SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules.

To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours

Exploring Public Health Benefits of Dolichos Lablab as A Dietary Supplement During The COVID-19 Outbreak: A Computational Study

The emerging case of coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndromecoronavirus (SARS-CoV-2) virus has become a global health issue. Since there is no available developed vaccine, health-promoting foods play a vital role in maintaining the immune system against the disease. Dolichos lablab (DL), an unutilized highly nutritional legume, has an excellent potential to cope with this pandemic with various health benefit phytochemicals. This study appraised the possibility of phytochemical content from DL to prevent virus infection and hyperinflammation in COVID-19 in silico. DL’s phytochemicals from liquid chromatography–high-resolution mass spectrometry analysis were docked with several SARS-CoV-2 proteins, including main protease and HR. Also, NF-κB docking was executed to pursue anti-inflammatory properties. The drug-likeness properties of screened phytochemicals were then evaluated using SwissADME. According to the results, there were 16 phytochemicals with a high affinity to targeted proteins. Among those, five phytochemicals consistently gave a low binding affinity to all targeted proteins. Those five phytochemicals’ physicochemical properties, except for rutin and (9cis)-retinal, also coped with small-molecule bioavailability, permeability, and flexibility according to the SwissADME calculations. In conclusion, DL has a high probability of complementing the medical effort as dietary supplementation to modulate the immune system and prevent viral infection

PREDIKSI INTERAKSI SENYAWA METABOLIT SEKUNDER TANAMAN BELUNTAS (Pluchea indica) TERHADAP HMGCoA REDUCTASE SECARA IN SILICO

Background: Hyperlipidemia is a condition of abnormal lipid levels in the blood. Pluchea indica plants has not been widely used as a traditional herbal treatment to treat hyperlipidemia. Also, until now there has been no research on the interaction of secondary metabolites of Pluchea indica plants by In silico. Objective: To Predict the profile of Bioavailability, Affinity and Interaction of secondary metabolites of Pluchea indica plants against HMG-CoA Reductase by in silico Metode: Using the SwissADME web server with the BOILED-Egg method and use Druglikeness parameters to predict the bioavailability profile, as well as performing Molecular Docking using Autodock software to predict the affinity and interaction of secondary metabolite compounds in review with the Protein Plus web server. Result and conclusions: Of the 122 secondary metabolite compounds, 51 compounds are predicted to have a good oral bioavailability profile, indicated by high GI Absorptions. Of the 51 test compounds, there is one compound that is predicted to have potential affinity as an antihyperlipidemia and is predicted to have an interaction with HMG-CoA Reductase similar to antihyperlipidemia drugs of the statin group

Antibacterial and antibiofilm activities of Prangos acaulis Bornm. extract against Streptococcus mutans: an in silico and in vitro study

Introduction: Streptococcus mutans is a principal pathogenic agent in biofilm formation on the teeth surfaces and subsequently development of dental caries and plaque. Therefore, currently introducing novel anti-bacterial and anti-biofilm agents, especially plant based materials are highly regarded. This study was planned to investigate in silico and in vitro antibacterial activities of Prangos acaulis extracts against S. mutans in single and biofilm forms and their mutagenicity in Ames test. Methods: The anti-bacterial and anti-biofilm effects of methanol extracts from various parts of P. acaulis were evaluated using disk diffusion and microtiter assay. Moreover, the potential mutagenicity of the extracts was investigated using Ames test. In addition, dominant constitutes of P. acaulis that reported in previous studies were subjected to an in silico analysis. The ability of selected phytochemicals to inhibit the glucosyltransferase was evaluated using molecular docking method. Results: All tested extracts especially root extract had significant antibacterial activity against the single form of S. mutans and inhibited biofilm formation without any mutagenic activity. The results also confirmed that three compounds consisting of ar-curcumene, d-limonene and alpha-pinene had strong and appropriate interactions to glucosyltransferase. Conclusion: This study indicated that P. acaulis has potent antibacterial and biofilm inhibition activity against S. mutans and can be good candidate for in vitro and in vivo studies with the aim of introducing novel inhibitors of dental caries developmen

VB-MK-LMF: Fusion of drugs, targets and interactions using Variational Bayesian Multiple Kernel Logistic Matrix Factorization

Background Computational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies. Other studies showed that specificities of the DTI task, such as weighting the observations and focusing the side information are also vital for reaching top performance. Method We present Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of (1) multiple kernel learning, (2) weighted observations, (3) graph Laplacian regularization, and (4) explicit modeling of probabilities of binary drug-target interactions. Results VB-MK-LMF achieves significantly better predictive performance in standard benchmarks compared to state-of-the-art methods, which can be traced back to multiple factors. The systematic evaluation of the effect of multiple kernels confirm their benefits, but also highlights the limitations of linear kernel combinations, already recognized in other fields. The analysis of the effect of prior kernels using varying sample sizes sheds light on the balance of data and knowledge in DTI tasks and on the rate at which the effect of priors vanishes. This also shows the existence of ``small sample size'' regions where using side information offers significant gains. Alongside favorable predictive performance, a notable property of MF methods is that they provide a unified space for drugs and targets using latent representations. Compared to earlier studies, the dimensionality of this space proved to be surprisingly low, which makes the latent representations constructed by VB-ML-LMF especially well-suited for visual analytics. The probabilistic nature of the predictions allows the calculation of the expected values of hits in functionally relevant sets, which we demonstrate by predicting drug promiscuity. The variational Bayesian approximation is also implemented for general purpose graphics processing units yielding significantly improved computational time. Conclusion In standard benchmarks, VB-MK-LMF shows significantly improved predictive performance in a wide range of settings. Beyond these benchmarks, another contribution of our work is highlighting and providing estimates for further pharmaceutically relevant quantities, such as promiscuity, druggability and total number of interactions. Availability Data and code are available at http://bioinformatics.mit.bme.hu

Penelusuran Senyawa Inhibitor Alpha-Glukosidase Potensial untuk Diabetes Melitus Tipe 2 melalui Pendekatan In Silico dengan Evaluasi Profil Adme Via SwissADME.

