Predicting ADME properties in silico: methods and models

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations

Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.

Molecular Docking and Absorption, Distribution, Metabolism, and Excretion (ADME) Analysis: Examining the Binding Modes and Affinities of Myricetin With Insulin Receptor, Glycogen Synthase Kinase, and Glucokinase

Aim By using molecular docking analysis (MDA) to examine its interactions with important regulatory proteins linked to diabetes, such as glycogen synthase kinase 3 beta (GSK3β), insulin receptor (IR), and glucose kinase (GCK), this study seeks to explore the therapeutic potential of myricetin, a naturally occurring flavonoid. Objective The main goal is to determine potential effects on insulin signalling, GSK3β activity, and glucose metabolism by evaluating the binding affinities of myricetin with GCK, IR, and GSK3β through MDA. In order to assess the drug affinity of myricetin, the study also intends to perform absorption, distribution, metabolism, and excretion (ADME) studies. Materials and methods To model the interaction between myricetin and the target proteins (GCK, IR, and GSK3β), we used molecular docking analysis with computational tools. ADME studies were also included in the study to evaluate drug affinity. Identification of binding sites, essential residues, and interaction stability were all part of the structural analysis. Results As evidence of possible interactions with these regulatory proteins, myricetin showed positive binding affinities with GCK, IR, and GSK3β. Strong interactions with important ligand recognition residues were seen in the docking into IR, indicating a potential impact on insulin signalling. Moreover, a strong binding affinity for GCK indicated potential effects on the metabolism of glucose. Studies using ADME confirmed the high drug affinity of myricetin. Conclusion This work sheds light on the multi-target potential of myricetin in the regulation of diabetes. It appears that it has the ability to influence glucose metabolism, suppress GSK3β activity, and regulate insulin signalling based on its interactions with IR, GSK3β, and GCK. Although these computational results show promise, more experimental work is necessary to confirm and fully understand the precise mechanisms that underlie myricetin's effects on the regulation of diabetes.

Effects of Acute and Repeated Dose Toxicity Profiling of Chelidonic Acid in Rats: in Silico and in Vivo Evidence

Chelidonic acid is a phytoconstituent found in rhizomes of the perennial plant celandine. The current study aims to evaluate the acute and repeated dose oral toxicity study of chelidonic acid as per the OECD guidelines 425 and 407. The pharmacokinetic and toxicity profile of chelidonic acid was predicted using online servers and tools. A single dose of chelidonic acid (2000 mg/kg) was administered to female Wistar rats in an acute toxicity study, and the animals were monitored for 14 days. We studied the toxicity profile of chelidonic acid at 10, 20, and 40 mg/kg doses in Wistar rats for repeated dose toxicity (28 days). Clinical biochemistry, haematological, and urine parameters were estimated. A gross necropsy and histopathology were performed. A single oral dose of chelidonic acid (2000 mg/kg) showed no signs of toxicity or mortality. The Administration of chelidonic acid showed no significant alterations in haematological, biochemical, and urine parameters. The histopathology showed normal structure and architecture in all the vital organs. A gross necropsy of vital organs showed no signs of toxicity. The chelidonic acid was found to be safe at all selected dose levels in the acute and repeated dose toxicity study in rats.

Assessing the multitargeted antidiabetic potential of three pomegranate peel-specific metabolites: An in silico and pharmacokinetics study

Diabetes is a chronic metabolic disorder that occurs due to impaired secretion of insulin, insulin resistance, or both. Recent studies show that the antidiabetic drugs used to control hyperglycemic levels are associated with undesirable adverse effects. Therefore, developing a safe and effective medicine with antidiabetic potential is needed. In this context, in silico studies are considered a rapid, effectual, and cost-effective method in drug discovery procedures. It is evident from the literature that plant-based natural components have shown promising outcomes in drug development to alleviate various diseases and hence have diversified the screening of potential antidiabetic agents. Purposely, in the present study, an in silico approach was performed on three Punica granatum peel metabolites (punicalin, punicalagin, and ellagic acid). All these three compounds were docked against nine protein targets involved in glucose metabolism (GFAT, PTP1β, PPAR-ᵞ, TKIR, RBP4, α-amylase, α-glucosidase, GCK, and AQP-2). These three pomegranate-specific compounds demonstrated significant interactions with GFAT, PTP1β, PPAR-ᵞ, TKIR, RBP4, α-amylase, α-glucosidase, GCK, and AQP-2 protein targets. Specifically, punicalin, punicalagin, and ellagic acid revealed significant binding scores (-9.2, -9.3, -8.1, -9.1, -8.5, -11.3, -9.2, -9.5, -10.1 kcal/mol; -10, -9.9, -8.5, -8.9, -10.4, -9.0, -10.2, -9.4, -9.0 kcal/mol; and -8.1, -8.0, -8.0, -6.8, -8.7, -7.8, -8.3, -8.1, -8.1 kcal/mol, respectively), with nine protein targets mentioned above. Hence, punicalin, punicalagin, and ellagic acid can be promising candidates in drug discovery to manage diabetes. Furthermore, in vivo and clinical trials must be conducted to validate the outcomes of the current study.

In silico ADME/tox comes of age: twenty years later

In the early 2000s pharmaceutical drug discovery was beginning to use computational approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction was an effort to reduce the risk of later stage failures from ADME/Tox.Much has been written in the intervening twenty plus years and significant expenditure has occurred in companies developing these in silico capabilities which can be gleaned from publications. It is therefore an appropriate time to briefly reflect on what was proposed then and what the reality is today.20 years ago, we tended to optimise bioactivity and perhaps one ADME/Tox property at a time. Previously pharmaceutical companies needed a whole infrastructure for models - in silico and in vitro experts, IT, champions on a project team, educators and management support. Now we are in the age of generative de novo design where bioactivity and many ADME/Tox properties can be optimised and large language model technologies are available.There are also some challenges such as the focus on very large molecules which may be outside of current ADME/Tox models.We provide an opportunity to look forward with the increasing public data for ADME/Tox as well as expanded types of algorithms available.

Revealing the Mechanisms of Byu dMar 25 in the Treatment of Alzheimer's Disease through Network Pharmacology, Molecular Docking, and In Vivo Experiment

BACKGROUND

Alzheimer's disease (AD) is the most common neurodegenerative disease, severely reducing the cognitive level and life quality of patients. Byu dMar 25 (BM25) has been proved to have a therapeutic effect on AD. However, the pharmacological mechanism is still unclear. Therefore, this study aims to reveal the potential mechanism of BM25 affecting AD from the perspective of network pharmacology and experimental validation.

