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.
Identifies potential target candidates for the given probe small molecules using pharmacophore mapping approach. PharmMapper is a freely accessed web-server designed to profil the potential secondary or side effects for a drug molecule in a different viewpoint from the regular chemogenetic method. It can also be used FOR mapping the regulation genomic network for an existing drug or a drug candidate.
Detects small molecules protein interactions at proteome-scale by using data from the Library of Integrated Network-Based Cellular Signatures (LINCS). Target2 can identify drug-target interactions encoded in correlations between cellular gene expression profiles from millions of knockdown and drug experiments. It utilizes an orthogonal, high-throughput, structure-based screen to refine the prediction results.
A computational approach for compound target identification based on network similarity graphs. Query and reference compounds are populated on the network connectivity map and a graph-based neighbor counting method is applied to rank the consensus targets among the neighborhood of each query ligand. The CSNAP approach can facilitate high-throughput target discovery and off-target prediction for any compound set identified from phenotype-based or cell-based chemical screens.
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.
Discovers new uses for existing drugs. DTI prediction uses complex network theory to predict drug-target interactions (DTI) from a drug-target network. It tends to yield high performance when additional information about attributes of drugs, targets, and their interactions is available. The tool provides real, reliable predictions (in contrast to pseudo evaluation) that will benefit the research community and pharmaceutical industry.
Integrates heterogenous drug similarities with protein interation network data to accurately predict drug-target relations. drugCIPHER proposes three linear regression models respectively using drug therapeutic similarity, chemical similarity and their combination as responses and network distance as predictors.
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.