CaNDis: a web server for investigation of causal relationships between diseases, drugs and drug targets

: Due to the increasing prevalence of cancer year by year, and the complexity and refractory nature of the disease itself, it is required to constantly innovate the development of new cancer treatment schemes. At the same time, the understanding of cancers has deepened, from the use of chemotherapy regimens with high toxicity and side effects, to the popularity of targeted drugs with specific targets, to precise treatments based on tumor characteristics rather than traditional anatomical location classification. In precision medical, in the view of the specific tumor diseases and their biological characteristics, it has great potential to develop tissue-agnostic targeted therapy with broad-spectrum anticancer significance. The present review has discussed tissue-agnostic targeted therapy based on the biological and genetic characteristics of cancers, expounded its theoretical basis and strategies for drug development. And the feasible drug targets, FDA-approved drugs, as well as drug candidates in clinical trials have also been summarized. In conclusion, the “tissue-agnostic targeted therapy” is a breakthrough in anticancer therapies.

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Coronavirus Disease 2019: Virology and Drug Targets

Infectious Disorders - Drug Targets ◽

10.2174/1871526520666201209145302 ◽

2020 ◽

Vol 20 ◽

Author(s):

Praveen Thaggikuppe Krishnamurthy

Keyword(s):

High Throughput Screening ◽

Drug Targets ◽

Vaccine Development ◽

Antiviral Drug ◽

Drug Repurposing ◽

Structural Proteins ◽

Current Status ◽

Approved Drugs ◽

Viral Target ◽

Fda Approved Drugs

: The Coronavirus Disease 2019, a pandemic caused by novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is seriously affecting global health and the economy. As the vaccine development takes time, the current research is focused on repurposing FDA approved drugs against the viral target proteins. This review discusses the current understanding of SARS-CoV-2 virology, its target structural proteins (S- glycoprotein), non-structural proteins (3- chymotrypsin-like protease, papain-like protease, RNA-dependent RNA polymerase, and helicase) and accessory proteins, drug discovery strategies (drug repurposing, artificial intelligence, and high-throughput screening), and the current status of antiviral drug development.

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DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions

10.1101/2021.04.28.441733 ◽

2021 ◽

Author(s):

Tilman Hinnerichs ◽

Robert Hoehndorf

Keyword(s):

Drug Target ◽

Drug Targets ◽

Interaction Network ◽

Drug Repurposing ◽

Computational Method ◽

Interaction Networks ◽

Supplementary Information ◽

Prediction Methods ◽

Link Type ◽

Molecular Features

AbstractMotivationIn silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding potentials. Both approaches can be combined with information about interaction networks.ResultsWe developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major affects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.AvailabilityDTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO.Supplementary informationSupplementary data are available at https://github.com/ THinnerichs/DTI-VOODOO.

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Identification of Drugs Targeting Multiple Viral and Human Proteins Using Computational Analysis for Repurposing Against COVID-19

10.26434/chemrxiv.12366938.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Sugandh Kumar ◽

Pratima Kumari ◽

Geetanjali Agnihotri ◽

Preethy VijayKumar ◽

Shaheerah Khan ◽

...

Keyword(s):