METHODS

The potential active ingredients of BM25 were obtained from the TCMSP database and literature. Possible targets were predicted using SwissTargetPrediction tools. AD-related genes were identified by using GeneCards, OMIM, DisGeNET, and Drugbank databases. The candidate genes were obtained by extraction of the intersection network. Additionally, the "drug-target-disease" network was constructed by Cytoscape 3.7.2 for visualization. The PPI network was constructed by the STRING database, and the core network modules were filtered by Cytoscape 3.7.2. Enrichment analysis of GO and KEGG was carried out in the Metascape platform. Ledock software was used to dock the critical components with the core target. Furthermore, protein levels were evaluated by immunohistochemistry.

RESULTS

In this study, 112 active components, 1112 disease candidate genes, 3084 GO functions, and 277 KEGG pathways were obtained. Molecular docking showed that the effective components of BM25 in treating AD were β-asarone and hydroxysafflor yellow A. The most important targets were APP, PIK3R1, and PIK3CA. Enrichment analysis indicated that the Golgi genetic regulation, peroxidase activity regulation, phosphatidylinositol 3-kinase complex IA, 5-hydroxytryptamine receptor complexes, cancer pathways, and neuroactive ligand-receptor interactions played vital roles against AD. The rat experiment verified that BM25 affected PI3K-Akt pathway activation in AD.

CONCLUSIONS

This study reveals the mechanism of BM25 in treating AD with network pharmacology, which provides a foundation for further study on the molecular mechanism of AD treatment.

Leveraging Cell Painting Images to Expand the Applicability Domain and Actively Improve Deep Learning Quantitative Structure-Activity Relationship Models

The search for chemical hit material is a lengthy and increasingly expensive drug discovery process. To improve it, ligand-based quantitative structure-activity relationship models have been broadly applied to optimize primary and secondary compound properties. Although these models can be deployed as early as the stage of molecule design, they have a limited applicability domain─if the structures of interest differ substantially from the chemical space on which the model was trained, a reliable prediction will not be possible. Image-informed ligand-based models partly solve this shortcoming by focusing on the phenotype of a cell caused by small molecules, rather than on their structure. While this enables chemical diversity expansion, it limits the application to compounds physically available and imaged. Here, we employ an active learning approach to capitalize on both of these methods' strengths and boost the model performance of a mitochondrial toxicity assay (Glu/Gal). Specifically, we used a phenotypic Cell Painting screen to build a chemistry-independent model and adopted the results as the main factor in selecting compounds for experimental testing. With the additional Glu/Gal annotation for selected compounds we were able to dramatically improve the chemistry-informed ligand-based model with respect to the increased recognition of compounds from a 10% broader chemical space.

Molecular recognition of bio-active triterpenoids from Swertia chirayita towards hepatitis Delta antigen: a mechanism through docking, dynamics simulation, Gibbs free energy landscape

Medicinal plants the underpinning of indigenous herbal serve, are the possible source of key compounds for the development of new drugs. Hepatitis D, one of the most widespread infectious diseases associated with global public health issues. Therefore, we aim to screen natural compounds to find out potent inhibitor towards hepatitis delta antigen. Through ADMET investigation, we have screened twenty phytochemicals for this study. Additionally, using molecular docking, these phytochemicals were docked with the HDV protease which signifies the phytochemicals beta-amyrin, chiratenol, episwertenol and swertanone have a significant capability to bind with hepatitis D virus protein. The docking study was further accompanied by analyzes RMSD, RMSF, Rg, SASA, Hbond number, and principal component analysis through 100 ns MD simulations. Based on our principal component analysis, beta-amyrin, chiratenol, episwertenol and swertanone phytochemicals can be a potential drug candidates for inhibition of hepatitis D. The above observation is also supported by our Gibbs free energy landscape study. The potential therapeutic characteristics of the phytochemicals against hepatitis D inhibition offer additional support for the in vitro and in vivo studies in future.

Identification of probable inhibitors for the DNA polymerase of the Monkeypox virus through the virtual screening approach

Given the paucity of antiviral treatments for monkeypox disease, caused by the Monkeypox virus (MPXV), there is a pressing need for the development/identification of new drugs to treat the infection. MPXV possesses a linear dsDNA genome that is replicated by a DNA replication complex of which DNA polymerase (DPol) forms an important component. Owing to the importance of DPol in the viral life cycle, identifying/designing small molecules abolishing its function could yield new antivirals. In this study, we first used the AlphaFold artificial intelligence program to model the 3D structure of the MPXV DPol; like the fold of DPol from other organisms, the MPXV DPol structure has the characteristic exonuclease, thumb, palm, and fingers sub-domains arrangement. Subsequently, we have identified several inhibitors through virtual screening of ZINC and antiviral libraries. Molecules with phenyl scaffold along with alanine-based and tetrazole-based molecules showed the best docking score of -8 to -10 kcal/mol. These molecules bind in the palm and fingers sub-domains interface region, which partially overlaps with the DNA binding path. The delineation of DPol/inhibitor interactions showed that majorly active site residues ASP549, ASP753, TYR550, ASN551, SER552, and ASN665 interact with the inhibitors. These compounds exhibit good Absorption, Distribution, Metabolism and Excretion properties.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Identification of potential phytochemicals from Citrus Limon against main protease of SARS-CoV-2: molecular docking, molecular dynamic simulations and quantum computations

The outbreak of coronavirus disease (COVID-19) caused by a novel RNA virus emerged at the end of 2019. Most of the patient's symptoms are mild to moderate, and influenza, acute respiratory distress syndrome (ARDS) and multi-organ failure are common. The disease is mild to moderate in most patients and is reported in many cases such as pneumonia, ARDS and multi-organ dysfunction. This study's objective is to evaluate 25 natural compounds from Citrus limon (CL) used by comprehensive molecular docking, density functional theory (DFT) and molecular dynamics analysis against SARS-CoV-2 main protease (M^pro). Among all the experimental compounds, diosmetin has shown the best docking values against the M^pro of SARS-CoV-2 compared to the standard antiviral drug. In DFT calculations, the order associated with biochemical reactivity is as follows: eriodictoyl > quercetin > spinacetin > diosmetin > luteolin > apigenin, whereas the regions of oxygen and hydrogen atoms from the selected isolated compounds are appropriate for electrophilic and nucleophilic attacks, respectively. Also, HOMO-LUMO and global descriptors values indicated a promising result of these compounds. Moreover, a molecular dynamics simulation study revealed the stable conformation and binding pattern in a stimulating environment of natural compounds CL. Considering molecular docking, simulation, and DFT analysis of the selected compounds, notably eriodictoyl, quercetin, and diosmetin showed good potential against SARS-CoV-2 M^pro. Our in silico study revealed promising antiviral activity, which may be considered a potential key factor or a therapeutic target for COVID-19.Communicated by Ramaswamy H. Sarma.

