Mapping genes to druggable interactions
DGIdb (dgidb.org) scores 73 out of 100 on Brand Analyzer, placing it in the Brand-Ready tier among Healthcare brands, Education brands, Technology brands, in the 34th percentile of 17325 Healthcare brands.
DGIdb (Drug Gene Interaction Database) is a research resource that aggregates and curates drug-gene interaction data from multiple sources into a single searchable database. It allows researchers to search candidate genes or drugs against the known and potentially druggable genome, supporting biomedical research, drug discovery, and pharmacological studies. It serves academic and scientific users rather than clinical or commercial markets.
| Brand Name | DGIdb |
|---|---|
| Domain | dgidb.org |
| Industry | Healthcare, Education |
| Main Competitors | DrugBank (90/100), ChEMBL (65/100), Open Targets (74/100), PharmGKB (73/100), Comparative Toxicogenomics Database (CTD) (54/100), Pharos |
Moz Domain Authority 35/100 vs category average 40 / leader 95 - limited third-party links, so AI systems rarely encounter mentions of the brand.
Ranked #4,061,636 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 "DGIdb, The Drug Gene Interaction Database, is a research resource that can be used to search candidate genes or drugs against the known and potentially druggable genome." (Meta description: 27 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.
Detected tech stack: Nginx.
Social footprint: verified profiles on GitHub, Instagram, YouTube; no detected presence on X (Twitter), LinkedIn, 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 DGIdb?", Claude could identify the brand as of October 2026. DGIdb (Drug Gene Interaction Database) is an open-access bioinformatics resource that aggregates and normalizes information on drug-gene interactions and druggable genes from various sources like publications, databases, and other web-based resources. It's widely used by research Best known for Curating and providing a searchable database of known and potential drug-gene interactions for research purposes.
When asked "Best brands similar to DGIdb?", Claude would recommend DGIdb as of October 2026. It's a well-known, frequently cited resource in genomics and pharmacology research for identifying drug-gene interactions, making it relevant for anyone looking for bioinformatics tools in this space.
Common questions people ask in Google, ChatGPT, Claude, Gemini, Perplexity, and other AI search engines.
DGIdb's main competitors in Healthcare: DrugBank (90/100), ChEMBL (65/100), Open Targets (74/100), PharmGKB (73/100), Comparative Toxicogenomics Database (CTD) (54/100).
Sources: Brand Analyzer scan
DGIdb offers a searchable database interface where users can look up genes or drugs to discover known and potential drug-gene interactions. It functions as a research tool/service rather than a commercial product, aggregating curated interaction data from multiple sources for use by the scientific community.
Sources: DGIdb official site
DGIdb, or the Drug Gene Interaction Database, is a research resource that provides a comprehensive, searchable database of known and potential drug-gene interactions. It aggregates data from multiple disparate sources, allowing researchers to search candidate genes or drugs against the known and potentially druggable genome.
Sources: DGIdb official site
DGIdb has limited AI-search visibility, scoring 25/100 on Brand Analyzer's AI visibility composite (visibility 13, trust 15, recommendation 58). This estimates how likely AI engines like ChatGPT, Claude, Gemini and Perplexity are to know, trust, and recommend the brand.
Sources: DGIdb official site
DGIdb curates and aggregates drug-gene interaction data from numerous sources into a single, searchable database. Researchers can query genes to find associated drugs or query drugs to find associated genes, helping identify druggable targets and candidate drugs for further study.
Sources: DGIdb official site
DGIdb scores 58/100 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: DGIdb official site
DGIdb is known for being a centralized, curated resource that consolidates drug-gene interaction data from many different databases and publications into one searchable platform, making it easier for researchers to explore the druggable genome without having to consult multiple disparate sources individually.
Sources: DGIdb official site
DGIdb 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: DGIdb official site
DGIdb is used by biomedical researchers, bioinformaticians, pharmacologists, and academic scientists who study gene-drug interactions and drug discovery. It serves the research community rather than clinical practitioners or commercial markets.
Sources: DGIdb official site
DGIdb is useful to researchers because it consolidates drug-gene interaction information from many disparate sources into a single searchable resource, saving time and effort compared to searching multiple separate databases. This makes it a convenient tool for identifying druggable targets and candidate drugs in biomedical and pharmacological research.
Sources: DGIdb official site
DGIdb scores 25/100 for AI visibility - how well ChatGPT, Claude, Perplexity and Google AI Overviews can discover, identify and cite the brand.
DGIdb has a weak AI-visibility profile at 25/100 - a proxy for how readily ChatGPT, Perplexity and Google's AI Overviews can recognise and cite it. Its strongest area is recommendation likelihood (58/100) and its weakest is visibility (13/100). It benefits from a Wikidata knowledge-graph entry, a global traffic rank of #4,061,636 (Tranco) and open access for AI crawlers (GPTBot, ClaudeBot, etc.). The main gaps holding it back: no clear Wikipedia entity to anchor it in LLM training data, a low Moz Domain Authority of 35/100 and thin schema.org structured data. In a live check, Claude could already identify DGIdb 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 DGIdb: Dermio (73/100), Dementia Society of America (73/100), Dial-dent (73/100), DigitalOwl (73/100).
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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