some derivatives of Tetrahydro curcumin (THC), a curcumin metabolite. Methods: BioVia Draw 2017 was used to design 168 THC derivatives. All of the derivatives were docked using Maestro Schrodinger programme. Depending on the docking score, the ADME, drug-likeness, and toxicity prediction of a few THC derivatives were conducted. Results: 168 THC derivatives were designed. 14 derivatives exhibited a better binding score than Remdesivir. All 14 derivatives' pharmacokinetic characteristics were discovered to be within the acceptable range. Lipinski's rule of five was violated by all derivatives, including the reference drug, yet they all stayed within the recommended range. The greatest docking score among the 14 derivatives was displayed by Structure 21. A study on molecular dynamic (MD) stimulation showed that the protein-ligand complex was relatively stable. Toxicity prediction showed that 14 derivatives were non-hepatotoxic, non-cytotoxic, immunotoxic (except S21), non-mutagenic (except S31) and half of the developed structures were carcinogenic, while the other half, including the standard drug, was non-carcinogenic. Conclusion: Among 168 THC derivatives, 14 derivatives exhibited better binding score than the reference drug. For all 14 derivatives, pharmacokinetic, drug-likeness, and toxicity prediction were found to be satisfactory. It was discovered that the protein-ligand complex was thermodynamically stable. All 14 compounds present exciting prospects for further in vitro and in vivo investigation.

Current Trends and Challenges in Drug-Likeness Prediction: Are They Generalizable and Interpretable?

Importance: Drug-likeness of a compound is an overall assessment of its potential to succeed in clinical trials, and is essential for economizing research expenditures by filtering compounds with unfavorable properties and poor development potential. To this end, a robust drug-likeness prediction method is indispensable. Various approaches, including discriminative rules, statistical models, and machine learning models, have been developed to predict drug-likeness based on physiochemical properties and structural features. Notably, recent advancements in novel deep learning techniques have significantly advanced drug-likeness prediction, especially in classification performance. Highlights: In this review, we addressed the evolving landscape of drug-likeness prediction, with emphasis on methods employing novel deep learning techniques, and highlighted the current challenges in drug-likeness prediction, specifically regarding the aspects of generalization and interpretability. Moreover, we explored potential remedies and outlined promising avenues for future research. Conclusion: Despite the hurdles of generalization and interpretability, novel deep learning techniques have great potential in drug-likeness prediction and are worthy of further research efforts.

Bioactivity of dihydropyrimidinone derivatives as inhibitors of cyclooxygenase-2 (COX-2): an in silico approach.

Cyclooxygenase-2 (COX-2) is an enzyme involved in inflammation. The overexpression of COX-2 causes chronic inflammation, which can be prevented by COX-2 inhibitors. Generally, COX-2 inhibitors possess a carboxyl group and an aromatic ring in their molecular structure. These moieties are involved in the interaction with the active site of COX-2, thus playing a pivotal role in the inhibitory activity. Regarding the requisite molecular structure of COX-2 inhibitors, derivatives of dihydropyrimidinone (DHPM) are ideal candidates to be explored as COX-2 inhibitors, due to the ease of synthesis and their versatility to be transformed chemically. In this study, we prepared a novel small library consisting of 288 designed DHPM derivatives by varying the constituent components. The selection criteria of potential candidates for the COX-2 inhibitor of the data bank involve in silico studies via molecular docking investigations, prediction of ADMET and druglikeness, as well as molecular dynamics (MD) simulations. Molecular docking served as the initial step of selection, based on the comparison of grid score, docking pose, and interactions with those of lumiracoxib (LUR) as the original ligand of COX-2. The next criteria of selection were scores obtained from the ADMET and druglikeness by comparing the designed candidates with COX-2 inhibitors that were already marketed. Compound RDUE2 and SDT29 were the most potential candidates, which were further analyzed using the MD simulation. The results of the MD simulation indicated that RDUE2 and SDT29 interacted stably with amino acid residues on the active site of COX-2. The estimation of binding free energy indicated that SDT29 exhibited an inhibitory activity comparable to that of LUR, whereas RDUE2 showed a lower inhibitory activity than that of SDT29 and LUR.

MACHINE LEARNING-BASED VIRTUAL SCREENING, MOLECULAR DOCKING, DRUG-LIKENESS, PHARMACOKINETICS AND TOXICITY ANALYSES TO IDENTIFY NEW NATURAL INHIBITORS OF THE GLYCOPROTEIN SPIKE (S1) OF SARS-CoV-2