Heterogeneous network propagation with forward similarity integration to enhance drug-target association prediction

Identification of drug-target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug-drug and nine target-target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug-disease associations, and the cosine scores of drug-drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs.

Identification of Novel Inhibitors Targeting SGK1 via Ensemble-Based Virtual Screening Method, Biological Evaluation and Molecular Dynamics Simulation

Serum and glucocorticoid-regulated kinase 1 (SGK1), as a serine threonine protein kinase of the AGC family, regulates different enzymes, transcription factors, ion channels, transporters, and cell proliferation and apoptosis. Inhibition of SGK1 is considered as a valuable approach for the treatment of various metabolic diseases. In this investigation, virtual screening methods, including pharmacophore models, Bayesian classifiers, and molecular docking, were combined to discover novel inhibitors of SGK1 from the database with 29,158 compounds. Then, the screened compounds were subjected to ADME/T, PAINS and drug-likeness analysis. Finally, 28 compounds with potential inhibition activity against SGK1 were selected for biological evaluation. The kinase inhibition activity test revealed that among these 28 hits, hit15 exhibited the highest inhibition activity against SGK1, which gave 44.79% inhibition rate at the concentration of 10 µM. In order to further investigate the interaction mechanism of hit15 and SGK1 at simulated physiological conditions, a molecular dynamics simulation was performed. The molecular dynamics simulation demonstrated that hit15 could bind to the active site of SGK1 and form stable interactions with key residues, such as Tyr178, ILE179, and VAL112. The binding free energy of the SGK1-hit15 was −48.90 kJ mol−1. Therefore, the identified hit15 with novel scaffold may be a promising lead compound for development of new SGK1 inhibitors for various diseases treatment.

In-silico study for the screening and preparation of ionic liquid-AVDs conjugate to combat COVID-19 surge

The pandemic due to COVID-19 caused by SARS-CoV-2 has led to the recorded deaths worldwide and is still a matter of concern for scientists to find an effective counteragent. The combination therapy is always been a successful attempt in treating various threatful diseases. Recently, Ionic liquids (ILs) are known for their antiviral activity. Fascinating tunable properties of ILs make them a potential candidate for designing the therapeutic agent. The concern while using ILs in biomedical field remains is toxicity therefore, choline-based ILs were used in the study as they are considered to be greener as compared to other ILs. In the present study strategically, we performed the blind molecular docking of antiviral drug (Abacavir, Acyclovir, and Galidesivir)-choline based ILs conjugates with the target protein (M_pro protease). The molecules were screened on the basis of binding energy. The data suggested that the combination of AVDs-ILs have greater antiviral potential as compared to the drugs and ILs alone. Further, the ADME properties and toxicity analysis of the screened conjugates was done which revealed the non-toxicity of the conjugates. Additionally, the energetic profiling of the ILs drugs and their conjugates was done using DFT calculations which revealed the stability of the conjugates and have a better option to be developed as a therapeutic agent. Also, from molecular dynamic simulation was done and results showed the stability of the complex formed between target protein and the designed conjugates of AVDs and ILs.

DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method

Drug–target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.

Systems Drug Discovery for Diffuse Large B Cell Lymphoma Based on Pathogenic Molecular Mechanism via Big Data Mining and Deep Learning Method

Diffuse large B cell lymphoma (DLBCL) is an aggressive heterogeneous disease. The most common subtypes of DLBCL include germinal center b-cell (GCB) type and activated b-cell (ABC) type. To learn more about the pathogenesis of two DLBCL subtypes (i.e., DLBCL ABC and DLBCL GCB), we firstly construct a candidate genome-wide genetic and epigenetic network (GWGEN) by big database mining. With the help of two DLBCL subtypes’ genome-wide microarray data, we identify their real GWGENs via system identification and model order selection approaches. Afterword, the core GWGENs of two DLBCL subtypes could be extracted from real GWGENs by principal network projection (PNP) method. By comparing core signaling pathways and investigating pathogenic mechanisms, we are able to identify pathogenic biomarkers as drug targets for DLBCL ABC and DLBCL GCD, respectively. Furthermore, we do drug discovery considering drug-target interaction ability, drug regulation ability, and drug toxicity. Among them, a deep neural network (DNN)-based drug-target interaction (DTI) model is trained in advance to predict potential drug candidates holding higher probability to interact with identified biomarkers. Consequently, two drug combinations are proposed to alleviate DLBCL ABC and DLBCL GCB, respectively.

3,5-dinitrobenzoylhydrazone derivatives as a scaffold for antituberculosis drug development

Background: The development of drugs is essential to eradicate tuberculosis. Materials & methods: Sixteen 3,5-dinitrobenzoylhydrazone (2-17) derivatives and their synthetic precursors 3,5-dinitrobenzoylhydrazide (1) and methyl ester (18) were screened for their anti-Mycobacterium tuberculosis (Mtb) potential. Results: Twelve compounds had minimum inhibitory concentration (MIC) ranging from 0.24 to 7.8 μg/ml against the Mtb strain. The activity was maintained in multidrug-resistant Mtb clinical isolates. Only compound (17) showed activity against nontuberculous mycobacteria. The compounds exhibited a limited spectrum of activity, with an MIC >500 μg/ml against Gram-positive and -negative bacteria. Compounds (2), (5) and (11) showed a synergistic effect with rifampicin. An excellent selectivity index value was found, with values reaching 583.33. Conclusion: 3,5-dinitrobenzoylhydrazone derivatives could be considered as a scaffold for the development of antituberculosis drugs.

PBPK Modeling to Simulate the Fate of Compounds in Living Organisms

Pharmacokinetics study the fate of xenobiotics in a living organism. Physiologically based pharmacokinetic (PBPK) models provide realistic descriptions of xenobiotics' absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mechanistic framework to understand and simulate the time-course of the concentration of a substance in various organs and body fluids. These models are well suited for performing extrapolations inherent to toxicology and pharmacology (e.g., between species or doses) and for integrating data obtained from various sources (e.g., in vitro or in vivo experiments, structure-activity models). In this chapter, we describe the practical development and basic use of a PBPK model from model building to model simulations, through implementation with an easily accessible free software.

