Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale.
Reveals both expected and unexpected similarities that may be tested by examining the ‘off-target’ activities of the ligands themselves. SEA is a web application that reports proteins based on the set-wise chemical similarity among ligands. This method can be used to rapidly search large compound databases and to build cross-target similarity maps.
Differentiates the molecular targets or mediators of the treatment response from hundreds of additional genes that exhibit expression changes. MNI employs a machine-learning approach based on multiple regression to the training data to construct a statistical model of regulatory influences of genes on one another. It was used to determine drug targets and disease mediators based on gene-expression data from yeast, humans, bacteria and other organisms.
A data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. Open Targets platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. Open Targets platform provides free data access through your web browser or through an API (Application Programming Interface).
Automates the high throughput in silico vaccine candidate prediction process for the identification of putative vaccine candidates against the proteome of bacterial pathogens. VacSol rapidly and efficiently screens the whole bacterial pathogen proteome to identify a few predicted putative vaccine candidate proteins. It saves computational costs and time by efficiently reducing false positive candidate hits. The results do not depend on any universal set of rules and may vary based on the provided input.
Provides enrichment analysis tools for identifying chemical-associated Gene Ontology (GO), pathway (KEGG and Reactome) and Disease Ontology (DO and DOlite) based on a hypergeometric distribution. ChemDIS is a chemical-disease inference system based on chemical-protein interactions. By integrating the chemical-protein interactions and protein-disease interactions, the diseases associated with a given chemical can be inferred from the chemical-protein-disease relationship. ChemDIS is expected to be a useful chemical-disease inference system for assessing potential risks associated with environmental chemicals.
Allows you to predict the targets of a small molecule. Using a combination of 2D and 3D similarity measures, it compares the query molecule to a library of 280'000 compounds active on more than 2000 targets of 5 different organisms.
Combines kernel-based nonlinear dimensionality reduction, matrix factorization and binary classification for predicting drug-target interaction networks using only chemical similarity between drug compounds and genomic similarity between target proteins. KBMF is a fully probabilistic formulation for drug-target interaction network inference. We show its performance on four benchmark datasets using three experimental scenarios with practical importance: (i) exploratory data analysis using low-dimensional projections, (ii) predicting interactions for the out-of-sample drug compounds and (iii) predicting unknown interactions of the given network. KBMF can make use of multiple side information sources about the objects (both rows and columns) and be applied in various scenarios including recommender systems, interaction network modeling, multilabel classification, and multiple output regression.
Assists users with the hit identification and target prediction of chemical screenings. HitPick is a web application that permits to detect bioassay hits using the B-score method. It also predicts targets of a chemical of interest using a new integrative approach that combines 1-nearest-neighbor (1NN) similarity searching and a machine-learning method.
Provides a scaled random forest protein target prediction protocol. PIDGIN is a method for the extraction of inactive compounds from PubChem repository. This algorithm enables the oversampling of additional inactive compounds for targets with insufficient number of inactive compounds. This method was validated both through cross-validation and an external validation set.
A multimodal web interface that presents the data from the Target Central Resource Database (TCRD) which collates many heterogeneous gene and protein datasets. Pharos serves as entry point into the druggable genome. TCRD integrates a wide array of knowledge and data types about genes, proteins and small molecules collected and processed from numerous resources. It includes text-mined bibliometric associations and statistics from the biomedical and patent literature, mRNA and protein expression data, disease and phenotype associations, bioactivity data, drug target interactions, and processed datasets about the functions of genes and proteins from 66 resources organized into 114 datasets imported from the Harmonizome. Pharos application provides facile access to all data types collected. Given the complexity of the data surrounding any target, efficient and intuitive visualization has been a high priority, to enable users to quickly navigate and summarize search results and rapidly identify patterns.
Allows prediction of unknown drug–target interaction networks from various types of biological data. DINIES is a web server that provides two options: DINIES Search for exploring pre-calculated drug–target interaction networks that were predicted with available data in KEGG or other databases and DINIES Prediction with a “simple” and an “advanced” mode. The flexibility of the input data, allows analysis of drug–target interaction networks in various aspects.
Predicts possible binding targets of a small chemical molecule. Unlike previous approaches that screen against a specific class of targets or a limited number of targets, idTarget screen against nearly all protein structures deposited in the Protein Data Bank (PDB).
A web service enabling drug developers to carry out network pharmacology-based prediction and analysis by integrating results from structural biology with systems biology. Its user-friendly GUI interface simplifies essential operations for large-scale screening. Using the predictive docking approach, systemsDock can test a large number of target proteins with good prediction accuracy. This will reduce the number of tests for bioassay. Together with a curated pathway map, systemsDock helps to comprehensively characterize the underlying mechanism of a drug candidate and to interpret its cascading effects, improving the prediction of drug efficacy and safety.
Analyzes quantitative structure-activity relationships in protein family. WebProAnalyst enables automated database search for sequences of such proteins whose predicted activities meet specific requirements. This software allows researchers to seek correlations between protein activity and physiochemical characteristics in queried sequences. Its use is appropriate for computational resolution of proteomics dilemmas like activity-based protein profiling.
Provides data-derived statistical network models for 8 human cancers. CL contains several functions for biological interpretability of the network models, such as pathway analysis, drug-target recommendations, survival network analysis; and candidate gene selection. Cancer Landscapes is also a community server that enables you to mark and comment predictions of interest, for instance to plan experiments with collaborators.
Uses the measurements of gene expression in a given condition as reference and calculates the signaling activity of all the signaling circuits represented in the pathways. PathAct is a web server that assess how interventions over genes can affect to signaling pathways and to the cell functionalities triggered by them. This web app implements improved robust models of signaling pathways within an advanced graphical interface that provide a unique interactive working environment in which potentially actionable genes, that could eventually become drug targets, can be easily assayed alone or in combinations.
A web-based application for analysis of drug effects. It provides an intuitive interface to be used by anybody interested in leveraging microarray data to gain insights into the pharmacological effects of a drug, mainly identification of candidate targets, elucidation of mode of action and understanding of off-target effects. The core of Galahad is a network-based analysis method of gene expression. As an input, Galahad takes raw Affymetrix human microarray data from treatment versus control experiments and provides quality control and data exploration tools, as well as computation of differential expression. Alternatively, differential expression values can be uploaded directly. Using these differential expression values, drug target prioritization and both pathway and disease enrichment can be calculated and visualized. Drug target prioritization is based on the integration of the gene expression data with a functional protein association network.
Creates constraint-based models of metabolism, load existing models, export models, and run analyses on these models to predict the production of desired compounds by microbes under genetic manipulations. MOST implements GDBB (Genetic Design through Branch and Bound) in an intuitive user-friendly interface with Excel-like editing functionality, as well as implementing FBA (Flux Balance Analysis). The updated version implements visualization of models if metabolites in the model are identified with KEGG ids or ChEBI ids.