Prediction Of ADMET Properties Research Articles

Integration of 3D-QSAR, molecular docking, and machine learning techniques for rational design of nicotinamide-based SIRT2 inhibitors

Selective inhibitors of sirtuin-2 (SIRT2) are increasingly recognized as potential therapeutics for cancer and neurodegenerative diseases. Derivatives of 5-((3-amidobenzyl)oxy)nicotinamides have been identified as some of the most potent and selective SIRT2 inhibitors reported to date (​Ai et al., 2016​; ​Ai et al., 2023​, ​Baroni et al., 2007​). In this study, a 3D-QSAR (3D-Quantitative Structure-Activity Relationship) model was developed using a dataset of 86 nicotinamide-based SIRT2 inhibitors from the literature, along with GRIND-derived pharmacophore models for selected inhibitors. External validation parameters emphasized the reliability of the 3D-QSAR model in predicting SIRT2 inhibition within the defined applicability domain. The interpretation of the 3D-QSAR model facilitated the generation of GRIND-derived pharmacophore models, which in turn enabled the design of novel SIRT2 inhibitors. Furthermore, based on molecular docking results for the SIRT1–3 isoforms, two classification models were developed: a SIRT1/2 model using the Naive Bayes algorithm and a SIRT2/3 model using the k-nearest neighbors algorithm, to predict the selectivity of inhibitors for SIRT1/2 and SIRT2/3. External validation parameters of the selectivity models confirmed their predictive power. Ultimately, the integration of 3D-QSAR, selectivity models and prediction of ADMET properties facilitated the identification of the most promising selective SIRT2 inhibitors for further development.

ESTUDO IN SILICO DOS CONSTITUINTES DO MEL DA AROEIRA (Astronium urundeuva) E A CAPACIDADE DE INTERAÇÃO COM A PROTEINASE GP63 DA LEISHMANIOSE

Neglected tropical diseases represent a significant source of morbidity and mortality in developing countries, representing a global health challenge. Honey produced by bees is an organic food, composed of a mixture rich in nutrients, such as carbohydrates, amino acids, fatty acids, enzymes, minerals and vitamins. Depending on flowering, honey may contain phenolic compounds, flavorings and flavonoids, which may exhibit biological activities. In this in silico study on the constituents of mastic honey (Astronium urundeuva), we sought to predict the physicochemical, pharmacokinetic and toxicological properties, considering the interaction capacity of the constituents with the GP63 receptor, from Leishmaniasis. To this end, a systematic review of the literature was carried out and the molecular structures of the constituents were obtained, predicting ADMET properties and evaluating the binding affinity with molecular docking. Thus, information was obtained on the in silico physicochemical and pharmacological properties of the identified compounds, highlighting the potential biological activity for the treatment of Leishmaniasis, through the ability to interact with the GP63 protease. This focus will enable new investigations and therapeutic advances in the area.

In silico Investigation of Caged Xanthone Compounds Isolated from Cratoxylum sumatranum Stem Bark against Entamoeba histolytica Enzymes

Background: Amoebiasis is caused by Entamoeba histolytica, a pathogenic species living on human colon tissues. Metronidazole is currently used for the treatment of amoebiasis, but resistance of E. histolytica to the use of such treatment has been reported. Therefore, the development of new anti-amoebic drugs is still very much needed for clinical treatment. Preliminary research on extract and fractions from Cratoxylum sumatranum stem bark has shown their anti-amoebic activity. Two compounds from the cage xanthone groups, cochinchinoxanthone and cochinchinone D, have been isolated from the active fraction of C. sumatranum stem bark. Objective: This study aimed to investigate the anti-amoebic activity of the two known compounds against E. histolytica. Methods: The in silico method used was molecular docking with several receptors, including thioredoxin reductase, triose phosphate isomerase, pyruvate ferredoxin oxidoreductase, Giardia fructose-1,6-bisphosphate aldolase, serine acetyltransferase, and phosphoserine phosphatase. The prediction of ADMET properties was also carried out for both the compounds. Results: The results showed cochinchinone D to have a higher binding affinity to thioredoxin reductase, pyruvate ferredoxin oxidoreductase, and Giardia fructose-1,6-bisphosphate aldolase receptors than cochinchinoxanthone. In contrast, cochinchinoxanthone bound better to the triose phosphate isomerase and phosphoserine phosphatase receptors, while both exhibited the same affinity for serine acetyltransferase. In general, the two compounds were also found to have similar ADMET profiles. Conclusion: In conclusion, caged xanthone compounds from C. sumatranum have the potential to be developed as anti-amoebic agents against E. histolytica through the mechanism of inhibition of these enzymes.

