Systematic drug target selection insights
Open Targets (opentargets.org) scores 74 out of 100 on Brand Analyzer, placing it in the Brand-Ready tier among Healthcare brands, Technology brands, Education brands, in the 38th percentile of 13204 Healthcare brands.
Open Targets is a public-private research partnership headquartered at the Wellcome Genome Campus in Cambridge, United Kingdom, founded in 2014. It generates evidence on the validity of therapeutic targets through genome-scale experiments and analysis, aiming to improve drug target identification and prioritisation. The initiative provides a freely-available, open-source platform that aggregates multiple public data sources, offering pre-competitive, causally-linked evidence connecting genetic and genomic targets to diseases for scientists and pharmaceutical/biotech organizations engaged in drug discovery.
| Brand Name | Open Targets |
|---|---|
| Domain | opentargets.org |
| Industry | Healthcare, Technology |
| Founded | 2014 |
| Headquarters | Wellcome Genome Campus |
| Main Competitors | Pharos, DrugBank (90/100), ChEMBL (63/100), GeneCards (70/100), DisGeNET, BenevolentAI (74/100) |
Moz Domain Authority 41/100 vs category average 41 / leader 95 - a strong backlink profile, so AI systems frequently encounter mentions of the brand.
Ranked #134,881 on the Tranco list of most-visited sites - modest traffic makes the brand easy for AI models to overlook.
No Wikipedia presence was found - a major gap, since Wikipedia is among the most heavily weighted sources in AI training data.
The homepage meta description reads "Open Targets is a public-private initiative to generate evidence on the validity of therapeutic targets based on genome-scale experiments and analysis." (Meta description: 21 words (ideal length). No OG description (falling back to meta)) - this is the summary AI engines are most likely to quote.
Structured data on the homepage: No structured data or Open Graph tags detected. Adding Organization and FAQ schema would further help AI crawlers parse the brand's identity.
AI-crawler access: robots.txt present, no AI bot restrictions - robots.txt controls whether engines like GPTBot and ClaudeBot can read the site at all.
Social footprint: verified profiles on LinkedIn, GitHub, X (Twitter), Instagram, YouTube; no detected presence on Facebook - consistent profiles reinforce the brand's identity across the web.
0 Reddit mentions - community discussion signals real-world reputation to AI models.
Live results from asking a general-purpose AI assistant about the brand, checked October 2026.
When asked "What is Open Targets?", Claude could identify the brand as of October 2026. Open Targets is a public-private partnership that integrates genetic, genomic, and chemical data to systematically identify and prioritize drug targets for disease, using data from GWAS, gene expression, and other sources. Best known for Its open-access Open Targets Platform used for target identification and validation in drug discovery.
When asked "Best brands similar to Open Targets?", Claude would recommend Open Targets as of October 2026. It's a widely used, respected resource in biomedical research and drug discovery for connecting genetics to disease targets, so it would naturally come up for target identification tools.
Common questions people ask in Google, ChatGPT, Claude, Gemini, Perplexity, and other AI search engines.
Open Targets's main competitors in Healthcare: DrugBank (90/100), ChEMBL (63/100), GeneCards (70/100), BenevolentAI (74/100).
Sources: Brand Analyzer scan
Open Targets offers an open-source, freely-available platform that aggregates genome-scale data from multiple public sources to generate causally-linked evidence between therapeutic targets and diseases. This platform supports systematic identification and prioritisation of drug targets for use in pharmaceutical and biotech research and development.
Sources: Open Targets official site
Open Targets is a public-private research initiative based at the Wellcome Genome Campus in Cambridge, United Kingdom, founded in 2014. It is a consortium involving academic, industry, and nonprofit partners that generates evidence on the validity of therapeutic targets using genome-scale experiments and analysis. It operates as an open, pre-competitive collaboration aimed at improving the process of identifying and prioritising drug targets for disease treatment.
