Choosing the right biological target or a combination of targets is a fundamental task for any successful drug discovery project. All the subsequent efforts -- be it a small molecule hit identification, lead optimization, pharmacokinetic studies, or a clinical trial -- will just be as effective, at the end of the day, as was the initial decision to choose one target or another.
No wonder, why target identification and validation play central roles in the overall drug discovery process. The number of promising drug targets increases every year thanks to new scientific advances. Understanding the definition and range of drug targets is facilitated by web resources covering molecular mechanisms, modes of action and signaling network interactions, especially when the evidence results from the linkage of target with disease and specific experimental factor ontologies, as illustrated in the accompanying diagram on the "ETDO" architecture.
Below is a curated list of 36 online open access resources, with their accompanying explanatory publications (mostly free), all crafted around ETDO variations. They should prove useful for both educational and research purposes:
A searchable database of experimentally measured binding affinities, focusing chiefly on the interactions of proteins considered to be drug-targets with small, drug-like molecules. BindingDB contains 1,419,347 binding data, for 7,000 protein targets and 635,301 small molecules.
The Biological General Repository for Interaction Datasets is an open access database on protein, genetic and chemical interactions for humans and all major model organism species and humans. To facilitate network-based approaches for drug discovery, BioGRID now incorporates 27,501 chemical-protein interactions for human drug targets.
A cancer research and drug discovery knowledgebase that contains chemical and pharmacological data for over one million, bioactive, small molecule drugs and compounds corresponding to ~8 million pharmacological bioactivities as well as over 10 million calculated chemical properties intended to enable target selection and validation in drug discovery.
A confederated database of more than 1 million bioactivity values for over 400,000 compounds associated with 3,500 protein targets. A one-click user query can determine potential leads for a target, associated off-targets, and druggable targets in associated disease pathways.
A large-scale database of bioactive drug-like small molecules, it contains 2-D structures, calculated properties (e.g. logP, Molecular Weight, Lipinski Parameters, etc.) and abstracted bioactivities (e.g. binding constants, pharmacology and ADMET information). Target data can be searched via keyword, protein sequence search (BLAST), or by navigating the target classification hierarchy. Research reference.
Chemical Similarity Network Analysis Pull-down is a computational approach for compound target identification. 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. The CSNAP approach facilitates high-throughput target discovery and off-target prediction.
7. DGIdb 3.0
The Drug-Gene interaction database consolidates, organizes and presents drug-gene relationships and gene druggability information from papers, databases and web resources. It encapsulates multiple gene categories (e.g. kinases, G-protein coupled receptors) that are expected to be good drug targets.
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Topics: Emerging Technologies