Graphic reads: Open Targets Platform, 24.03

Open Targets Platform 24.03 has been released!

Release Notes Mar 20, 2024

The latest release of the Platform — 24.03 — is now available at

Key points

In addition to our regular data updates:

Key stats

Metric Count
Targets 63,226
Diseases 25,817
Drugs 17,111
Evidence 17,317,290
Associations 7,802,260

Additional metrics are available on the Open Targets Community.

Direction of Effect

Direction of effect describes the relationship between a gene and a trait, including both the functional consequence of the perturbation on the gene, and how this affects the manifestation of the trait. 

We have implemented this analysis for eight sources of target-disease association evidence where there is enough information available to make an assessment. These are:

  • Open Targets Genetics
  • Gene Burden
  • ClinVar
  • ClinVar somatic
  • Gene2Phenotype
  • Orphanet
  • IMPC
  • ChEMBL

On the BRCA2 associations page, the new direction of effect assessment in the Gene Burden widget for breast neoplasm shows that the top variants are loss of function and confer risk, as represented by the downwards trending arrow and the exclamation point, respectively.

Direction of effect assessment is shown as two columns: one indicating the target directionality (loss of function, gain of function, or null), and one indicating the direction on trait (risk, protective, null). The Platform provides a direction of effect assessment for over 2.8 million evidence.

The assessment is tailored to each dataset; this is detailed in our target-disease evidence documentation.

Bioinformatician Juan María Roldán Romero introduces Direction of Effect assessments in this walkthrough looking at PCSK9 and hypercholesterolemia.

Project Score

Earlier this year, an Open Targets research team published an update to Project Score. Clare Pacini and the team at Open Targets, Wellcome Sanger Institute and collaborators annotated 930 cancer cell lines with multiomic data to generate a second-generation map of cancer dependencies (Score2) — the most comprehensive study of its type.

The team identified 370 priority drug targets for 27 cancer types in a truly data-driven approach, leveraging clinical-relevant transcriptional signatures, metabolic and proteomics data, protein-protein interaction networks and more.

This data has now been integrated into the Platform, and contributes evidence of target-disease associations as part of our Pathways and Systems Biology data type. 

Any Project Score prioritised target with priority score reaching 36.0 is included as independent evidence. Note that pan-cancer dependencies are excluded from the integration. Find out more in the Platform documentation.

A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization - PubMed
Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 …

Read the paper: Pacini et al. A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization (2024) Cancer Cell. DOI: 10.1016/j.ccell.2023.12.016

Data updates

Target safety

We used pharmacogenetics data that informs about adverse drug response as an additional source of information on target safety. This has doubled the number of targets with known adverse events in the Platform; 932 targets now have safety annotation.

Safety widget on the DPYD target profile page, highlighting safety events associated with DPYD. This is an example of new information in the Platform brought in by an Open Targets analysis of the data from PharmGKB.

The widget now highlights targets in which genetic variation is known to increase toxicology risk in the presence of drugs.

In addition, we now indicate whether a target is a direct target of the drug. This extra information can better inform target prioritisation.

Open Targets Platform 23.12 has been released!
This release introduces the Target Prioritisation view, which provides an assessment of target features considered when prioritising targets for drug discovery. We have also introduced a pharmacogenetics widget integrating data from PharmGKB.

Have you seen our new target prioritisation view? It was introduced in our 23.12 release — check out the blogpost to find out more!


We have expanded the pharmacogenetics data we integrated in our 2023 December release by including star alleles from PharmGKB, which represent key functional variants involved in drug responses.

The TPMT target profile page now contains pharmacogenetics information on the TPMT star alleles from PharmGKB. This is also an example of a gene which is not the direct drug target.

Cancer DepMap

This release integrates the 23Q4 version of DepMap (, which contains data from CRISPR knockout screens from Project Achilles, and genomic characterization data from the CCLE project. Besides updated analysis pipelines, this update also introduces 28 new genome-wide CRISPR screens to improve cancer dependency assessments.

In the Platform, DepMap data powers our Core Essentiality widget. In our target annotation pages, this indicates whether a target is a core essential gene: one unlikely to tolerate inhibition, and susceptible of causing adverse events if modulated. Find out more in the Platform documentation.

Cancer Gene Census

We have updated our Cancer Gene Census data to reflect their latest update (v99).

Part of the Wellcome Sanger Institute Catalogue of Somatic Mutations (COSMIC), the Cancer Gene Census aims to catalogue genes containing mutations that have been causally implicated in cancer.

Associations page for RAD50 in the Open Targets Platform. For all except one of the top eight associated diseases shown, Cancer Gene Census is the only source of evidence for the association.
RAD50 was included as a Tier 1 gene in the latest release of the Cancer Gene Census. This brings in new target-disease associations into the Platform, including ones for which the CGC is the first source of evidence.

Find out more about v99 on the COSMIC website.

User interface updates

Bibliography filter

On our entity annotation pages, users now have the option of filtering the bibliography data to a specific timeframe.

Bibliography widget in the Cystic Fibrosis profile page. The date filter has been changed to show only papers published between March 2019 and March 2024.

In related news, we were very excited that Open Targets L2G prediction data is now an Ensembl VEP plugin!  Read more on in the Ensembl 111 release notes.

As usual, please share any comments, questions, or suggestions on the Open Targets Community.

This post was updated on May 8, 2024 to include the Direction of Effect walkthrough video.