For biopharma

Get to defensible answers sooner

Work across biology, clinical evidence, and competitive context in one place, with every connection traced back to its source.

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Scientific workflows

Landscape & whitespace analysis Target diligence Modality-fit target discovery Competitive clinical landscape Efficacy & safety benchmarking

Connected capabilities

Proprietary data mapping AI agent grounding Competitive landscape monitoring AI/ML model training

Landscape & whitespace analysis

See where the real opportunities are

Map a disease, indication, drug class, company, or space to see where activity is concentrated, where opportunities remain, and why programs failed or stalled.

Know what’s actually undrugged. Explore a target graph dense enough to trust the whitespace it reveals, with 49,000+ source-cited drug-protein interactions representing more than 3× the coverage of ChEMBL and TTD combined.

Make whitespace measurable. Quantify untargeted proteins across four categories in a single, reproducible analysis, replacing weeks of manual counting that's stale before it's done.

Show me the drugs investigated for MASH and the targets they hit, and flag targets linked to the disease but with nothing in development.

disease drug target TTD development status Open Targets association score protein family

Target diligence

Know the target before you back the program

Evaluate more targets without sacrificing the depth of your diligence. Bring together target biology, development history, and safety liabilities in one place, instead of piecing evidence together across sources.

Find safety liabilities at the target level. Assess 1.5M+ drug interactions, 167,000+ adverse effects, 9,900+ contraindications, and 2,200+ boxed warnings across drugs acting on your target of interest.

See every drug that acts on the target. Compare every drug that has hit the target by action type, modality, and development status to see what progressed and what stalled.

What safety liabilities are associated with JAK1?

target drug boxed warning adverse effect approval status

Modality-fit target discovery

Find the targets your modality was built for

Continuously surface targets where your modality could address the limitations of existing drugs, using connected pharmacology, safety, and metabolic data instead of one-off analyses.

See where incumbent drugs fall short. Compare half-life, clearance, bioavailability, Cmax, AUC, elimination, interactions, and contraindications across approved drugs.

Explore every reported value. Get every reported value for a drug parameter, not just a single canonical number, so you can choose the one that best matches your context.

For a GalNAc-siRNA platform reaching hepatocytes, what’s the PK on the approved small-molecule incumbents, and which have a half-life under 24 hours?

modality drug half-life clearance bioavailability dose route population

Competitive clinical landscape

Know who you’re really competing with

Start from a target, mechanism, or indication and see the drugs and trials shaping the competitive landscape. Then dig into each competing asset by phase, sponsor, why a trial stopped, and safety signals.

Connect targets to the clinical pipeline. See the drugs that act on a target and follow them from preclinical development through clinical trials and approval, so you can understand how development around that target is progressing.

See every asset in a competitor’s pipeline. Get a complete view of competitor activity across mergers, acquisitions, and subsidiaries, with trial sponsors traced to the right parent company, so assets don’t disappear when ownership changes.

Who has worked on drugs against CCR2 and CCR5 in MASH?

target drug synonym trial sponsor parent company

Efficacy & safety benchmarking

Benchmark where your asset wins and loses

Compare your asset against competing programs to see what succeeded, what failed, and why. Then measure how its safety profile holds up against trials and approved drugs.

Learn from the programs that failed. See whether trials stopped for reasons such as efficacy, futility, safety, or business, with all free-text 'why stopped' reasons categorized, so you can explore patterns without blind spots.

Go beyond label-only safety data. Compare trial-reported adverse events with label-derived adverse effects, including incidence, dose, population, contraindications, boxed warnings, and drug interactions.

What’s in trials for MASH, and for any trials that stopped, was it the biology or the business?

disease drug trial trial status reported stop reason stop reason category

10/20
top pharma companies: 65,000+
academic citations: 150M+
structured data points: 20+
ontologies aligned

Connected capabilities

Take your workflows further

Proprietary data mapping

Connect internal compounds, targets, and programs to DrugBank’s biological, clinical, and competitive evidence, including entities never publicly disclosed. Runs on-premise or in your private cloud.

AI agent grounding

Ground AI agents in structured, source-linked DrugBank data so you get consistent, verifiable answers built on evidence, with every fact traced back to its source. Connect through MCP or GraphQL in your AI tools.

Competitive landscape monitoring

Turn landscapes into an ongoing monitor so you can see what changed without rebuilding the analysis. Track new trials, readouts, sponsors, and other activity, with the source and context for each change.

AI/ML model training

Train and power your models with structured DrugBank data that’s already resolved, connected, and mapped across 20+ external ontologies, so you can spend less time preparing data and more time using it.

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