• Antiviral
• Ca2+ cation channel
• Camel milk
• In silico
• Viral replication inhibition

• Humans
• Antimicrobial Peptides
• Antiviral Agents/pharmacology
• Antiviral Agents/therapeutic use
• Antiviral Agents/chemistry
• DEAD-box RNA Helicases/metabolism
• DEAD-box RNA Helicases/pharmacology
• Dengue/drug therapy
• Dengue Virus/genetics
• Peptides/pharmacology
• Peptides/chemistry
• Peptides/metabolism
• RNA, Viral/genetics
• TRPV Cation Channels
• Virus Replication

• QSAR
• dengue virus
• machine learning
• predictive models
• web server

• Drug Repositioning
• Dengue Virus
• Reproducibility of Results
• Machine Learning
• Antiviral Agents/pharmacology

• OLP0143, STS038 Institute of Microbial Technology

• Deanship of Scientific Research at King Khalid University
• Deanship of Scientific Research, King Faisal University

• Carbazole alkaloids
• Docking
• Drug discovery
• Main protease
• Murraya koenigii
• SARS-CoV-2

• Humans
• SARS-CoV-2
• Murraya/chemistry
• Molecular Docking Simulation
• COVID-19 Vaccines
• COVID-19
• Alkaloids/pharmacology
• Alkaloids/chemistry
• Antiviral Agents/pharmacology
• Antiviral Agents/chemistry
• Molecular Dynamics Simulation
• Carbazoles/pharmacology
• Carbazoles/chemistry
• Protease Inhibitors/pharmacology
• Protease Inhibitors/chemistry

• SARS-CoV-2
• Traditional Chinese medicine
• machine learning

• Humans
• SARS-CoV-2
• COVID-19
• Plants, Medicinal
• Cheminformatics
• COVID-19 Drug Treatment
• Antiviral Agents/pharmacology
• Machine Learning

• No. 32160153 National Natural Science Foundation of China
• No. 202001BC070001 and 202102AA100053 Natural Science Foundation of Yunnan Province
• No. 2019FB050 and 202105AD160008 the Yunnan Fundamental Research Projects

The therapeutic potential of venom-derived peptides, such as bioactive peptides (BAPs), is determined by specificity, stability, and pharmacokinetics properties. BAPs, including anti-infective or antimicrobial peptides (AMPs) and cell-penetrating peptides (CPPs), share several physicochemical characteristics and are potential alternatives to antibiotic-based therapies and drug delivery systems, respectively. This study used in silico methods to predict AMPs and CPPs derived from natterins from the venomous fish Thalassophryne nattereri. Fifty-seven BAPs (19 AMPs, 8 CPPs, and 30 AMPs/CPPs) were identified using the web servers CAMP, AMPA, AmpGram, C2Pred, and CellPPD. The physicochemical properties were analyzed using ProtParam, PepCalc, and DispHred tools. The membrane-binding potential and cellular location of each peptide were analyzed using the Boman index by APD3, and TMHMM web servers. All CPPs and two AMPs showed high membrane-binding potential. Fifty-four peptides were located in the plasma membrane. Peptide immunogenicity, toxicity, allergenicity, and ADMET parameters were evaluated using several web servers. Sixteen antiviral peptides and 37 anticancer peptides were predicted using the web servers Meta-iAVP and ACPred. Secondary structures and helical wheel projections were predicted using the PEP-FOLD3 and Heliquest web servers. Fifteen peptides are potential lead compounds and were selected to be further synthesized and tested experimentally in vitro to validate the in silico screening. The use of computer-aided design for predicting peptide structure and activity is fast and cost-effective and facilitates the design of potent therapeutic peptides. The results demonstrate that toxins form a natural biotechnological platform in drug discovery, and the presence of CPP and AMP sequences in toxin families opens new possibilities in toxin biochemistry research.