In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review.

This article reviews computational research on benzimidazole derivatives. Cytotoxicity for all compounds against cancer cell lines was measured and the results revealed that many compounds exhibited high inhibitions. This research examines the varied pharmacological properties like anticancer, antibacterial, antioxidant, anti-inflammatory and anticonvulsant activities of benzimidazole derivatives. The suggested method summarises In silico research for each activity. This review examines benzimidazole derivative structure-activity relationships and pharmacological effects. In silico investigations can anticipate structural alterations and their effects on these derivative's pharmacological characteristics and efficacy through many computational methods. Molecular docking, molecular dynamics simulations and virtual screening help anticipate pharmacological effects and optimize chemical design. These trials will improve lead optimization, target selection, and ADMET property prediction in drug development. In silico benzimidazole derivative studies will be assessed for gaps and future research. Prospective studies might include empirical verification, pharmacodynamic analysis, and computational methodology improvement. This review discusses benzimidazole derivative In silico research to understand their specific pharmacological effects. This will help scientists design new drugs and guide future research. Latest, authentic and published reports on various benzimidazole derivatives and their activities are being thoroughly studied and analyzed. The overview of benzimidazole derivatives is more comprehensive, highlighting their structural diversity, synthetic strategies, mechanisms of action, and the computational tools used to study them. In silico studies help to understand the structure-activity relationship (SAR) of benzimidazole derivatives. Through meticulous alterations of substituents, ring modifications, and linker groups, this study identified the structural factors influencing the pharmacological activity of benzimidazole derivatives. These findings enable the rational design and optimization of more potent and selective compounds.

Computational investigation of novel synthetic analogs of C-1′β substituted remdesivir against RNA-dependent RNA-polymerase of SARS-CoV-2

Remdesivir, a C-nucleotide prodrug binds to the viral RNA-dependent-RNA polymerase (RdRp) and inhibits the viral replication by terminating RNA transcription prematurely. It is reported in literature that interaction between the C-1’β–CN moiety of Remdesivir (RDV) and the Ser861 residue in RdRp enzyme, causes a delayed chain termination during the RNA replication process and is one of the important aspect of its mechanism of action. In the pursuance of increasing the biological activity of RDV and enhancing the SAR studies, against RNA viruses, we have designed its fourteen C1’β substituted analogs, 10 –23 bearing 4/5-membered heterocyclic rings. The docking and 100 ns molecular dynamics (MD) simulations of 10-23 to the RdRp protein (PDB ID: 7L1F) revealed important interactions between 2’,3’-diol, oxo group of phosphoramidate, nitrogen residues of heterocyclic rings of synthetic molecules with Arg555, Arg553, Ser759, Cys622, Asn691, Asp623 amino acid residues of protein. The docking score of 2-ethylbutyl ((S)-(((2R,3S,4R,5R)-5-(4-aminopyrrolo[2,1-f][1,2,4]triazin-7-yl)-3,4-dihydroxy-5-(1H-1,2,3-triazol-4-yl)tetrahydrofuran-2-yl)methoxy)(phenoxy)phosphoryl)-L-alaninate, 11 was found to be the higher than RDV among 14 new compounds i.e. -5.20 kcal/mol. Out of 3 compounds, 10, 12 and 13 submitted for MD simulations and Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis, trifluoro-oxadiazole derivative, 13 showed higher binding energy as compared to Remdesivir. The predicted ADMET properties of 14 compounds showed their potential for being drug candidates. The present study suggests that substitution at the C1’β position by 4/5-membered rings plays an important role in the interactions between nucleoside/tide and target protein.

