Computational Identification of Inhibitors Using QSAR Approach Against Nipah Virus

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the "Anti-Dengue" algorithm that predicts dengue virus inhibitors using a quantitative structure-activity relationship (QSAR) and MLTs. Using the "DrugRepV" database, we extracted chemicals (small molecules) and repurposed drugs targeting the dengue virus with their corresponding IC50 values. Then, molecular descriptors and fingerprints were computed for these molecules using PaDEL software. Further, these molecules were split into training/testing and independent validation datasets. We developed regression-based predictive models employing 10-fold cross-validation using a variety of machine learning approaches, including SVM, ANN, kNN, and RF. The best predictive model yielded a PCC of 0.71 on the training/testing dataset and 0.81 on the independent validation dataset. The created model's reliability and robustness were assessed using William's plot, scatter plot, decoy set, and chemical clustering analyses. Predictive models were utilized to identify possible drug candidates that could be repurposed. We identified goserelin, gonadorelin, and nafarelin as potential repurposed drugs with high pIC50 values. "Anti-Dengue" may be beneficial in accelerating antiviral drug development against the dengue virus.

Anti-Biofilm: Machine Learning Assisted Prediction of IC50 Activity of Chemicals Against Biofilms of Microbes Causing Antimicrobial Resistance and Implications in Drug Repurposing

Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of anti-biofilm agents helps in tackling the menace of antibiotic resistance. The identification of efficient anti-biofilm chemicals remains a challenge. Therefore, in this study, we developed 'anti-Biofilm', a machine learning technique (MLT) based predictive algorithm for identifying and analyzing the biofilm inhibition of small molecules. The algorithm is developed using experimentally validated anti-biofilm compounds with half maximal inhibitory concentration (IC₅₀) values extracted from aBiofilm resource. Out of the five MLTs, the Support Vector Machine performed best with Pearson's correlation coefficient of 0.75 on the training/testing data set. The robustness of the developed model was further checked using an independent validation dataset. While analyzing the chemical diversity of the anti-biofilm compounds, we observed that they occupy diverse chemical spaces with parent molecules like furanone, urea, phenolic acids, quinolines, and many more. Use of diverse chemicals as input further signifies the robustness of our predictive models. The three best-performing machine learning models were implemented as a user-friendly 'anti-Biofilm' web server (https://bioinfo.imtech.res.in/manojk/antibiofilm/) with different other modules which make 'anti-Biofilm' a comprehensive platform. Therefore, we hope that our initiative will be helpful for the scientific community engaged in identifying effective anti-biofilm agents to target the problem of antimicrobial resistance.

Are we ready to fight the Nipah virus pandemic? An overview of drug targets, current medications, and potential leads

Nipah virus (NiV) is a high-lethality RNA virus from the family of Paramyxoviridae and genus Henipavirus, classified under Biosafety Level-4 (BSL-4) pathogen due to the severity of pathogenicity and lack of medications and vaccines. Direct contacts or the body fluids of infected animals are the major factor of transmission of NiV. As it is not an airborne infection, the transmission rate is relatively low. Still, mutations of the NiV in the animal reservoir over the years, followed by zoonotic transfer, can make the deadliness of the virus manifold in upcoming years. Therefore, there is no denial of the possibility of a pandemic after COVID-19 considering the severe pathogenicity of NiV, and that is why we need to be prepared with possible drugs in upcoming days. Considering the time constraints, computational aided drug design (CADD) is an efficient way to study the virus and perform the drug design and test the HITs to lead experimentally. Therefore, this review focuses primarily on NiV target proteins (covering NiV and human), experimentally tested repurposed drug details, and latest computational studies on potential lead molecules, which can be explored as potential drug candidates. Computationally identified drug candidates, including their chemical structures, docking scores, amino acid level interaction with corresponding protein, and the platform used for the studies, are thoroughly discussed. The review will offer a one-stop study to access what had been performed and what can be performed in the CADD of NiV.

Biofilm-i: A Platform for Predicting Biofilm Inhibitors Using Quantitative Structure-Relationship (QSAR) Based Regression Models to Curb Antibiotic Resistance

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.

Computational Identification of Potential Multitarget Inhibitors of Nipah Virus by Molecular Docking and Molecular Dynamics

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 amidst Covid-19 Pandemic, another Re-Emerging Infectious Disease of Pandemic Potential - a Narrative Review