• COVID-19
• Molecular dynamic simulation
• Nsps
• famotidine
• molecular docking

• Cimetidine/pharmacology
• Famotidine/pharmacology
• Histamine
• Histamine H2 Antagonists/pharmacology
• Humans
• Molecular Docking Simulation
• COVID-19 Drug Treatment

Antibiotic drug resistance has emerged as a major public health threat globally. One of the leading causes of drug resistance is the colonization of microorganisms in biofilm mode. Hence, there is an urgent need to design novel and highly effective biofilm inhibitors that can work either synergistically with antibiotics or individually. Therefore, we have developed a recursive regression-based platform “Biofilm-i” employing a quantitative structure–activity relationship approach for making generalized predictions, along with group and species-specific predictions of biofilm inhibition efficiency of chemical(s). The platform encompasses eight predictors, three analysis tools, and data visualization modules. The experimentally validated biofilm inhibitors for model development were retrieved from the “aBiofilm” resource and processed using a 10-fold cross-validation approach using the support vector machine and andom forest machine learning techniques. The data was further sub-divided into training/testing and independent validation sets. From training/testing data sets the Pearson’s correlation coefficient of overall chemicals, Gram-positive bacteria, Gram-negative bacteria, fungus, Pseudomonas aeruginosa, Staphylococcus aureus, Candida albicans, and Escherichia coli was 0.60, 0.77, 0.62, 0.77, 0.73, 0.83, 0.70, and 0.71 respectively via Support Vector Machine. Further, all the QSAR models performed equally well on independent validation data sets. Additionally, we also checked the performance of the random forest machine learning technique for the above datasets. The integrated analysis tools can convert the chemical structure into different formats, search for a similar chemical in the aBiofilm database and design the analogs. Moreover, the data visualization modules check the distribution of experimentally validated biofilm inhibitors according to their common scaffolds. The Biofilm-i platform would be of immense help to researchers engaged in designing highly efficacious biofilm inhibitors for tackling the menace of antibiotic drug resistance.

• QSAR
• antibiotic drug resistance
• biofilm
• chemical descriptors
• inhibitors
• predictor

• Antiviral
• Drug repurposing
• Hepatitis C Virus
• Machine learning
• NS3-NS3/NS4A-NS5A-NS5B
• Non-structural protein
• Prediction
• QSAR

• ADMET studies
• SARS-CoV-2
• antifungal agents
• dynamics simulation
• methyl α-D-galactopyranoside esters
• molecular docking
• one-step acylation

• Anti-Bacterial Agents/chemistry
• Anti-Bacterial Agents/pharmacology
• Anti-Infective Agents/chemistry
• Anti-Infective Agents/pharmacology
• Antifungal Agents/chemistry
• Antifungal Agents/pharmacology
• COVID-19
• Esters/chemistry
• Fluconazole
• Galactose
• Humans
• Molecular Docking Simulation
• SARS-CoV-2

Nipah virus (NiV) is a recently emerged paramyxovirus that causes severe encephalitis and respiratory diseases in humans. Despite the severe pathogenicity of this virus and its pandemic potential, not even a single type of molecular therapeutics has been approved for human use. Considering the role of NiV attachment glycoprotein G (NiV-G), fusion glycoprotein (NiV-F), and nucleoprotein (NiV-N) in virus replication and spread, these are the most attractive targets for anti-NiV drug discovery. Therefore, to prospect for potential multitarget chemical/phytochemical inhibitor(s) against NiV, a sequential molecular docking and molecular-dynamics-based approach was implemented by simultaneously targeting NiV-G, NiV-F, and NiV-N. Information on potential NiV inhibitors was compiled from the literature, and their 3D structures were drawn manually, while the information and 3D structures of phytochemicals were retrieved from the established structural databases. Molecules were docked against NiV-G (PDB ID:2VSM), NiV-F (PDB ID:5EVM), and NiV-N (PDB ID:4CO6) and then prioritized based on (1) strong protein-binding affinity, (2) interactions with critically important binding-site residues, (3) ADME and pharmacokinetic properties, and (4) structural stability within the binding site. The molecules that bind to all the three viral proteins (NiV-G ∩ NiV-F ∩ NiV-N) were considered multitarget inhibitors. This study identified phytochemical molecules RASE0125 (17-O-Acetyl-nortetraphyllicine) and CARS0358 (NA) as distinct multitarget inhibitors of all three viral proteins, and chemical molecule ND_nw_193 (RSV604) as an inhibitor of NiV-G and NiV-N. We expect the identified compounds to be potential candidates for in vitro and in vivo antiviral studies, followed by clinical treatment of NiV.