Discovery of thiazolidin-4-one analogue as selective GSK-3β inhibitor through structure based virtual screening

GSK-3β directly phosphorylate tubulin binding site of tau protein, indicating its importance in tau aggregation and, therefore, in Alzheimer's disease pathology. New GSK-3β inhibitors were identified using a structure-based screening, ADMET analysis. These studies revealed that ZINC09036109, ZINC72371723, ZINC72371725, and ZINC01373165 approached optimal ADMET properties along with good MM-GBSA dG binding. Protein kinase assays of these compounds against eight disease-relevant kinases were performed. During disease-relevant kinase profiling, ZINC09036109 ((E)-2-((3,4-dimethylphenyl)imino)-5-(3-methoxy-4-(naphthalen-2-ylmethoxy)benzyl)thiazolidin-4-one) emerged as a selective GSK-3β inhibitor with more than 10-fold selectivity over other disease-relevant kinases. Molecular dynamics study of ZINC09036109 molecule revealed interactions with Ile62, Phe67, Val135, Leu188, Asp200 amino acid residues of the binding site of GSK-3β, which were highly comparable to the co-crystallized molecule and hence validating comparative better activity of this compound compared to overall screened molecules.

Spectral, NBO, NLO, NCI, aromaticity and charge transfer analyses of anthracene-9,10-dicarboxaldehyde by DFT

Anthracene-9,10-dicarboxaldehyde (ADCA) is a polynuclear aromatic compound that has a planar structure with double bonds which are in conjugation. The molecule is subjected to theoretical investigation with DFT/B3LYP/6-311++G(d,p) basis set to find out the electronic structural properties and stability. Theoretical and experimental vibrational analyses are carried out. NBO studies predict that the molecule has high stability. NCI interaction studies reveal that Van der Waals force and steric effect are seen in the molecule. A shaded surface map with a projection of LOL analysis pointed out that electron depletion area is seen in this molecule. The tunnelling current is more in the boundary rings than the central ring. It is docked with the protein 4COF and the binding energy is found to be -6.6 kcal/mol. Electrons excitation analysis is performed and found that local excitation takes place for the lowest five excitations. The aromaticity of the molecule is also thoroughly investigated.

Novel Anti-Tubulin Compounds from Trigonella foenum-graecum Seeds; Insights into In-vitro and Molecular Docking Studies

Background

Fenugreek, also known as Trigonella foenum-graecum L, is a natural plant that belongs to the Fabaceae family and has been known as a promising source of bioactive compounds. It has been widely used as traditional medicine since it has shown to lower blood glucose, manage cholesterol levels and further aid in the prevention and treatment of cancer. Herein, we aim to evaluate the anticancer activity of methanolic fenugreek seed extract against several cancer cell lines.

Methods

We sought to investigate the phytochemical classes present in multiple fenugreek seeds extracts using HPLC-DAD followed by LC/MS, predict and investigate anticancer activity using PASS online webserver, the CellTiter-Glo assay, evaluate ADME properties, and perform molecular docking for all bioactive compounds via Maestro software.

Results

Multiple extracts exhibited distinct phytochemical classes that demonstrated different biological activities. Fenugreek methanolic extract contains flavonoid chemical class, which showed the highest anticancer activity against the HCT8 cell line of colorectal cancer (IC₅₀ of 8.83 μg/mL), followed by KAIMRC1 breast cancer cell line (IC₅₀ of 35.06 μg/mL), HL60 leukemia cell line (37.80 μg/mL), MDA-MB-231 breast cancer cell line (38.51 μg/mL), and lastly, HCT116 colorectal cancer cell line with IC₅₀ of 56.03 μg/mL. In contrast, the chloroform extract was inactive. The molecular docking study for all the bioactive compounds suggested that flavonoids F6 (−9.713 and −12.132), F7 (−10.166 and −12.411), and F11 (−10.084 and −13.516) possess the highest docking scores through SP and XP scores, respectively.

Conclusion

The obtained results confirm that the bioactive compounds present in fenugreek seeds exhibit anticancer activity against several cancer cells that can mediate via tubulin polymerization inhibition. Although our study has evaluated the anticancer potential of Trigonella foenum-graecum as a promising natural source for new anticancer agents, fenugreek biological activity needs further research and investigations on their mechanism of action and toxicity profile.

Recent Advances in the Prediction of Pharmacokinetics Properties in Drug Design Studies: A Review

This review presents the main aspects related to pharmacokinetic properties, which are essential for the efficacy and safety of drugs. This topic is very important because the analysis of pharmacokinetic aspects in the initial design stages of drug candidates can increase the chances of success for the entire process. In this scenario, experimental and in silico techniques have been widely used. Due to the difficulties encountered with the use of some experimental tests to determine pharmacokinetic properties, several in silico tools have been developed and have shown promising results. Therefore, in this review, we address the main free tools/servers that have been used in this area, as well as some cases of application. Finally, we present some studies that employ a multidisciplinary approach with synergy between in silico, in vitro, and in vivo techniques to assess ADME properties of bioactive substances, achieving successful results in drug discovery and design.

Anti-COVID-19 activity of some benzofused 1,2,3-triazolesulfonamide hybrids using in silico and in vitro analyses

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pandemic fatal infection with no known treatment. The severity of the disease and the fast viral mutations forced the scientific community to search for potential solution. Here in the present manuscript, some benzofused1,2,3triazolesulfonamide hybrids were synthesized and evaluated for their anti- SARS-CoV-2 activity using in silico prediction then the most potent compounds were assessed using in-Vitro analysis. The in-Silico study was assessed against RNA dependent RNA polymerase, Spike protein S1, Main protease (3CLpro) and 2'-O-methyltransferase (nsp16). It was found that 4b and 4c showed high binding scores against RNA dependent RNA polymerase reached -8.40 and -8.75 ​kcal/mol, respectively compared to the approved antiviral (remdesivir -6.77 ​kcal/mol). Upon testing the binding score with SARS-CoV-2 Spike protein it was revealed that 4c exhibited the highest score (-7.22 ​kcal/mol) compared to the reference antibacterial drug Ceftazidime (-6.36 ​kcal/mol). Surprisingly, the two compounds 4b and 4c showed the highest binding scores against SARS-CoV-2 3CLpro (-8.75, -8.48 ​kcal/mol, respectively) and nsp16 (- 8.84 and - 8.89 ​kcal/mol, respectively) displaying many types of interaction with all the enzymes binding sites. The derivatives 4b and 4c were examined in vitro for their potential anti-SARS-CoV-2 and it was revealed that 4c was the most promising compound with IC50 reached 758.8108 ​mM and complete (100%) inhibition of the binding of SARS-CoV-2 virus to human ACE2 can be accomplished by using 0.01 ​mg.