Introduction: Nipah virus (NiV) was reported for the first time from the Kampung Sungai Nipah village of Malaysia in 1998. Since then, there have been multiple outbreaks, all of them in South- and South-East Asia. According to the World Health Organization (WHO), up to 75% of Nipah infections were proven to be fatal. Nipah virus belongs to the group of Biosafety Level-4 pathogen associated with high case fatality rate (40-75%). Methodology:According to the PRISMA guidelines for 2020, we searched in four medical databases (PubMed, Google Scholar, EMBASE and Scopus) and selected relevant studies from the past twenty years till November 2021. Review:Nipah virus was first detected in Malaysia's Kampung Sungai Nipah in 1998. By May 1999, the Malaysia Ministry of Health in association with the Centers for Disease Control (CDC) reported a total of 258 cases with a case fatality rate of almost 40%. Nipah in Kozhikode:Experts from the Pune Institute and Bhopal's National Institute of High Security Animal Diseases had collected Bat samples from Pazhoor in Chathamangalam gram panchayat (where a 12-year-old died due to Nipah infection on September 5 carried antibodies of the virus). All Indian outbreaks have seen person-to-person transmission. The virus found in Kerala differed from those two variants in terms of genetic structure. It also differed by 1.96% from the Bangladesh variant. The difference with the Malaysian variant was 8.42%. While PCR is the most sensitive technique for diagnosing active NiV infection, NiV-specific IgM ELISA offers a serological option when PCR is not available. Conclusions:Understanding the fruit bat ecology, NiV illness seasonality, and the transmission risk of various intermediate species requires a One Health approach. The danger of reintroduction into animal or human populations cannot be handled without a thorough understanding of the wildlife reservoir.

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Ebola virus is a deadly pathogen responsible for a frequent series of outbreaks since 1976. Despite various efforts from researchers worldwide, its mortality and fatality are quite high. For antiviral drug discovery, the computational efforts are considered highly useful. Therefore, we have developed an 'anti-Ebola' web server, through quantitative structure-activity relationship information of available molecules with experimental anti-Ebola activities. Three hundred and five unique anti-Ebola compounds with their respective IC₅₀ values were extracted from the 'DrugRepV' database. Later, the compounds were used to extract the molecular descriptors, which were subjected to regression-based model development. The robust machine learning techniques, namely support vector machine, random forest and artificial neural network, were employed using tenfold cross-validation. After a randomization approach, the best predictive model showed Pearson's correlation coefficient ranges from 0.83 to 0.98 on training/testing (T²⁷⁴) dataset. The robustness of the developed models was cross-evaluated using William's plot. The highly robust computational models are integrated into the web server. The 'anti-Ebola' web server is freely available at https://bioinfo.imtech.res.in/manojk/antiebola . We anticipate this will serve the scientific community for developing effective inhibitors against the Ebola virus.

Computational identification of repurposed drugs against viruses causing epidemics and pandemics via drug-target network analysis

Viral epidemics and pandemics are considered public health emergencies. However, traditional and novel antiviral discovery approaches are unable to mitigate them in a timely manner. Notably, drug repurposing emerged as an alternative strategy to provide antiviral solutions in a timely and cost-effective manner. In the literature, many FDA-approved drugs have been repurposed to inhibit viruses, while a few among them have also entered clinical trials. Using experimental data, we identified repurposed drugs against 14 viruses responsible for causing epidemics and pandemics such as SARS-CoV-2, SARS, Middle East respiratory syndrome, influenza H1N1, Ebola, Zika, Nipah, chikungunya, and others. We developed a novel computational “drug-target-drug” approach that uses the drug-targets extracted for specific drugs, which are experimentally validated in vitro or in vivo for antiviral activity. Furthermore, these extracted drug-targets were used to fetch the novel FDA-approved drugs for each virus and prioritize them by calculating their confidence scores. Pathway analysis showed that the majority of the extracted targets are involved in cancer and signaling pathways. For SARS-CoV-2, our method identified 21 potential repurposed drugs, of which 7 (e.g., baricitinib, ramipril, chlorpromazine, enalaprilat, etc.) have already entered clinical trials. The prioritized drug candidates were further validated using a molecular docking approach. Therefore, we anticipate success during the experimental validation of our predicted FDA-approved repurposed drugs against 14 viruses. This study will assist the scientific community in hastening research aimed at the development of antiviral therapeutics.

Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning

The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anticorona activity (IC50/EC50) from 'DrugRepV' repository. The unique entries of SARS-CoV-2 (142), SARS (221), MERS (123), and overall Coronaviruses (414) were subdivided into the training/testing and independent validation datasets, followed by the extraction of chemical/structural descriptors and fingerprints (17968). The highly relevant features were filtered using the recursive feature selection algorithm. The selected chemical descriptors were used to develop prediction models with Pearson's correlation coefficients ranging from 0.60 to 0.90 on training/testing. The robustness of the predictive models was further ensured using external independent validation datasets, decoy datasets, applicability domain, and chemical analyses. The developed models were used to predict promising repurposed drug candidates against coronaviruses after scanning the DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by molecular docking against the spike protein complex with ACE receptor. We found potential repurposed drugs namely Verteporfin, Alatrofloxacin, Metergoline, Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. These 'anticorona' computational models would assist in antiviral drug discovery against SARS-CoV-2 and other Coronaviruses.