• Nipah virus
• drug repurposing
• molecular docking
• molecular dynamics
• multitarget inhibitor
• small-molecule inhibitors

In the past few decades, chronic hepatitis B caused by hepatitis B virus (HBV) has been one of the most serious diseases to human health. The development of innovative systems is essential for preventing the complex pathogenesis of hepatitis B and reducing side effects caused by drugs. HBV inhibitory drugs have been developed through various compounds, and they are often limited by routine experimental screening and delay drug development. More recently, virtual screening of compounds has gradually been used in drug research with strong computational capability and is further applied in anti-HBV drug screening, thus facilitating a reliable drug screening process. However, the lack of structural information in traditional compound analysis is an important hurdle for unsatisfactory efficiency in drug screening. Here, a natural language processing technique was adopted to analyze compound simplified molecular input line entry system strings. By using the targeted optimized word2vec model for pretraining, we can accurately represent the relationship between the compound and its substructure. The machine learning model based on training results can effectively predict the inhibitory effect of compounds on HBV and liver toxicity. The reliability of the model is verified by the results of wet-lab experiments. In addition, a tool has been published to predict potential compounds. Hence, this article provides a new perspective on the prediction of compound properties for anti-HBV drugs that can help improve hepatitis B diagnosis and further develop human health in the future.

• Antiviral Agents/pharmacology
• Antiviral Agents/therapeutic use
• Drug Discovery/methods
• Hepatitis B/drug therapy
• Hepatitis B virus
• Humans
• Reproducibility of Results

• National Natural Science Foundation of China

• MD simulation
• SARS-CoV-2
• docking
• drug
• main protease
• triazole

• Antiviral Agents/chemistry
• Antiviral Agents/metabolism
• Antiviral Agents/therapeutic use
• Benzocycloheptenes/chemistry
• Benzocycloheptenes/metabolism
• Binding Sites
• COVID-19/virology
• Coronavirus 3C Proteases/antagonists & inhibitors
• Coronavirus 3C Proteases/metabolism
• Databases, Chemical
• Half-Life
• Humans
• Ligands
• Molecular Docking Simulation
• Molecular Dynamics Simulation
• Protease Inhibitors/chemistry
• Protease Inhibitors/metabolism
• Protease Inhibitors/therapeutic use
• Protein Binding
• Quantitative Structure-Activity Relationship
• SARS-CoV-2/enzymology
• SARS-CoV-2/isolation & purification
• Triazoles/chemistry
• Triazoles/metabolism
• Triazoles/therapeutic use
• COVID-19 Drug Treatment

• NU20-03-00309 Ministry of Health of the Czech Republic,
• CZ.1.05/1.1.00/02.0109 BIOCEV
• 86652036 Institute of Biotechnology of t he Czech Academy of Sciences

• Antivirals
• Drug repurposing
• Epidemics
• FDA approved
• Pandemics
• Virus

• COVID-19
• Epidemics
• Humans
• Influenza A Virus, H1N1 Subtype
• Molecular Docking Simulation
• Pharmaceutical Preparations
• SARS-CoV-2
• Zika Virus
• Zika Virus Infection

• Ebola virus
• Machine learning
• Prediction algorithm
• QSAR
• Random forest
• Web server

• AI
• COVID-19
• Chemical descriptors
• Coronaviruses
• Drug repurposing
• Machine learning
• SARS-CoV-2

• Machine learning
• antivirals
• drug design
• drug discovery

• HIV
• antiviral drugs
• bioinformatics
• cheminformatics
• drug treatment optimization.
• machine learning

• bioinformatics 3
• in silico
• interfering peptides
• peptide
• protein–protein interactions