To identify natural bioactive compounds (NBCs) as potential inhibitors of spike (S1) by means of in silico assays. NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening and molecular docking to identify those with higher affinity to the spike protein. Eight machine learning models were used to validate the results: Principal Component Analysis (PCA), Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Partial Least Squares-Discriminant Analysis (PLS-DA), Gradient Boosted Tree Discriminant Analysis (XGBoostDA), Soft Independent Modelling of Class Analogies (SIMCA) and Logistic Regression Discriminate Analysis (LREG). Selected NBCs were submitted to drug-likeness prediction using Lipinski’s and Veber’s rule of five. A prediction of pharmacokinetic parameters and toxicity was also performed (ADMET). Antivirals currently used for COVID-19 (remdesivir and molnupiravir) were used as a comparator. A total of 170,906 compounds were analyzed. Of these, 34 showed greater affinity with the S1 (affinity energy < -7 kcal mol-1). Most of these compounds belonged to the class of coumarins (benzopyrones), presenting a benzene ring fused to a lactone (group of heterosides). The PLS-DA model was able to reproduce the results of the virtual screening and molecular docking (accuracy of 97.0%). Of the 34 compounds, only NBC5 (feselol), NBC14, NBC15 and NBC27 had better results in ADMET predictions. These had similar binding affinity to S1 when compared to remdesivir and molnupirvir. Feselol and three other NBCs were the most promising candidates for treating COVID-19. In vitro and in vivo studies are needed to confirm these findings.

-Based Virtual Screening Towards The Discovery of Usnic Acid Derivatives as Novel mTOR Inhibitor to Treat Breast Cancer

In this study, mTOR is chosen as the main target for breast cancer treatment. While the existing drugs still pose severe side effects, research on finding new anti-cancer drug should be done continuously. Usnic acid (UA) has been studied for its wide range of biological properties and potential in pharmaceutical research. A structure-based virtual screening approach is applied since it could reduce production time, cost and environmental issues. This study comprises molecular docking simulation, ADMET filtration and drug-likeness prediction. 340 UA derivatives were retrieved from literature and used to build an in-house database. The resulting compounds from docking were then filtered using ADMET prediction which comprises human intestine absorption, aqueous solubility, plasma protein binding, cytochrome P450 2D6 (CYP2D6) and hepatotoxicity parameters to identify the most potent UA derivatives with favourable physicochemical characteristics. After all, the hit compound, 118, was further stimulated in order to forecast its drug-like features. The chalcone-based scaffold of 118 resembled the reported breast cancer compound’s chemical structure strengthening the results obtained from this study. Thus, it is concluded that the structure-based virtual screening was an efficient and effective approach in the discovery of UA derivative, 118, as a potential novel mTOR inhibitor to treat breast cancer.

Virtual Screening of Flavonoid Compounds as A Main Protease Inhibitor for Anti-Sars-Cov-2 Candidates

In 2019, the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) was discovered in Hubei Province, China. After one year, no drug therapy has been approved, necessitating the development of SARS-CoV-2 drugs. To screen new drug candidates from natural product databases, the in-silico method, a drug discovery process based on computer simulations, can be utilized. Flavonoid is one of the most common compounds found in nature. They are secondary metabolites compounds contains phenolic functional group that can be virtually screened by predicting antiviral activity, drug likeness prediction, pharmacokinetic prediction, toxicity prediction, and molecular docking simulation Virtual screening is used in molecular docking simulations to design drugs based on activity prediction, compound similarity with oral drugs based on similar physical properties, pharmacokinetic profiles that include absorption and distribution, toxicity, and interactions of compounds with targets. The main protease used by target receptors is an enzyme that is important in determining SARS-CoV-2 survival. The structure of SARS-CoV-2 main protease code ID PDB 5RL4, 5R7Y and 7BUY is used in molecular docking simulation. The results of virtual screening of 80 flavonoid compounds showed that there are two most potential compounds, namely naringin and rutin that have lower ∆G values than the three native ligands, predictions of toxicity and good activity and a fairly good distribution profile.

Ursolic Acid and Solasodine as Potent Anti-Mycobacterial Agents for Combating Paratuberculosis: An Anti-Inflammatory and In Silico Analysis.

Mycobacterium avium subspecies paratuberculosis (MAP) infection in domestic livestock causes persistent diarrhea, weight loss, and death and is also a potential cause of Crohn's disease (CD) in humans; notably, treatments against MAP are insufficient, costly, and can cause adverse reactions. Hence, plant-derived bioactive constituents have been taken into consideration in this regard. Herein, we present the results of two bioactive constituents (Solasodine and Ursolic acid) that were evaluated for their safety and efficacy against MAP protein (Dephospho-Coenzyme A kinase (DPCK) by utilizing in vitro assays and different tools of in silico biology. The ADME/t-test, the drug-likeness property test, pharmacophore modelling, and PASS prediction have proven that both the constituents have better binding capacities than the available antibiotic drugs used to target protein inhibition pathways. Through our observations, it can be inferred that these two phytochemicals can be adequately used to treat paratuberculosis, thereby combating inflammatory bowel disorders (IBD) of an autoimmune nature.

MiDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies.

The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural networks, we developed three subdivisional models to individually predict the capacity of a compound to enter in vivo testing, clinical trials, and market approval stages. Furthermore, we proposed a strategy combing both active learning and ensemble learning to improve the quality of the models. The models achieved satisfactory performance in the internal test datasets and four self-collected external test datasets. We also employed the models as a general index to make an evaluation on a widely known benchmark dataset DEKOIS 2.0, and surprisingly found a powerful ability on virtual screening tasks. Our model system (termed as miDruglikeness) provides a comprehensive drug-likeness prediction tool for drug discovery and development.