Chemical, antimicrobial, antioxidant, and molecular dynamic studies of spiro[chromane-3',2''-thiopyran]-4'-one 1''-oxide and spiro[chromane-3',2''-thiopyran]-4'-one 1'',1''-dioxide

Some of spiro[chromane-3',2''-thiopyran]-4'-ones were oxidized via m-chloroperbenzoic acid and/or hydrogen peroxide to afford the corresponding nine spiro[chromane-3',2''-thiopyran]-4'-one 1''-oxides and three spiro[chromane-3',2''-thiopyran]-4'-one 1'',1''-dioxides, which were confirmed by spectral data (IR, 1H NMR, 13C NMR, MS-ESI, and MS-EI). Whereas, the yield of oxidation using hydrogen peroxide is higher than that of oxidation using m-chloroperbenzoic acid. Moreover, Gram-negative bacteria (Escherichia coli ATCC 25922), and Gram-positive bacteria (Staphylococcus aureus ATCC 6538) were adopted to test the antimicrobial activity of 12 newly synthesized compounds (7a, 8a-c, 9a, 10a-c, 11a, 12a-c). The three sulfoxide derivatives 8b, 10b, and 10c showed antimicrobial activity. Also, antioxidant activity was evaluated for the newly synthesized compounds and shows that, all newly synthesized compounds (7a, 8a-c, 9a, 10a-c, 11a, 12a-c) have antioxidant activity. Molecular dynamic and system stability, Binding interaction mechanism based on binding free energy calculation, Identification of the critical residues responsible for ligands binding, Ligand–residue interaction network profiles, and In silico ADMET properties prediction were studied for the new compounds. The results of molecular dynamic studies were consistent with the in vitro antibacterial and antioxidant activity.

Hybrid fragment-SMILES tokenization for ADMET prediction in drug discovery

Background:Drug discovery and development is the extremely costly and time-consuming process of identifying new molecules that can interact with a biomarker target to interrupt the disease pathway of interest. In addition to binding the target, a drug candidate needs to satisfy multiple properties affecting absorption, distribution, metabolism, excretion, and toxicity (ADMET). Artificial intelligence approaches provide an opportunity to improve each step of the drug discovery and development process, in which the first question faced by us is how a molecule can be informatively represented such that the in-silico solutions are optimized.Results:This study introduces a novel hybrid SMILES-fragment tokenization method, coupled with two pre-training strategies, utilizing a Transformer-based model. We investigate the efficacy of hybrid tokenization in improving the performance of ADMET prediction tasks. Our approach leverages MTL-BERT, an encoder-only Transformer model that achieves state-of-the-art ADMET predictions, and contrasts the standard SMILES tokenization with our hybrid method across a spectrum of fragment library cutoffs.Conclusion:The findings reveal that while an excess of fragments can impede performance, using hybrid tokenization with high frequency fragments enhances results beyond the base SMILES tokenization. This advancement underscores the potential of integrating fragment- and character-level molecular features within the training of Transformer models for ADMET property prediction.

Unlocking the potential of higher-molecular-weight 5-HT7R ligands: Synthesis, affinity, and ADMET examination

An increasing number of drugs introduced to the market and numerous repositories of compounds with confirmed activity have posed the need to revalidate the state-of-the-art rules that determine the ranges of properties the compounds should possess to become future drugs. In this study, we designed a series of two chemotypes of aryl-piperazine hydantoin ligands of 5-HT7R, an attractive target in search for innovative CNS drugs, with higher molecular weight (close to or over 500). Consequently, 14 new compounds were synthesised and screened for their receptor activity accompanied by extensive docking studies to evaluate the observed structure–activity/properties relationships. The ADMET characterisation in terms of the biological membrane permeability, metabolic stability, hepatotoxicity, cardiotoxicity, and protein plasma binding of the obtained compounds was carried out in vitro. The outcome of these studies constituted the basis for the comprehensive challenge of computational tools for ADMET properties prediction. All the compounds possessed high affinity to the 5-HT7R (Ki below 250 nM for all analysed structures) with good selectivity over 5-HT6R and varying affinity towards 5-HT2AR, 5-HT1AR and D2R. For the best compounds of this study, the expression profile of genes associated with neurodegeneration, anti-oxidant response and anti-inflammatory function was determined, and the survival of the cells (SH-SY5Y as an in vitro model of Alzheimer’s disease) was evaluated. One 5-HT7R agent (32) was characterised by a very promising ADMET profile, i.e. good membrane permeability, low hepatotoxicity and cardiotoxicity, and high metabolic stability with the simultaneous high rate of plasma protein binding and high selectivity over other GPCRs considered, together with satisfying gene expression profile modulations and neural cell survival. Such encouraging properties make it a good candidate for further testing and optimisation as a potential agent in the treatment of CNS-related disorders.