• COVID-19
• RdRp
• SARS-CoV-2
• mutations
• remdesivir
• virtual screening

• ADME-Tox
• CADD
• COVID-19
• MM-PBSA
• SARS-CoV-2
• Strychnos nux-vomica
• main protease
• molecular dynamics simulations

Hepatitis A virus (HAV) infection is still an important health issue worldwide. Although several effective HAV vaccines are available, it is difficult to perform universal vaccination in certain countries. Therefore, it may be better to develop antivirals against HAV for the prevention of severe hepatitis A. We found that several drugs potentially inhibit HAV internal ribosomal entry site-dependent translation and HAV replication. Artificial intelligence and machine learning could also support screening of anti-HAV drugs, using drug repositioning and drug rescue approaches.

• Artificial intelligence
• Hepatitis A virus internal ribosomal entry sites
• Cap-independent translation
• Antivirals
• Severe hepatitis A
• Glucose-regulated protein 78

• COVID-19
• Computational tools
• Coronavirus
• Databases
• Genomic
• SARS-CoV-2

• COVID-19/epidemiology
• COVID-19/genetics
• Computational Biology
• Computer Simulation
• Humans
• Pandemics
• Proteome/genetics
• SARS-CoV-2/genetics

• deep learning
• drug discovery
• machine learning
• pharmaceutics
• support vector machines

• Anti-viral activity
• Docking
• Molecular dynamics simulation
• Pharmacokinetics
• SARS-CoV-2 mpro

The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https://github.com/pmartR/FIRM-AVP with an accompanying web application at https://msc-viz.emsl.pnnl.gov/AVPR.

• HBV
• HBV RNA
• HBcrAg
• direct-acting antivirals
• novel viral markers

• Bioinformatics
• COVID-19 receptor blockers
• Medicinal herbal compounds
• SARS-CoV-2
• Therapeutic targets
• miRNAs

• QSAR
• docking
• homology model
• molecular dynamics
• peptide design
• pharmacophore mapping

• ChEMBL
• antiretroviral compounds
• big data
• machine learning
• perturbation theory

Biofilms play an important role in the antibiotic drug resistance, which is threatening public health globally. Almost, all microbes mimic multicellular lifestyle to form biofilm by undergoing phenotypic changes to adapt adverse environmental conditions. Many anti-biofilm agents have been experimentally validated to disrupt the biofilms during last three decades. To organize this data, we developed the ‘aBiofilm’ resource (http://bioinfo.imtech.res.in/manojk/abiofilm/) that harbors a database, a predictor, and the data visualization modules. The database contains biological, chemical, and structural details of 5027 anti-biofilm agents (1720 unique) reported from 1988–2017. These agents target over 140 organisms including Gram-negative, Gram-positive bacteria, and fungus. They are mainly chemicals, peptides, phages, secondary metabolites, antibodies, nanoparticles and extracts. They show the diverse mode of actions by attacking mainly signaling molecules, biofilm matrix, genes, extracellular polymeric substances, and many more. The QSAR based predictor identifies the anti-biofilm potential of an unknown chemical with an accuracy of ∼80.00%. The data visualization section summarized the biofilm stages targeted (Circos plot); interaction maps (Cytoscape) and chemicals diversification (CheS-Mapper) of the agents. This comprehensive platform would help the researchers to understand the multilevel communication in the microbial consortium. It may aid in developing anti-biofilm therapeutics to deal with antibiotic drug resistance menace.

• HIV
• Naïve Bayes
• assay central
• deep learning
• drug discovery
• machine learning
• reverse transcriptase
• support vector machine