Permeability Data of Organosulfur Garlic Compounds Estimated by Immobilized Artificial Membrane Chromatography: Correlation Across Several Biological Barriers

Among healthy vegetables, those of the genus Allium stand out. Antioxidant and anti-inflammatory properties have been associated with these vegetables, attributed mainly to organosulfur compounds (OSCs). In turn, they are linked to a protective effect counteracting cardiovascular disease development. Now, to really ensure the bioactive efficacy of the said compounds once consumed, it is necessary to previously evaluate the ADME (absorption, distribution, metabolism, and excretion) profile. Alternatively, in vitro and in silico methods attempt to avoid or reduce experimental animals' use and provide preliminary information on drugs' ability to overcome the various biological barriers inherent in the ADME process. In this sense, in silico methods serve to provide primary information on drugs' bioavailability mechanisms. High-performance liquid chromatography (HPLC) using a stationary phase composed of phospholipids, the so-called immobilized artificial membrane (IAM), has been widely recognized as a valuable alternative method to extract and quantify information about the structure and physicochemical properties of organic compounds which are extensively used in studies of quantitative structure-activity relationships (QSARs). In the present study, the chromatographic capacity factors (log k' (IAM)) for 28 OSCs were determined by IAM-HPLC. In order to evaluate the ability of the IAM phase in assessing lipophilicity of the compounds under study, several quantitative structure-retention relationships (QSRRs) were derived from exploring fundamental intermolecular interactions that govern the retention of compounds under study on IAM phases. As expected, the hydrophobic factors are of prime importance for the IAM retention of these compounds. However, the molecular flexibility and specific polar interactions expressed by several electronic descriptors (relative negative charge, RNCG, and Mulliken electronegativity) are also involved. We also evaluated the IAM phase ability to assess several ADME parameters for the OSCs under study obtained using the SwissADME web tool integrated into the SwissDrugDesign workspace and the PreADMET web tool. The human gastrointestinal absorption (HIA), blood-brain barrier (BBB) permeation, and skin permeability were investigated through QSAR modeling, using several chemometric approaches. The ADME properties under study are strongly dependent on hydrophobic factors as expressed by log k'(IAM), which provide evidence for the great potential of the IAM phases in the development of QSAR models.

Characterization of the NiRAN domain from RNA-dependent RNA polymerase provides insights into a potential therapeutic target against SARS-CoV-2

Apart from the canonical fingers, palm and thumb domains, the RNA dependent RNA polymerases (RdRp) from the viral order Nidovirales possess two additional domains. Of these, the function of the Nidovirus RdRp associated nucleotidyl transferase domain (NiRAN) remains unanswered. The elucidation of the 3D structure of RdRp from the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), provided the first ever insights into the domain organisation and possible functional characteristics of the NiRAN domain. Using in silico tools, we predict that the NiRAN domain assumes a kinase or phosphotransferase like fold and binds nucleoside triphosphates at its proposed active site. Additionally, using molecular docking we have predicted the binding of three widely used kinase inhibitors and five well characterized anti-microbial compounds at the NiRAN domain active site along with their drug-likeliness. For the first time ever, using basic biochemical tools, this study shows the presence of a kinase like activity exhibited by the SARS-CoV-2 RdRp. Interestingly, a well-known kinase inhibitor- Sorafenib showed a significant inhibition and dampened viral load in SARS-CoV-2 infected cells. In line with the current global COVID-19 pandemic urgency and the emergence of newer strains with significantly higher infectivity, this study provides a new anti-SARS-CoV-2 drug target and potential lead compounds for drug repurposing against SARS-CoV-2.

The on-going coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is significantly affecting the world health. Unfortunately, over 180 million cases of COVID-19 resulting in nearly 4 million deaths have been reported till June, 2021. In this study, using a combination of bioinformatics, biochemical and mass spectrometry methods, we show that the Nidovirus RdRp associated Nucleotidyl transferase (NiRAN) domain of the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 exhibits a kinase like activity. Additionally, we also show that few broad spectrum anti-cancer and anti-microbial drugs dampen this kinase like activity. Of note, Sorafenib, an FDA approved anti-cancer kinase inhibiting drug significantly reduces the SARS-CoV-2 load in cell lines. Our study suggests that NiRAN domain of the SARS-CoV-2 RdRp is indispensible for the successful viral life cycle and shows that abolishing this enzymatic function of RdRp by small molecule inhibitors may open novel avenues for COVID-19 therapeutics.

Molecular docking analysis of pyruvate kinase M2 with a potential inhibitor from the ZINC database

The pyruvate kinase M2 isoform (PKM2) is linked with cancer. Therefore, it is of interest to document the molecular docking analysis of Pyruvate Kinase M2 (PDB ID: 4G1N) with potential activators from the ZINC database. Thus, we document the optimal molecular docking features of a compound having ID ZINC000034285235 with PKM2 for further consideration.

Virtual screening and dynamics of potential inhibitors targeting RNA binding domain of nucleocapsid phosphoprotein from SARS-CoV-2

The emergence of the coronavirus disease-2019 pandemic has led to an outbreak in the world. The SARS-CoV-2 is seventh and latest in coronavirus family with unique exonucleases for repairing any mismatches in newly transcribed genetic material. Therefore, drugs with novel additional mechanisms are required to simultaneously target and eliminate the virus. Thus, a newly deciphered N protein is taken as a target that belongs to SARS-CoV-2. They play a vital role in RNA transcription, viral replication and new virion formation. This study used virtual screening, molecular modeling and docking of the 8987 ligands from Asinex and PubChem databases against this novel target protein. Three hotspot sites having DScore ≥1 (Site 1, Site 2 and Site 3) for ligand binding were selected. Subsequently, high throughput screening, standard precision and extra precision docking process and molecular dynamics concluded three best drugs from two libraries. Two antiviral moieties from Asinex databases (5817 and 6799) have docking scores of -10.29 and -10.156; along with their respective free binding energies (ΔG bind) of -51.96 and -64.36 on Site 3. The third drug, Zidovudine, is from PubChem database with docking scores of -9.75 with its binding free energies (ΔG bind) of -59.43 on Site 3. The RMSD and RMSF were calculated for all the three drugs through molecular dynamics simulation studies for 50 ns. Zidovudine shows a very stable interaction with fluctuation starting at 2.4 Å on 2 ns and remained stable at 3 Å from 13 to 50 ns. Thus, paving the way for further biological validation as a potential treatment.Communicated by Ramaswamy H. Sarma.