Synthesis and antifungal evaluation of novel triazole derivatives bearing a pyrazole-methoxyl moiety

Life-threatening invasive fungal infections pose a serious threat to human health. A series of novel triazole derivatives bearing a pyrazole-methoxyl moiety were designed and synthesized in an effort to obtain antifungals with potent, broad-spectrum activity that are less susceptible to resistance. Most of these compounds exhibited moderate to excellent in vitro antifungal activities against Candida albicans SC5314 and 10,231, Cryptococcus neoformans 32,609, Candida glabrata 537 and Candida parapsilosis 22,019 with minimum inhibitory concentration (MIC) values of ≤0.125 μg/mL to 0.5 μg/mL. Use of recombinant Saccharomyces cerevisiae strains showed compounds 7 and 10 overcame the overexpression and resistant-related mutations in ERG11 of S. cerevisae and several pathogenic Candida spp. Despite being substrates of the C. albicans and Candida auris Cdr1 drug efflux pumps, compounds 7 and 10 showed moderate potency against five fluconazole (FCZ)-resistant fungi with MIC values from 2.0 μg/mL to 16.0 μg/mL. Growth kinetics confirmed compounds 7 and 10 had much stronger fungistatic activity than FCZ. For C. albicans, compounds 7 and 10 inhibited the yeast-to-hyphae transition, biofilm formation and destroyed mature biofilm more effectively than FCZ. Preliminary mechanism of action studies showed compounds 7 and 10 blocked the ergosterol biosynthesis pathway at Erg11, ultimately leading to cell membrane disruption. Further investigation of these novel triazole derivatives is also warranted by their predicted ADMET properties and low cytotoxicity.

Quantum-Informed Molecular Representation Learning Enhancing ADMET Property Prediction.

We examined pretraining tasks leveraging abundant labeled data to effectively enhance molecular representation learning in downstream tasks, specifically emphasizing graph transformers to improve the prediction of ADMET properties. Our investigation revealed limitations in previous pretraining tasks and identified more meaningful training targets, ranging from 2D molecular descriptors to extensive quantum chemistry simulations. These data were seamlessly integrated into supervised pretraining tasks. The implementation of our pretraining strategy and multitask learning outperforms conventional methods, achieving state-of-the-art outcomes in 7 out of 22 ADMET tasks within the Therapeutics Data Commons by utilizing a shared encoder across all tasks. Our approach underscores the effectiveness of learning molecular representations and highlights the potential for scalability when leveraging extensive data sets, marking a significant advancement in this domain.

Synthesis of isomeric β-cycloalkoxyphosphonated hydrazones containing a dioxaphosphorinane ring: Configurational and conformational investigation and molecular docking analysis

A series of Z/E isomeric β-cycloalkoxyphosphonated hydrazones [R1R2C(CH2O)2P(=O)CH2-C{=NN(H)R5}C(H)R3R4] 3 bearing a six-membered dioxaphosphorinane ring was prepared, structurally analysed, and subjected to in silico studies to assess their Acetylcholinesterase (AChE) and Butyrylcholinesterase (BuChE) inhibition activity. Furthermore, their ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties were determined. Hydrazones 3 were prepared from their corresponding allenylphosphonates [R1R2C(CH2O)2P(=O)C(H)=C=CR3R4] 2 by addition of various hydrazines NH2NHR5. Since NMR studies indicated the occurrence of several isomeric species in solution, conformational and configurational studies, based on mechanistic elucidation and energy stability using Density functional theory (DFT) calculation at the B3LYP/6-311 G++ (d,p) level of theory, were performed to determine their relative predominance. The hydrazone 1-(2-oxo-1,3,2-dioxaphosphoranyl)-3-phenylpropanone 3c was crystallographically analysed by single-crystal X-ray diffraction (SCXRD), revealing the occurrence of strong intra- and intermolecular NH···O hydrogen bonding. These secondary interactions were further examined by a Hirshfeld surface analysis. The experimental SCXRD parameters are compared with the theoretical calculated ones and the preferred configuration of product 3c was determined. Furthermore, the crystal structure of the symmetric azine 2,2′-((2E,2′E)-hydrazine-1,2-diylidenebis(3-phenylpropan-1-yl-2-ylidene))bis(5-methyl-5-propyl-1,3,2-dioxaphosphinane 2-oxide) 4, resulting from the reaction between hydrazone 3a and allene 2a, has been determined. To predict whether hydrazones 3 are appropriate as anticholinesterase agents exhibiting a potential activity for treatment of the Alzheimer disease, in silico molecular docking simulations were employed to investigate the interaction modes between hydrazones 3 and the active sites of the BuChE and AChE enzymes. Additionally, an ADMET-properties prediction was carried out for selected hydrazones to determine their pharmacokinetics and drug-likeness properties.