Nipah virus (NiV) caused several outbreaks in Asian countries including the latest one from Kerala state of India. There is no drug available against NiV till now, despite its urgent requirement. In the current study, we have provided a computational one-stop solution for NiV inhibitors. We have developed the first “anti-Nipah” web resource, which comprising of a data repository, prediction method, and data visualization module. The database contains of 313 (181 unique) chemicals extracted from research articles and patents, which were tested for different strains of NiV isolated from various outbreaks. Moreover, the quantitative structure–activity relationship (QSAR) based regression predictors were developed using chemicals having half maximal inhibitory concentration (IC₅₀). Predictive models were accomplished using support vector machine employing 10-fold cross validation technique. The overall predictor showed the Pearson's correlation coefficient of 0.82 on training/testing dataset. Likewise, it also performed equally well on the independent validation dataset. The robustness of the predictive model was confirmed by applicability domain (William's plot) and scatter plot between actual and predicted efficiencies. Further, the data visualization module from chemical clustering analysis displayed the diversity in the NiV inhibitors. Therefore, this web platform would be of immense help to the researchers working in developing effective inhibitors against NiV. The user-friendly web server is freely available on URL: http://bioinfo.imtech.res.in/manojk/antinipah/.

• Nipah virus
• QSAR
• database
• inhibitors
• outbreak
• prediction algorithm

Novel experimental and sequencing techniques have led to an exponential explosion and spiraling of data in viral genomics. To analyse such data, rapidly gain information, and transform this information to knowledge, interdisciplinary approaches involving several different types of expertise are necessary. Machine learning has been in the forefront of providing models with increasing accuracy due to development of newer paradigms with strong fundamental bases. Support Vector Machines (SVM) is one such robust tool, based rigorously on statistical learning theory. SVM provides very high quality and robust solutions to classification and regression problems. Several studies in virology employ high performance tools including SVM for identification of potentially important gene and protein functions. This is mainly due to the highly beneficial aspects of SVM. In this chapter we briefly provide lucid and easy to understand details of SVM algorithms along with applications in virology.

Flaviviruses are arboviruses, which comprises more than 70 viruses, covering broad geographic ranges, and responsible for significant mortality and morbidity globally. Due to the lack of efficient inhibitors targeting flaviviruses, the designing of novel and efficient anti-flavi agents is an important problem. Therefore, in the current study, we have developed a dedicated prediction algorithm anti-flavi, to identify inhibition ability of chemicals and peptides against flaviviruses through quantitative structure–activity relationship based method. We extracted the non-redundant 2168 chemicals and 117 peptides from ChEMBL and AVPpred databases, respectively, with reported IC₅₀ values. The regression based model developed on training/testing datasets of 1952 chemicals and 105 peptides displayed the Pearson’s correlation coefficient (PCC) of 0.87, 0.84, and 0.87, 0.83 using support vector machine and random forest techniques correspondingly. We also explored the peptidomimetics approach, in which the most contributing descriptors of peptides were used to identify chemicals having anti-flavi potential. Conversely, the selected descriptors of chemicals performed well to predict anti-flavi peptides. Moreover, the developed model proved to be highly robust while checked through various approaches like independent validation and decoy datasets. We hope that our web server would prove a useful tool to predict and design the efficient anti-flavi agents. The anti-flavi webserver is freely available at URL http://bioinfo.imtech.res.in/manojk/antiflavi.

• QSAR
• flaviviruses
• inhibitor
• machine learning techniques
• peptidomimetics
• prediction algorithm
• random forest
• support vector machine

A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC₅₀ and percent inhibition datasets of PR, RT, IN respectively. These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform (http://bioinfo.imtech.res.in/manojk/hivproti) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development.

• Algorithm
• HIV
• Inhibitors
• Integrase
• Protease
• QSAR
• Reverse transcriptase
• Web server

• Antimicrobial peptides
• Antiviral peptides
• Biologically active peptides
• Cell-penetrating peptides
• Peptide-based drugs

• Anti-Infective Agents
• Antimicrobial Cationic Peptides
• Cell-Penetrating Peptides
• Communicable Diseases/drug therapy
• Drug Design
• Humans

• ADMET
• anti-HCV activity
• contribution
• design
• fragments
• mtk-QSBER
• quadratic indices
• screening

• Alignment-free protein analysis
• Enzyme
• ProtDCal
• Protein descriptors
• Support vector machines
• TI2BioP

• Chemically modified siRNA
• RNAi
• cm-siRNAs
• efficacy prediction
• siRNA
• siRNA modifications
• small interfering RNA
• webserver