Gram-Scale Synthesis of 1,8-Naphthyridines in Water: The Friedlander Reaction Revisited

The products of the Friedlander reaction, i.e., 1,8-naphthyridines, have far-reaching impacts in materials science, chemical biology, and medicine. The reported synthetic methodologies elegantly orchestrate the diverse synthetic routes of naphthyridines but require harsh reaction conditions, organic solvents, and expensive metal catalysts. Here, we introduce gram-scale synthesis of 1,8-naphthyridines in water using an inexpensive and biocompatible ionic liquid (IL) as a catalyst. This is the first-ever report on the synthesis of naphthyridines in water. This is a one-step reaction, and the product separation is relatively easy. The choline hydroxide (ChOH) is used as a metal-free, nontoxic, and water-soluble catalyst. In comparison to other catalysts reported in the literature, ChOH has the advantage of forming an additional hydrogen bond with the reactants, which is the vital step for the reaction to happen in water. Density functional theory (DFT) and noncovalent interaction (NCI) plot index analysis provide the plausible reaction mechanism for the catalytic cycle and confirm that hydrogen bonds with the IL catalyst are pivotal to facilitate the reaction. Molecular docking and molecular dynamics (MD) simulations are also performed to demonstrate the potentialities of the newly synthesized products as drugs. Through MD simulations, it was established that the tetrahydropyrido derivative of naphthyridine (10j) binds to the active sites of the ts3 human serotonin transporter (hSERT) (PDB ID: 6AWO) without perturbing the secondary structure, suggesting that 10j can be a potential preclinical drug candidate for hSERT inhibition and depression treatment.

In silico studies: Physicochemical properties, drug score, toxicity predictions and molecular docking of organosulphur compounds against Diabetes mellitus

Diabetes mellitus (DM) is a significant common metabolic disorder seen all over the world. In 2020, according to the International Diabetes Federation (IDF), out of 463 million people who have diabetes all over the world, 77 million belong to India. As per the statistical prediction, the affected numbers are probably expected to rise to 642 million by 2040. The commercially available anti-diabetic drugs in the market include metformin, sulphonyl urea, meglitinides, miglitol, acarbose, biguanides, and thiazolidinediones cause side effects like hypoglycaemia, dizziness, liver cell injury, digestive discomfort, neurological defects, etc. Hence, bioactive organosulphur based functional ligands are chosen in this study to arrive at a newer drug for DM. In this work, in silico analysis of organosulphur molecular descriptors like physicochemical properties, solubility, drug score, and toxicity predictions are evaluated using OSIRIS and Toxtree freeware. The essential parameters for discovering drugs for biopharmaceutical formulations viz the solubility of drugs and toxicity have been calculated. The protein target Dipeptidyl peptidase DPP4 (PID: 2RIP) was docked against energy minimised sulphur compounds using Hex 6.3. The results indicate that the drug likeliness of the molecule 4, that is, N-[(3,3-dimethyl piperidin-2-yl) methyl]-4-ethyl sulphonyl aniline is active with decreasing binding energy score (-212.24 Kcal mol^-1 ) with no toxicity and also few sulphur compounds are active against diabetes compared to standard drug metformin (-158.33 Kcal mol^-1 ). The best drug-like ligand N-[(3,3-dimethyl piperidin-2-yl) methyl]-4-ethyl sulphonyl aniline, was docked using commercial Maestro Schrodinger software to predict the results.

A Convolutional Neural Network System to Discriminate Drug-Target Interactions

Biological targets are most commonly proteins such as enzymes, ion channels, and receptors. They are anything within a living organism to bind with some other entities (like an endogenous ligand or a drug), resulting in change in their behaviors or functions. Exploring potential drug-target interactions (DTIs) are crucial for drug discovery and effective drug development. Computational methods were widely applied in drug-target interactions, since experimental methods are extremely time-consuming and resource-intensive. In this paper, we proposed a novel deep learning-based prediction system, with a new negative instance generation, to identify DTIs. As a result, our method achieved an accuracy of 0.9800 on our created dataset. Another dataset derived from DrugBank was used to further assess the generalization of the model, which yielded a good performance with accuracy of 0.8814 and AUC value of 0.9527 on the dataset. The outcome of our experimental results indicated that the proposed method, involving the credible negative generation, can be employed to discriminate the interactions between drugs and targets. Website: http://www.dlearningapp.com/web/DrugCNN.htm.

QSAR, molecular docking and ADMET properties in silico studies of novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2-Yl derivatives derived from dimedone as potent anti-tumor agents through inhibition of C-Met receptor tyrosine kinase

A quantitative structure-activity relationship (QSAR) study is performed on 48 novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2 derivatives as anticancer agents capable of inhibiting c-Met receptor tyrosine kinase. The present study is conducted using multiple linear regression, multiple nonlinear regression and artificial neural networks. Three QSAR models are developed after partitioning the database into two sets (training and test) via the k-means method. The obtained values of the correlation coefficients by the three developed QSAR models are 0.90, 0.91 and 0.92, respectively. The resulting models are validated by using the external validation, leave-one-out cross-validation, Y-randomization test, and applicability domain methods. Moreover, we evaluated the drug-likeness properties of seven selected molecules based on their observed high activity to inhibit the c-Met receptor. The results of the evaluation showed that three of the seven compounds present drug-like characteristics. In order to identify the important active sites for the inhibition of the c-Met receptor responsible for the development of cancer cell lines, the crystallized form of the Crizotinib-c-Met complex (PDB code: 2WGJ) is used. These sites are used as references in the molecular docking test of the three selected molecules to identify the most suitable molecule for use as a new c-Met inhibitor. A comparative study is conducted based on the evaluation of the predicted properties of ADMET in silico between the candidate molecule and the Crizotinib inhibitor. The comparison results show that the selected molecule can be used as new anticancer drug candidates.