Interaction Network ◽

Viral Proteins ◽

Inhibitory Effect ◽

Host Proteins ◽

Disease Manifestation ◽

Protein Protein Interaction ◽

Human Proteins ◽

Approved Drugs ◽

Viral Lifecycle ◽

Fda Approved Drugs

The SARS-CoV2 is a highly contagious pathogen that causes a respiratory disease named COVID-19. The COVID-19 was declared a pandemic by the WHO on 11th March 2020. It has affected about 5.38 million people globally (identified cases as on 24th May 2020), with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. The molecularly targeted therapies have proven their utility in various diseases such as HIV, SARS, and HCV. Therefore, a lot of efforts are being directed towards the identification of molecules that can be helpful in the management of COVID-19. In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against thirteen SARS-CoV2 proteins in order to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Our strategy originates from the fact that individual viral proteins play specific role in multiple aspects of viral lifecycle such as attachment, entry, replication, morphogenesis and egress and targeting them simultaneously will have better inhibitory effect. Additionally, we analyzed if the identified molecules can also affect the host proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) were identified using analysis of NCBI-GEO data (GEO-ID: GSE-147507). A pathway and protein-protein interaction network analysis of the identified DEGs led to the identification of network hubs that may play important roles in SARS-CoV2 infection. Therefore, targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 29 molecules that can bind to various SARS-CoV2 and human host proteins. We hope that this study will help researchers in the identification and repurposing of multipotent drugs, simultaneously targeting the several viral and host proteins, for the treatment of COVID-19.

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ChAlPred: A Web Server for Prediction of Allergenicity of Chemical Compounds

10.1101/2021.05.21.445101 ◽

2021 ◽

Author(s):

Neelam Sharma ◽

Sumeet Patiyal ◽

Anjali Dhall ◽

Leimarembi Devi Naorem ◽

Gajendra P.S. Raghava

Keyword(s):

Chemical Compound ◽

Prediction Models ◽

Web Server ◽

Chemical Compounds ◽

Validation Dataset ◽

Fingerprint Analysis ◽

Approved Drugs ◽

3D Descriptors ◽

User Friendly ◽

Fda Approved Drugs

Allergy is the abrupt reaction of the immune system that may occur after the exposure with allergens like protein/peptide or chemical allergens. In past number of methods of have been developed for classifying the protein/peptide based allergen. To the best of our knowledge, there is no method to classify the allergenicity of chemical compound. Here, we have proposed a method named ChAlPred, which can be used to fill the gap for predicting the chemical compound that might cause allergy. In this study, we have obtained the dataset of 403 allergen and 1074 non-allergen chemical compounds and used 2D, 3D and FP descriptors to train, test and validate our prediction models. The fingerprint analysis of the dataset indicates that PubChemFP129 and GraphFP1014 are more frequent in the allergenic chemical compounds, whereas KRFP890 is highly present in non-allergenic chemical compounds. Our XGB based model achieved the AUC of 0.89 on validation dataset using 2D descriptors. RF based model has outperformed other classifiers using 3D descriptors (AUC = 0.85), FP descriptors (AUC = 0.92), combined descriptors (AUC = 0.93), and hybrid model (AUC = 0.92) on validation dataset. In addition, we have also reported some FDA-approved drugs like Cefuroxime, Spironolactone, and Tioconazole which can cause the allergic symptoms. A user user-friendly web server named ChAlPred has been developed to predict the chemical allergens. It can be easily accessed at https://webs.iiitd.edu.in/raghava/chalpred/.

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Identification of Drugs Targeting Multiple Viral and Human Proteins Using Computational Analysis for Repurposing Against COVID-19

10.26434/chemrxiv.12366938 ◽

2020 ◽

Author(s):

Sugandh Kumar ◽

Pratima Kumari ◽

Geetanjali Agnihotri ◽

Preethy VijayKumar ◽

Shaheerah Khan ◽

...

Keyword(s):