Synthesis, Characterization, DFT mechanistic study, Antibacterial Activity, Molecular modeling, and ADMET properties of novel chromone-isoxazole hybrids

A series of novel hybrid heterocycles incorporating isoxazole rings have been successfully synthesized, characterized, and evaluated for their antibacterial responses. The synthesized compounds were obtained by a subsequent N-alkylation and 1,3-dipolar cycloaddition reactions, their structures were identified by spectroscopy techniques (1H NMR, 13C NMR and 2D HSQC), and confirmed by mass spectrometry (HRMS). The spectroscopic data also show that the 1,3-dipolar cycloaddition (1,3-DC) reactions proceed in a regiospecific manner to give only one cycloadduct. The observed regioselectivity and the mechanistic study of the 1,3-DC reaction were further highlighted and explained using Density Functional Theory (DFT) calculations with the B3LYP/6–31G(d,p) basis set and Electron Localization Function (ELF) analysis. Moreover, the antibacterial screening shows that some compounds exhibit good efficacy against the tested bacterial strains. In addition, molecular docking studies were carried out to highlight the modes of interaction between the most active molecule 5d and receptors of Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus strains. The results show that the studied compound was effectively docked to the active sites of each responsible protein with high protein-binding energies in kcal/mol. Finally, the prediction of ADMET properties suggests that almost all the hybrid compounds have good pharmacokinetic profiles, with no observed signs of toxicity, except for compounds 5e, 5f and 5g.

Integrated machine learning-based virtual screening and biological evaluation for identification of potential inhibitors against cathepsin K.

Cathepsin K is a type of cysteine proteinase that is primarily expressed in osteoclasts and has a key role in the breakdown of bone matrix protein during bone resorption. Many studies suggest that the deficiency of cathepsin K is concomitant with a suppression of osteoclast functioning, therefore rendering the resorptive properties of cathepsin K the most prominent target for osteoporosis. This innovative work has identified a novel anti-osteoporotic agent against Cathepsin K by using a comparison of machine learning and deep learning-based virtual screening followed by their biological evaluation. Out of ten shortlisted compounds, five of the compounds (JFD02945, JFD02944, RJC01981, KM08968 and SB01934) exhibit more than 50% inhibition of the Cathepsin K activity at 0.1μM concentration and are considered to have a promising inhibitory effect against Cathepsin K. The comprehensive docking, MD simulation, and MM/PBSA investigations affirm the stable and effective interaction of these compounds with Cathepsin K to inhibit its function. Furthermore, the compounds RJC01981, KM08968 and SB01934 are represented to have promising anti-osteoporotic properties for the management of osteoporosis owing to their significantly well predicted ADMET properties.

Rational drug design from phosphatidylinositol 3-kinase-α inhibitors through molecular docking and 3D-QSAR methodologies for cancer immunotherapy

Dysregulation or aberrant activation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway is commonly observed in various cancers and is associated with tumor growth, metastasis, and resistance to therapy. Targeting PI3K-α with appropriate inhibitors can disrupt this pathway, hindering cancer progression, and potentially enhancing the immune system’s ability to recognize and eliminate cancer cells. In this study, we aimed to design a novel and potent inhibitor of PI3K-α for cancer immunotherapy using rational drug design techniques, including virtual screening, molecular docking, and 3D-QSAR. We obtained the human PI3K-α protein (6PYS) complexed with (3S)-3-benzyl-3-methyl-5-[5-(2-methylpyrimidin-5-yl)pyrazolo[1,5-a]pyrimidin-3-yl]-1,3-dihydro-2H-indol-2-one (PJ5) from the RCSB Protein Data Bank. Virtual screening of ligands, integrated with predictive computational molecular docking and 3D-field-based-QSAR, was implemented using appropriate Schrödinger Maestro modules. Rational drug design was also carried out, and its clinical relevance was validated across several ADMET descriptors. Docking results suggested that a hybrid of sulfonamide and pyridine-based heterocyclic compounds, functionalized with potent moieties derived from alkaloids, exhibited adequate synergistic biological effects capable of enhancing sufficient biological activity against PI3K-α. A field-based 3D-QSAR model was built on four partial least squares factors, and five statistical metrics were employed to validate the model. The newly designed ligand from this approach, named 6’-amino-5’-(2-fluoro-1,3-oxazol-5-yl)-N-{[3-(hydroxymethyl)oxetan-3-yl]methyl}-3-methyl-[2,3’-bipyridine]-6-sulfonamide or T85, exhibited a predicted bioactivity (pIC50) of 8.25. The predicted ADMET properties of T85 fell reasonably within the range of recommended standards, especially adhering to Lipinski’s rule of five and Jorgensen’s rule of three. In conclusion, the results of this study offer significant insights into in silico drug design using a rational approach, which could expedite the discovery and development of new drug molecules.

