12 months ago by
UK, Hinxton, EMBL-EBI
Not sure what you mean by "standard pipeline for drug target identification"??!?! I'd recommend this review for more details. Lately, there is a lot of focus on looking at genetics as genetic variants (both common and rare) seems to be a good predictor for approved drug indications (see the original paper by Nelson et al in Nature Genetics). In addition to genetics (and functional genomics) now used to guide drug target identification, there is a lot of hype on machine learning algorithms (and other types of AI) as well both for new discovery and repurposing opportunities. Check this perspective piece.
In addition to the ideas provided above by @dsull, you may also want to check Open Targets Platform and Open Targets Genetics, easy to use resources for target identification with a variety of integrated data from germline variants, to somatic mutations, drugs in clinical trials (or already approved), differential mRNA expression, text mining, functional genomics, and gene essentiality data using CRISPR-CAS9 in cancer cell lines...
These resources are part of the Open Targets public-private partnership and the gene essentiality data is akin to the DepMap at the Broad, so much so that our Sanger colleagues have just published a comparison of the gene essentiality data with our colleagues at the Broad Institute showing a remarkable agreement between the two datasets
Note that for both resources, Open Targets Genetics and Open Targets Platform, there are programmatic ways to access the data, in addition to the easy-to-use GUI. Check the docs for the Open Targets Platform REST API and Open Targets Genetics GraphiQL.