In Vitro and In Silico Evaluations of Anti-Mycobacterium tuberculosis Activity of Benzohydrazones Compounds

Tuberculosis is a disease caused by Mycobacterium tuberculosis, with high mortality rates and an extended treatment that causes severe adverse effects, besides the emergence of resistant bacteria. Therefore, the search for new compounds with anti-M. tuberculosis activity has considerably increased in recent years. In this context, benzohydrazones are significant compounds that have antifungal and antibacterial action. This study aimed at evaluating the in vitro activity of 18 benzohydrazones against M. tuberculosis. Compounds' cytotoxicity, inhibition of M. tuberculosis efflux pumps, and in silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) assays were also performed. In general, the minimum inhibitory concentration values for the standard M. tuberculosis H₃₇Rv strain ranged from 7.8 to 250 μg/mL, and some compounds were not toxic to any of the cells tested (IC₅₀ ranged from 18.0 to 302.5 μg/mL). In addition, compounds (4) and (7) showed to be possible efflux pump inhibitors. In ADMET assays, all benzohydrazones had high gastrointestinal absorption. Most of the compounds were able to overcome the blood-brain barrier, and no compounds had irritant or tumorigenic effects. Compounds (1), (3), (9), (12), and (15) stood out for showing good activities, both in vitro and in silico assays.

In vivo and in vitro anti-inflammatory, antipyretic and ulcerogenic activities of pyridone and chromenopyridone derivatives, physicochemical and pharmacokinetic studies

Throughout this study, we present the victorious synthesis of a novel class of 2(1H)-pyridone molecules, bearing a 4-hydroxyphenyl moiety through a one-pot reaction of 2-cyano-N-(4-hydroxyphenyl)acetamide with cyanoacetamide, acetylacetone or ethyl acetoacetate, and their corresponding aldehydes. In addition, the chromene moiety was introduced into the pyridine skeleton through the cyclization of the cyanoacetamide 2 with salicylaldehyde, followed by treatment with malononitrile, ethyl cyanoacetate, and cyanoacetamide, in order to improve their biological behaviour. Due to their anti-inflammatory, ulcerogenic, and antipyretic characters, the target molecules have undergone in-vitro and in-vivo examination, that display promising results. Moreover, in order to predict the physicochemical and ADME traits of all synthesized compounds and standard reference drugs, paracetamol and phenylbutazone, the in-silico prediction methodology was provided.

A Revisit to the Research Updates of Drugs, Vaccines, and Bioinformatics Approaches in Combating COVID-19 Pandemic

A new strain of coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for the coronavirus disease 2019 (COVID-19) pandemic was first detected in the city of Wuhan in Hubei province, China in late December 2019. To date, more than 1 million deaths and nearly 57 million confirmed cases have been recorded across 220 countries due to COVID-19, which is the greatest threat to global public health in our time. Although SARS-CoV-2 is genetically similar to other coronaviruses, i.e., SARS and Middle East respiratory syndrome coronavirus (MERS-CoV), no confirmed therapeutics are yet available against COVID-19, and governments, scientists, and pharmaceutical companies worldwide are working together in search for effective drugs and vaccines. Repurposing of relevant therapies, developing vaccines, and using bioinformatics to identify potential drug targets are strongly in focus to combat COVID-19. This review deals with the pathogenesis of COVID-19 and its clinical symptoms in humans including the most recent updates on candidate drugs and vaccines. Potential drugs (remdesivir, hydroxychloroquine, azithromycin, dexamethasone) and vaccines [mRNA-1273; measles, mumps and rubella (MMR), bacille Calmette-Guérin (BCG)] in human clinical trials are discussed with their composition, dosage, mode of action, and possible release dates according to the trial register of US National Library of Medicines (clinicaltrials.gov), European Union (clinicaltrialsregister.eu), and Chinese Clinical Trial Registry (chictr.org.cn) website. Moreover, recent reports on in silico approaches like molecular docking, molecular dynamics simulations, network-based identification, and homology modeling are included, toward repurposing strategies for the use of already approved drugs against newly emerged pathogens. Limitations of effectiveness, side effects, and safety issues of each approach are also highlighted. This review should be useful for the researchers working to find out an effective strategy for defeating SARS-CoV-2.

An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features

Background

Prediction of novel Drug–Target interactions (DTIs) plays an important role in discovering new drug candidates and finding new proteins to target. In consideration of the time-consuming and expensive of experimental methods. Therefore, it is a challenging task that how to develop efficient computational approaches for the accurate predicting potential associations between drug and target.

Results

In the paper, we proposed a novel computational method called WELM-SURF based on drug fingerprints and protein evolutionary information for identifying DTIs. More specifically, for exploiting protein sequence feature, Position Specific Scoring Matrix (PSSM) is applied to capturing protein evolutionary information and Speed up robot features (SURF) is employed to extract sequence key feature from PSSM. For drug fingerprints, the chemical structure of molecular substructure fingerprints was used to represent drug as feature vector. Take account of the advantage that the Weighted Extreme Learning Machine (WELM) has short training time, good generalization ability, and most importantly ability to efficiently execute classification by optimizing the loss function of weight matrix. Therefore, the WELM classifier is used to carry out classification based on extracted features for predicting DTIs. The performance of the WELM-SURF model was evaluated by experimental validations on enzyme, ion channel, GPCRs and nuclear receptor datasets by using fivefold cross-validation test. The WELM-SURF obtained average accuracies of 93.54, 90.58, 85.43 and 77.45% on enzyme, ion channels, GPCRs and nuclear receptor dataset respectively. We also compared our performance with the Extreme Learning Machine (ELM), the state-of-the-art Support Vector Machine (SVM) on enzyme and ion channels dataset and other exiting methods on four datasets. By comparing with experimental results, the performance of WELM-SURF is significantly better than that of ELM, SVM and other previous methods in the domain.

Conclusion

The results demonstrated that the proposed WELM-SURF model is competent for predicting DTIs with high accuracy and robustness. It is anticipated that the WELM-SURF method is a useful computational tool to facilitate widely bioinformatics studies related to DTIs prediction.

Applications of Machine Learning in Drug Target Discovery

Drug target discovery is a critical step in drug development. It is the basis of modern drug development because it determines the target molecules related to specific diseases in advance. Predicting drug targets by computational methods saves a great deal of financial and material resources compared to in vitro experiments. Therefore, several computational methods for drug target discovery have been designed. Recently, machine learning (ML) methods in biomedicine have developed rapidly. In this paper, we present an overview of drug target discovery methods based on machine learning. Considering that some machine learning methods integrate network analysis to predict drug targets, network-based methods are also introduced in this article. Finally, the challenges and future outlook of drug target discovery are discussed.