Synthesis, biological activity, X-ray crystallographic, DFT calculations and molecular dynamics simulation studies of 2-phenylthiazole-1,3,5-triazine derivatives as potential cholinesterase inhibitors

Seven 2-phenylthiazole-1,3,5-triazine derivatives were synthesized and estimated for cholinesterase inhibitory activity. All target compounds were characterized by 1H NMR, 13C NMR, FT-IR, and HRMS, and their purity was tested by HPLC to be above 99%. The in vitro enzyme inhibitory activity assays determined that the majority of compounds exhibited moderate to good inhibitory effects on acetylcholinesterase or butyrylcholinesterase and compound 6g displayed the best acetylcholinesterase (IC50 = 0.26 μM) and butyrylcholinesterase inhibitory activity (IC50 = 0.78 μM). The structures of compounds 6a and 6g was further investigated by single-crystal X-ray diffraction, and the Hirshfeld surfaces, crystal voids, and molecular energy framework analysis were further discussed to study the molecular conformations, crystal packing modes, intermolecular interactions, and their energies. What's more, the density functional theory calculations including frontier molecular orbitals, global reactivity descriptors, and molecular electrostatic potential were carried out to reveal the chemical reactivity and stability of 6a and 6g molecules. Molecular docking study indicated that compound 6g could not only interact with acetylcholinesterase but also with butyrylcholinesterase. Molecular dynamics simulations study showed that compound 6g could bind to acetylcholinesterase stably. In addition, ADMET properties prediction implied the drug-like properties and safety of seven compounds. Considering these results, several of the target compounds could work as novel cholinesterase inhibitors, and compound 6g may possess the potential to become a lead compound to develop novel anti-Alzheimer drugs.

Chemical constituents of the aerial parts of Mitracarpus hirtus (L.) DC (Rubiaceae)

A phytochemical investigation of the aerial parts of Mitracarpus hirtus afforded thirteen compounds, including a new naphthoquinone di-glycoside (1), three isopentenyl isoflavones (2−4), four flavonoids (5−8), three iridoid glycosides (9 − 11) and two coumarins (12 and 13). Their structures were elucidated based on extensive spectroscopic analyses, chemical methods, and the comparison with the literature. Among them, compound 1 possesses a 2-(3-methylnaphthalen-2-yl)acetic acid core with two glucosyl groups, compounds 2−4 are the first three representatives from the Rubiaceae family, and compounds 9−11 and 13 were isolated from Mitracarpus genus for the first time. Additionally, compounds 2−4 displayed potent antibacterial activities against Helicobacter pylori G27/HP159/JRES00015 (MIC = 4–16 μg/mL) , comparable to metronidazole. To date, wighteone (2) is the most active isoflavone with favourable predicted ADMET properties reported against H. pylori.

Identification of New EGFR Inhibitors by Structure-Based Virtual Screening and Biological Evaluation.