Piperazine-substituted derivatives of favipiravir for Nipah virus inhibition: What do in silico studies unravel?

Favipiravir is found to show excellent in-vitro inhibition activity against Nipah virus. To explore the structure-property relationship of Favipiravir, in silico designing of a series of piperazine substituted Favipiravir derivatives are attempted and computational screening has been done to evaluate its bimolecular interactions with Nipah virus. The geometrical features of all the molecules have been addressed from Density Functional Theory calculations. Chemical reactivity descriptor analysis was carried out to understand various reactivity parameters. The drug-likeness properties were estimated by a detailed ADMET study. The binding ability and the mode of binding of these derivatives into the Nipah virus are obtained from molecular docking studies. Our calculations show greater binding ability for the designed inhibitors compared to that of the experimentally reported molecule. Overall, the present work proves to offers new insights and guidelines for synthetic chemists to develop new drugs using piperazine substituted Favipiravir in the treatment of Nipah virus.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s42452-020-04051-9.

QSAR, QSTR, and molecular docking studies of the anti-proliferative activity of phenylpiperazine derivatives against DU145 prostate cancer cell lines

Background

Prostate cancer is the most common non-cutaneous cancer in males and accounts for about 4% of all cancer-related deaths in males annually. In silico methods provide faster, economical, and environmentally friendly alternatives to the traditional trial and error method of lead identification and optimization. This study, therefore, was aimed at building a robust QSAR and QSTR model to predict the anti-proliferate activity and toxicity of some phenylpiperazine compounds against the DU145 prostate cancer cell lines and normal prostate epithelial cells as well as carry out molecular docking studies between the compounds and the androgen receptor.

Results

Genetic Function Algorithm–Multilinear Regression approach was employed in building the QSAR and QSTR model. The QSAR model built had statistical parameters R2 = 0.7792, R2adj. = 0.7240, Q2cv = 0.6607, and R2ext = 0.6049 and revealed the anti-proliferate activity to be strongly dependent on the molecular descriptors: VR3_Dzp, VE3_Dzi, Kier3, RHSA, and RDF55v. The QSTR model, on the other hand, had statistical parameters R2 = 0.8652, R2adj. = 0.8315, Q2cv = 0.7788, and R2ext = 0.6344. The toxicity of the compounds was observed to be dependent on the descriptors MATS8c, MATS3s, ETA_EtaP_F, and RDF95m. The molecular descriptors in both models were poorly correlated (R < 0.4) and had variance inflation factors < 3. Molecular docking studies between the androgen receptor and compounds 25 and 32 revealed the compounds primarily formed hydrogen, halogen, and hydrophobic interactions with the receptor.

Conclusion

Findings from this study can be employed in in silico design of novel phenylpiperazine compounds. It can also be employed in predicting the toxicity and anti-proliferate activity of other phenylpiperazine compounds against DU145 prostate cancer cell lines.

COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19

Objective: The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19 to assist drug repurposing efforts. Materials and Methods: SciBiteAI ontological tagging of the COVID Open Research Dataset (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of protein and drug terms, and confidence scores were calculated for each entity pair. Results: COKE processing of the current CORD-19 database identified about 3,000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. Discussion: The rapidly evolving situation concerning the COVID-19 pandemic has resulted in a dramatic growth of publications on this subject in a short period. These circumstances call for methods that can condense the literature into the key concepts and relationships necessary for insights into SARS-CoV-2 drug repurposing. Conclusion: The COKE repository and web application deliver key drug - target protein relationships to researchers studying SARS-CoV-2. COKE portal may provide comprehensive and critical information on studies concerning drug repurposing against COVID-19. COKE is freely available at https://coke.mml.unc.edu/ and the code is available at https://github.com/DnlRKorn/CoKE.

NipahVR: a resource of multi-targeted putative therapeutics and epitopes for the Nipah virus

Nipah virus (NiV) is an emerging and priority pathogen from the Paramyxoviridae family with a high fatality rate. It causes various diseases such as respiratory ailments and encephalitis and poses a great threat to humans and livestock. Despite various efforts, there is no approved antiviral treatment available. Therefore, to expedite and assist the research, we have developed an integrative resource NipahVR (http://bioinfo.imtech.res.in/manojk/nipahvr/) for the multi-targeted putative therapeutics and epitopes for NiV. It is structured into different sections, i.e. genomes, codon usage, phylogenomics, molecular diagnostic primers, therapeutics (siRNAs, sgRNAs, miRNAs) and vaccine epitopes (B-cell, CTL, MHC-I and -II binders). Most decisively, potentially efficient therapeutic regimens targeting different NiV proteins and genes were anticipated and projected. We hope this computational resource would be helpful in developing combating strategies against this deadly pathogen. Database URL: http://bioinfo.imtech.res.in/manojk/nipahvr/.