The aim of this study was to demonstrate data retrieval from online databases to find compounds with relevant properties such as alpha-glucosidase inhibitors, which have the potential to be used in the treatment of Type 2 Diabetes Mellitus. The methods used included compound search, physicochemical property analysis, and drug-likeness prediction using Lipinski\u27s Rule of Five as well as ADME profile evaluation using the SwissADME platform. Preliminary results showed that Miglitol and Quercetin have promising drug-likeness profiles, comply with Lipinski\u27s Rule of Five, and exhibit good ADME characteristics. Although Acarbose, as an effective drug, does not meet all Lipinski criteria due to its specific mechanism of action. The conclusion of this study confirms the efficiency of the in-silico approach in the initial screening of drug candidates but requires further experimental validation to confirm its activity and safety. Keywords: ADME, Alpha-Glucosidase Inhibitor, Drug-Likeness, In Silico, SwissADME, Type 2 Diabetes Mellitus

QSAR MODELING OF pKа VALUES OF SULFONYLUREA HERBICIDES

Sulfonylureas are herbicides primarily used for control of weeds in early growth stages of cultivations. Structurally sulfonylureas contain a sulfonyl group with sulphur atom bonded to nitrogen atom of an ureylene group. According side chains there are pyrimidinyl-sulfonylureas and the triazinyl-sulfonylureas. Swiss ADME descriptors have been used to develop QSAR models for predicting the pKa values of selected 27 sulfonlyurea herbicides: 17 pyrimidinyl- and 10 triazinyl-sulfonylurea herbicides. Variable selection methods including stepwise, forward, and best model were employed. Two different approaches were performed to develop a predictive QSAR model: a set with all selected herbicides and a divided set according structure (pyrimidinyl/ triazinyl). QSAR models were analyzed using following statistical parameters: coefficient of correlation, adjusted coefficient of correlation, mean squared error, root mean square error, and Fischer test. Models with four descriptors in both sets of herbicides were statistically better, based on the values of these parameters

Automating Pharmacokinetic Predictions in \u3cem\u3eArtemisia\u3c/em\u3e

Pharmacokinetics (PK) is the time course of a compound in the body that is dependent on mechanisms of absorption, distribution, metabolism, and excretion or ADME. A thorough understanding of PK is essential to predict the consequences of organisms exposed to chemicals. In medicine, predictions of PK of drugs allows us to properly prescribe drug treatments. In toxicology, PK allows us to predict the potential exposure of environmental contaminants and how they may affect organisms at the time of exposure or in the future. Chemical ecology could benefit from computational predictions of PK to better understand which plants are consumed or avoided by wild herbivores. A limitation in computational predictions of PK in chemical ecology is the large quantities of biodiverse natural products involved in complex plant-herbivore-microbial interactions compared to biomedical and environmental toxicology studies that focus on a select number of chemicals. The objective of this research was to automate the process of mining predicted PK of known chemical structures in plants consumed by herbivores and to use predicted PK output to test hypotheses. The first hypothesis is that because monoterpenes are smaller in molecular weight and have relatively high lipophilicity when compared to phenolics and sesquiterpenes, they would have higher absorption, be more likely to be substrates for efflux transporters that regulate absorption, and be more likely to inhibit metabolizing enzymes than phenolics and sesquiterpenes. The second hypothesis is that monoterpenes that are induced or avoided by foraging herbivores would have higher absorption, be less likely to be substates for efflux transporters, and be more likely to inhibit metabolizing enzymes compared to the individual monoterpenes that are not induced or avoided by herbivores. This automated approach used Python packages to obtain chemical notations from the PubChem website and mine predicted PK information for chemical input from the SwissADME website. The PK output from SwissADME was analyzed using ANOVAs to test for differences in molecular weight and lipophilicity among chemical classes (monoterpenes, phenolics, and sesquiterpenes). Chi-squared tests were used to assess if chemical groups had high or low absorption, were substrates of efflux transporters, or inhibited metabolizing enzymes. Mined PK data for chemicals can be used to understand drug-drug interactions in pharmacology, predict exposure to environmental contaminants in toxicology, and identify mechanisms mediating plant-microbe-herbivore interactions. However, the broad benefits of mining predicted PK across disciplines requires a workforce with competency in chemistry, physiology, and computing who can validate the automation process and test hypotheses relative to different disciplines. Course-based and Lab-based Undergraduate Research Experiences (CUREs and LUREs) have been proven to not only improve grades but also increase engagement diversity and inclusion. As a graduate teaching assistant, I created and taught a PK LURE module in an undergraduate Animal Physiology and Nutrition course to create a sustainable quality control step to validate input of chemical structures and PK output generated from the automated process. The course simultaneously provided students with an authentic research experience where they integrated chemistry, pharmacology, computing, public databases, and literature searches to propose and test new hypotheses. Students gained indispensable interdisciplinary research skills that can be transferred to jobs in veterinary and human medicine, pharmaceutics, and natural sciences. Moreover, undergraduates used existing and new PK data to generate and test novel hypotheses that go beyond the work of any single graduate student or discipline. Overall, the integration of computing and authentic research experiences has advanced the research capacity of a diverse workforce who can predict exposure and consequences of chemicals in organisms