Epidermal growth factor receptor (EGFR) inhibitors have been used in clinical for the treatment of non-small-cell lung cancer for years. However, the emergence of drug resistance continues to be a major problem. To identify potential inhibitors, molecular docking-based virtual screening was conducted on ChemDiv and Enamine commercial databases using the Glide program. After multi-step VS and visual inspection, a total of 23 compounds with novel and varied structures were selected, and the predicted ADMET properties were within the satisfactory range. Further molecular dynamics simulations revealed that the reprehensive compound ZINC49691377 formed a stable complex with the allosteric pocket of EGFR and exhibited conserved hydrogen bond interactions with Lys 745 and Asp855 of EGFR over the course of simulation. All compounds were further tested in experiments. Among them, the most promising hit ZINC49691377 demonstrated excellent anti-proliferation activity against H1975 and PC-9 cells, while showing no significant anti-proliferation activity against A549 cells. Meanwhile, apoptosis analysis indicated that the compound ZINC49691377 can effectively induce apoptosis of H1975 and PC-9 cells in a dose-dependent manner, while having no significant effect on the apoptosis of A549 cells. The results indicate that ZINC49691377 exhibits good selectivity. Based on virtual screening and bioassays, ZINC4961377 can be considered as an excellent starting point for the development of new EGFR inhibitors.

Design of vilazodone-donepezil chimeric derivatives as acetylcholinesterase inhibitors by QSAR, molecular docking and molecular dynamics simulations.

Alzheimer's disease (AD) is a disease that affects the cognitive abilities of older adults, and it is one of the biggest global medical challenges of the 21st century. Acetylcholinesterase (AChE) can increase acetylcholine concentrations and improve cognitive function in patients, and is a potential target to develop small molecule inhibitors for the treatment of Alzheimer's disease (AD). In this study, 29 vilazodone-donepezil chimeric derivatives are systematically studied using 3D-QSAR modeling, and a robust and reliable Topomer CoMFA model was obtained with: q2 = 0.720, r2 = 0.991, F = 287.234, N = 6, and SEE = 0.098. Based on the established model and combined with the ZINC20 database, 33 new compounds with ideal inhibitory activity are successfully designed. Molecular docking and ADMET property prediction also show that these newly designed compounds have a good binding ability to the target protein and can meet the medicinal conditions. Subsequently, four new compounds with good comprehensive ability are selected for molecular dynamics simulation, and the simulation results confirm that the newly designed compounds have a certain degree of reliability and stability. This study provides guidance for vilazodone-donepezil chimeric derivatives as a potential AChE inhibitor and has certain theoretical value.

Virtual Screening to Identification The Active Compounds from Kratom (Mitragyna speciosa) as Analgetics

Kratom (Mytragina speciosa) is one of the endemic plants in Southeast Asia in general and Kalimantan in particular which is proven to have activities as an analgesic, sedative (antidepressant), and, antiobesity, breast anticancer, antinociceptive, CYP450 induction, anti-inflammatory, opiate, antimicrobial, and antioxidant. However, no report states the active compounds from kratom leaves that have these activities. Therefore, this study will identify the active compounds contained in kratom leaves with a virtual screening process responsible for analgesic activity along with the prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The virtual screening process was performed based on molecular tethering, while the prediction of ADMET properties was done with the help of a web server. The results obtained showed that the compound (-)-epicatechin was most responsible for the analgesic activity of kratom leaves. The ADMET profile of this compound predicts that it has good bioavailability and is non-toxic.

Prediction of In-silico ADMET Properties and Molecular docking study of Substituted Thiadiazole for screening of Antiviral activity against protein target Covid-19 main protease

The SARS-CoV-2 virus is the infectious disease known as coronavirus disease (COVID-19). The majority of COVID-19 patients will have mild to moderate symptoms and recover without additional care. However, some people will get serious illnesses and need medical attention. Designing novel medications and testing them for inhibitory action against the corona virus's primary targets could be a successful technique for the advancement of the drug discovery process and the treatment of corona virus disease in the context of the COVID-19 pandemic, which is spreading quickly. The objective of this work was to evaluate the physical-chemical, pharmacokinetic parameters (absorption, distribution, metabolism, excretion and toxicity) and pharmacodynamic parameters (bioactivity and adverse reactions) of Substituted thiadiazole by means of in-silico computational prediction. Online software such as Pre-ADMET, Molinspiration and Rule of Five were used for the analysis. In-silico results allow us to conclude that substituted thiadiazole is predicted to be a potential future drug candidate, due to its relevant Drug-likeness profile, bioavailability, excellent liposolubility and adequate pharmacokinetics, including at the level of CNS, penetrating the blood-brain barrier. Molecular docking studies of 20 designed compounds have also been performed to screen the inhibitory activity towards against protein target COVID-19 main protease (PDB: 6LU7). Among all the compounds C3 exhibited the most significant affinity score against COVID-19 main protease (PDB: 6LU7) and Shown best significant hydrogen bonds interaction at the active site of protein.