Sources: Open Targets official site
Open Targets has limited AI-search visibility, scoring 34/100 on Brand Analyzer's AI visibility composite (visibility 26, trust 29, recommendation 53). This estimates how likely AI engines like ChatGPT, Claude, Gemini and Perplexity are to know, trust, and recommend the brand.
Sources: Open Targets official site
Open Targets was founded in 2014. Open Targets operates in the Healthcare category. It is analyzed by Brand Analyzer across seven brand dimensions and AI-search visibility.
Sources: Wikidata
Open Targets is headquartered in Wellcome Genome Campus. Open Targets operates in the Healthcare category.
Sources: Wikidata
Open Targets generates evidence connecting genes and genomic targets to diseases by aggregating and analysing large-scale genetic, genomic, and experimental data from multiple public sources. It systematically evaluates the validity of potential therapeutic targets to help prioritise them for drug discovery. The organisation provides an open, freely-available platform that presents this causally-linked target-disease evidence to researchers and organisations involved in developing new treatments.
Sources: Open Targets official site
Open Targets scores 53/100 on Brand Analyzer's AI recommendation signal, indicating it is reasonably likely to be surfaced when AI assistants like ChatGPT suggest Healthcare options. Recommendation depends on crawlability, structured data, and category authority.
Sources: Open Targets official site
Open Targets is known for its open-access platform that aggregates genome-scale data to produce causally-linked evidence between therapeutic targets and diseases. It is recognised as a public-private partnership model in drug discovery, bringing together academic institutions, pharmaceutical companies, and nonprofit organisations to pre-competitively share target validation data that supports systematic drug target identification and prioritisation.
Sources: Open Targets official site
Open Targets can improve AI discoverability by strengthening structured data (Organization and FAQ schema), maintaining an accurate Wikipedia/Wikidata entity, earning authoritative citations, and keeping content crawlable for AI bots. Brand Analyzer measures these as visibility, trust, and recommendation signals.
Sources: Open Targets official site
Open Targets is used by scientists, academic researchers, and pharmaceutical and biotechnology organisations involved in drug discovery and therapeutic target identification. These users rely on the platform's aggregated genomic and experimental evidence to evaluate and prioritise potential drug targets during early-stage research and development.
Sources: Open Targets official site
Open Targets is used widely because it offers a freely-available, open-source platform that consolidates data from multiple public sources into a single system for identifying and prioritising drug targets. Its pre-competitive, collaborative model-backed by a consortium of partner institutions-provides scientists and biotech/pharmaceutical organisations with causally-linked target-disease evidence that streamlines early-stage drug discovery research.
Sources: Open Targets official site
Open Targets scores 34/100 for AI visibility - how well ChatGPT, Claude, Perplexity and Google AI Overviews can discover, identify and cite the brand.
Open Targets has a weak AI-visibility profile at 34/100 - a proxy for how readily ChatGPT, Perplexity and Google's AI Overviews can recognise and cite it. Its strongest area is recommendation likelihood (53/100) and its weakest is visibility (26/100). It benefits from a Wikidata knowledge-graph entry, a global traffic rank of #134,881 (Tranco) and a Moz Domain Authority of 41/100. The main gaps holding it back: no clear Wikipedia entity to anchor it in LLM training data, thin schema.org structured data and no llms.txt to steer AI to its best pages. In a live check, Claude could already identify Open Targets from memory (October 2026) - a sign these signals are paying off.
The score combines seven dimensions - name quality, digital presence, visual identity, messaging clarity, trust foundation, AI discoverability, and brand authority - into a single objective benchmark.
Ranked closest to Open Targets: Oneview Healthcare (74/100), Olive Fertility Centre (74/100), Ordre professionnel de la physiothérapie du Québec (OPPQ) (74/100), General Optical Council (GOC) (74/100).
A step up - brands to learn from: Zymo Research (81/100), Zurich Insurance Group (81/100).
Category leader: Booksy (96/100).
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