Interaction Network ◽

Viral Proteins ◽

Inhibitory Effect ◽

Host Proteins ◽

Disease Manifestation ◽

Protein Protein Interaction ◽

Human Proteins ◽

Approved Drugs ◽

Viral Lifecycle ◽

Fda Approved Drugs

The SARS-CoV2 is a highly contagious pathogen that causes a respiratory disease named COVID-19. The COVID-19 was declared a pandemic by the WHO on 11th March 2020. It has affected about 5.38 million people globally (identified cases as on 24th May 2020), with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. The molecularly targeted therapies have proven their utility in various diseases such as HIV, SARS, and HCV. Therefore, a lot of efforts are being directed towards the identification of molecules that can be helpful in the management of COVID-19. In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against thirteen SARS-CoV2 proteins in order to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Our strategy originates from the fact that individual viral proteins play specific role in multiple aspects of viral lifecycle such as attachment, entry, replication, morphogenesis and egress and targeting them simultaneously will have better inhibitory effect. Additionally, we analyzed if the identified molecules can also affect the host proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) were identified using analysis of NCBI-GEO data (GEO-ID: GSE-147507). A pathway and protein-protein interaction network analysis of the identified DEGs led to the identification of network hubs that may play important roles in SARS-CoV2 infection. Therefore, targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 29 molecules that can bind to various SARS-CoV2 and human host proteins. We hope that this study will help researchers in the identification and repurposing of multipotent drugs, simultaneously targeting the several viral and host proteins, for the treatment of COVID-19.

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Variable dose analysis: A novel RNAi-based method for detection of synthetic lethal interactions

10.1101/176974 ◽

2017 ◽

Author(s):

Benjamin E. Housden ◽

Zhongchi Li ◽

Colleen Kelley ◽

Yuanli Wang ◽

Yanhui Hu ◽

...

Keyword(s):

Drug Targets ◽

Limited Range ◽

Synthetic Lethal ◽

Candidate Drug ◽

Highly Sensitive ◽

Synthetic Lethal Interactions ◽

A Cell ◽

Approved Drugs ◽

Fda Approved Drugs ◽

Variable Dose

AbstractSynthetic sick or synthetic lethal (SS/L) screens are a powerful way to identify candidate drug targets to specifically kill tumor cells but such screens generally suffer from low reproducibility. We found that many SS/L interactions involve essential genes and are therefore detectable within a limited range of knockdown efficiency. Such interactions are often missed by overly efficient RNAi reagents. We therefore developed an assay that measures viability over a range of knockdown efficiency within a cell population. This method, called variable dose analysis (VDA), is highly sensitive to viability phenotypes and reproducibly detects SS/L interactions. We applied the VDA method to search for SS/L interactions with TSC1 and TSC2, the two tumor suppressors underlying tuberous sclerosis complex (TSC) and generated a SS/L network for TSC. Using this network, we identified four FDA-approved drugs that selectively affect viability of TSC deficient cells, representing promising candidates for repurposing to treat TSC-related tumors.

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Recognizing drug targets using evolutionary information: implications for repurposing FDA-approved drugs against Mycobacterium tuberculosis H37Rv

Molecular BioSystems ◽

10.1039/c5mb00476d ◽

2015 ◽

Vol 11 (12) ◽

pp. 3316-3331 ◽

Cited By ~ 11

Author(s):

Gayatri Ramakrishnan ◽

Nagasuma R. Chandra ◽

Narayanaswamy Srinivasan

Keyword(s):

Mycobacterium Tuberculosis ◽

Drug Targets ◽

Drug Repurposing ◽

Target Space ◽

Evolutionary Information ◽

Mycobacterium Tuberculosis H37rv ◽

The Past ◽

Approved Drugs ◽

Fda Approved Drugs ◽

Computational Drug Discovery

Drug repurposing to explore target space has been gaining pace over the past decade with the upsurge in the use of systematic approaches for computational drug discovery.

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A new computational drug repurposing method using established disease–drug pair knowledge

Bioinformatics ◽

10.1093/bioinformatics/btz156 ◽

2019 ◽

Vol 35 (19) ◽

pp. 3672-3678 ◽

Cited By ~ 8

Author(s):

Nafiseh Saberian ◽

Azam Peyvandipour ◽

Michele Donato ◽

Sahar Ansari ◽

Sorin Draghici

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Large Scale ◽

Expression Profiles ◽

Drug Repurposing ◽

Supervised Machine Learning ◽

Supplementary Information ◽

Sources Of Information ◽

Approved Drugs ◽

Fda Approved Drugs

Abstract Motivation Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs. Results We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing. Availability and implementation The R scripts are available on demand from the authors. Supplementary information Supplementary data are available at Bioinformatics online.

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