In vitro investigation of metabolic fate of α-mangostin and gartanin via skin permeation by LC-MS/MS and in silico evaluation of the metabolites by ADMET predictor™

BACKGROUND

Mangosteen, Garciniam angostana L., is a juicy fruit commonly found in Thailand. The rinds of Garciniam angostana L.have been used as a traditional medicine for the treatment of trauma, diarrhea and skin infection. It is also used in dermatological product such as in cosmetics. The mangosteen pericarp can be used to extract valuable bioactive xanthone compounds such as α-mangostin and gartanin. This study is aimed to predict the metabolism of α-mangostin and gartanin using in silico and in vitro skin permeation strategies.

METHODS

Based on their 2D molecular structures, metabolites of those compounds were predicted in silico using ADMET Predictor™. The K_m and V_max, for 5 important recombinant CYP isozymes 1A2, 2C9, 2C19, 2D6 and 3A4 were predicted. Moreover, the in vitro investigation of metabolites produced during skin permeation using human epidermal keratinocyte cells, neonatal (HEKn cells) was performed by LC-MS/MS.

RESULTS

It was found that the results derived from in silico were in excellent alignment with those obtained from in vitro studies for both compounds. The prediction referred that gartanin and α-mangostin were the substrate of CYP1A2, 2C9, 2C19 and 3A. In the investigation of α-mangostin metabolites by LC-MS/MS system, the MW of the parent compound was increased from 411.200 to 459.185 Da. Therefore, α-mangostin might be metabolized via tri-oxidation process. The increased molecular weight of parent compound (397.200 to 477.157 Da) illustrated that gartanin might be conjugated to sulfated derivatives.

CONCLUSIONS

In all the studies, α-mangostin and gartanin were predicted to be. metabolized via phase I and phase II metabolism (sulfation), respectively.

Prediction of Small Molecule Inhibitors Targeting the Severe Acute Respiratory Syndrome Coronavirus-2 RNA-dependent RNA Polymerase

The current COVID-19 outbreak warrants the design and development of novel anti-COVID therapeutics. Using a combination of bioinformatics and computational tools, we modelled the 3D structure of the RdRp (RNA-dependent RNA polymerase) of SARS-CoV2 (severe acute respiratory syndrome coronavirus-2) and predicted its probable GTP binding pocket in the active site. GTP is crucial for the formation of the initiation complex during RNA replication. This site was computationally targeted using a number of small molecule inhibitors of the hepatitis C RNA polymerase reported previously. Further optimizations suggested a lead molecule that may prove fruitful in the development of potent inhibitors against the RdRp of SARS-CoV2.

Exploring chemical compound space with quantum-based machine learning

Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space - the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of chemical compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of chemical compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and chemical knowledge.

TOCOSH FLOUR (Solanum tuberosum L.): A Toxicological Assessment of Traditional Peruvian Fermented Potatoes

Potato tocosh is a naturally processed potato for nutritional and curative purposes from traditional Peruvian medicine. The aim of this study was to investigate the acute and sub-acute toxicity of tocosh flour (TF). For sub-acute toxicity, TF was administered orally to rats daily once a day for 28 days at doses of 1000 mg/kg body weight (BW). Animals were observed for general behaviors, mortality, body weight variations, and histological analysis. At the end of treatment, relative organ weights, histopathology, hematological and biochemical parameters were analyzed. For acute toxicity, TF was administered orally to mice at doses of 2000 and 5000 mg/kg BW at a single dose in both sexes. Body weight, mortality, and clinical signs were observed for 14 days after treatment. The results of acute toxicity showed that the median lethal dose (LD50) value of TF is higher than 2000 g/kg BW but less than 5000 mg/Kg BW in mice. Death and toxicological symptoms were not found during the treatment. For sub-acute toxicity, we found that no-observed-adverse-effect levels (NOAEL) of TF in rats up to 1000 g/kg BW. There were statistically significant differences in body weight, and relative organ weight in the stomach and brain. No differences in hematological and biochemical parameters were observed when compared with the control group. For sub-acute toxicity, histopathological studies revealed minor abnormalities in liver and kidney tissues at doses of 5000 mg/Kg. Based on these results, TF is a traditional Peruvian medicine with high safety at up to 1000 mg/kg BW for 28 days in rats.

Identification of potential drug-targets by combining evolutionary information extracted from frequency profiles and molecular topological structures

Identifying interactions among drug compounds and target proteins is the basis of drug research and plays a crucial role in drug discovery. However, determining drug-target interactions (DTIs) and potential protein-compound interactions by biological experiment-based method alone is a very complicated, expensive, and time-consuming process. Hence, there is an intense motivation to design in silico prediction methods to overcome these obstacles. In this work, we designed a novel in silico strategy to predict proteome-scale DTIs based on the assumption that DTI pairs can be expressed through the evolutionary information derived from frequency profiles and drugs' structural properties. To achieve this, drug molecules are encoded into the substructure fingerprints to represent certain fragments; target proteins are first converted into position-specific scoring matrix (PSSM) and then encoded as 2-dimensional principal component analysis (2DPCA) descriptors. In the prediction phase, the feature weighted rotation forest (RF) classifier is used to estimate whether drug and target interact with each other on four benchmark datasets, including Enzymes, Ion Channels, GPCRs, and Nuclear Receptors. The prediction accuracy of cross-validation on the four datasets is 95.40%, 88.82%, 85.67%, and 82.22%, respectively. In order to have a clearer assessment of the proposed approach, we compared it with the discrete cosine transform (DCT) descriptor model, support vector machine (SVM) classifier model, and existing excellent approaches, including DBSI, NetCBP, KBMF2K, SIMCOMP, and RFDT. The excellent results of the experiment indicated that the proposed approach can effectively improve the DTI prediction accuracy and can be used as a practical tool for the research and design of new drugs.

Drug Side-Effect Profiles Prediction: From Empirical to Structural Risk Minimization

The identification of drug side-effects is considered to be an important step in drug design, which could not only shorten the time but also reduce the cost of drug development. In this paper, we investigate the relationship between the potential side-effects of drug candidates and their chemical structures. The preliminary Regularized Regression (RR) model for drug side-effects prediction has promising features in the efficiency of model training and the existence of a closed form solution. It performs better than other state-of-the-art methods, in terms of minimum accuracy and average accuracy. In order to dig inside how drug structure will associate with side effect, we further propose weighted GTS (Generalized T-Student Kernel: WGTS) SVM model from a structural risk minimization perspective. The SVM model proposed in this paper provides a better understanding of drug side-effects in the process of drug development. The usefulness of the WGTS model lies in the superior performance in a cross validation setting on 888 approved drugs with 1385 side-effects profiling from SIDER database. This work is expected to shed light on intriguing studies that predict potential un-identifying side-effects and suggest how we can avoid drug side-effects by the removal of some distinguished chemical structures.