The Pharmome Map: A Comprehensive Public Dataset for Drug-Target Interaction Modeling

Why It Matters

A near‑complete drug‑target activity matrix dramatically speeds AI‑driven discovery and safety assessment, giving pharma and academia a shared, high‑quality foundation for innovation.

Key Takeaways

• 1,397 FDA drugs screened across three major target families
• Dataset publicly available, updated every two months
• Covers >50% of small‑molecule drug targets
• Enables AI modeling of polypharmacology and adverse events
• 2026 expansion will triple GPCR and kinase coverage

Pulse Analysis

The pharmaceutical landscape has long suffered from a fragmented view of drug‑target interactions, with most databases offering only primary targets and a handful of off‑targets. This sparsity hampers predictive modeling, safety profiling, and the identification of novel therapeutic opportunities. By delivering quantitative activity measurements for every combination of 1,397 approved small molecules and three high‑impact target classes, the pharmome map fills a critical data void, creating a dense matrix that researchers can treat as a reliable foundation for computational studies.

EvE Bio’s approach emphasizes consistency and scalability. Nuclear receptors are profiled with biochemical co‑factor recruitment assays, GPCRs with cell‑based agonist/antagonist screens, and protein kinases with competition‑based inhibition assays. All assays follow a single‑format pipeline, ensuring comparable readouts across classes. The bi‑monthly public releases, coupled with detailed metadata, make the dataset immediately usable for machine‑learning pipelines, from structure‑activity relationship models to adverse‑event prediction engines. Its open‑access nature also encourages community‑driven validation and extension.

The implications extend beyond academic curiosity. Pharma companies can leverage the map to de‑risk pipelines by spotting hidden off‑target liabilities early, while clinicians may gain insights into polypharmacy effects through comprehensive activity profiles. Regulatory bodies could use the data to refine safety surveillance frameworks. With a planned 2026 expansion that will triple GPCR and kinase coverage and introduce biased‑signaling measurements, the pharmome map is poised to become an indispensable resource for next‑generation drug discovery and precision medicine.

The Pharmome Map: a comprehensive public dataset for drug-target interaction modeling

Published November 18 2025

Authors: Elaine McVey Houskeeper (eamcvey), Hugging Science, Georgia Channing (cgeorgiaw)

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The missing pharmome map

Therapeutic drugs are ubiquitous, with billions of prescriptions written worldwide every year. In the last 30 days, half of Americans have taken at least one prescription medication, and a quarter have taken at least three (CDC). Despite this, we know relatively little about the scope of effects these drugs have on the human body. Pharmaceutical companies focus their efforts on making new drugs that are safe and effective, typically with a single “target” in mind. The drug development process does not include comprehensively understanding all the effects a drug may have with other proteins it may encounter in the body.

Imagine a matrix of every approved drug against every potential protein a drug can act on (the “human druggable genome”). Until now, our knowledge of drug activities has been an incredibly sparse version of this matrix. We often – but not always! – know the primary target of a drug and what effect it has on that targeted protein. Sometimes we know about a handful of other “off‑target” effects. But most of the drug‑target matrix is a mystery.

Imagine instead that we have a measurement for every single one of these drug‑target combinations that either confirms the drug is inactive at a particular target, or quantitatively characterizes its activity. This is the pharmome map.

What could we do if we had this map? Understanding all the effects of a drug means we can address questions such as:

• What patterns of activity are associated with particular adverse events (AE modeling)?

• Is this drug’s mechanism of action simply via the known “on‑target” activity, or are its effects driven by interactions across multiple targets (polypharmacology)?

• What do patterns of activity suggest about the effects of drug combinations (polypharmacy)?

• What drugs act on targets that might make them suitable for treating new indications (drug repurposing)?

• Can we predict activity patterns across targets for novel compounds? (structure‑activity relationship modeling)

While the pharmome map has been sparsely known to this point, the breadth and depth of data available to join to the pharmome map is vast: clinical trials (ClinicalTrials.gov), adverse‑event surveillance (FAERS), individual health records (UK Biobank), many –omics databases. A complete pharmome map unlocks new ways to understand these outcomes and relate them back to drug activity.

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Mapping is underway!

EvE Bio is a non‑profit (a Focused Research Organization under Convergent Research) that is generating the pharmome map and putting it in the public domain. EvE develops assays in a single format for the members of each target class, then carries out a quantitative high‑throughput screening and profiling process that provides the final measurements. By approaching dataset creation as the primary goal, EvE is able to provide the type of comprehensive and consistently generated dataset that is ideal for machine learning.

This public dataset is already the largest of its kind, and is actively expanding with new data added every other month.

EvE is currently focused on the portion of the pharmome map representing a 1,397‑member compound library, primarily composed of FDA‑approved small‑molecule drugs, measured against key classes of drug targets. These target classes were selected because they are therapeutically relevant, druggable by small molecules, and addressable at scale by in‑vitro assays. The three target classes included are:

• Nuclear receptors (NRs)

• 7‑transmembrane receptors (7TMs, aka GPCRs)

• Protein kinases (PKs)

Collectively these cover the intended targets for more than half of FDA‑approved small‑molecule drugs. Small‑molecule drugs are those typically available in traditional pill form, such as statins, tamoxifen, and metformin. They are well suited to high‑throughput screening and profiling approaches.

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Target Classes

Each target class plays a critical role in physiology and pharmacology, and each is addressed with a different assay format that is considered best suited to high‑throughput screening for physiologically relevant activity. While the same types of response measures are collected across classes, understanding the uniqueness of each target class will inform data usage. (Details beyond what is included here can be found on EvE Bio’s methods site.)

Nuclear receptors (NRs) – Directly regulate gene expression, controlling which proteins get created and influencing the long‑term behavior of a cell. This is a small (< 50 members) but highly impactful receptor class, representing the targets for more than 10 % of approved small‑molecule drugs. NRs are activated by ligand binding; ligand‑binding domains have collectively evolved to bind a diverse set of small molecules. Drugs can be full or partial agonists (increasing activity), antagonists (blocking agonism), or inverse agonists (reducing basal activity). NR activity is measured with biochemical co‑factor recruitment assays that reflect conformational changes induced by ligand binding. These assays are separately configured for agonist and antagonist modes.

7‑transmembrane receptors (7TMs / GPCRs) – Sense a wide variety of extracellular signals and translate them into intracellular responses, effectively telling the cell what’s happening around it. More than a third of FDA‑approved drugs target 7TMs, covering a range of therapeutic areas. 7TMs are a large target class that has evolved to sense a diversity of molecules, making them exceptionally druggable. They have multiple binding sites with diverse ligand possibilities, allowing selective activation (via biased agonists) or control of agonism/antagonism. Because 7TMs are on the cell surface, drugs need not cross the cell membrane to access them. 7TM activity is measured with cell‑based assays configured for agonist and antagonist modes.

Protein kinases (PKs) – Enzymes that catalyze phosphorylation, controlling many molecular “switches” within cells. These switches regulate critical processes, enabling complex feedback loops, cascades, and signal integration. PKs are a newer and rapidly growing set of targets for FDA‑approved drugs, especially relevant to cancer where mutations can lead to dysregulated activation. Disease‑relevant mutations are included in the pharmome map wherever possible. PK activity is measured with biochemical competition‑based ligand‑binding assays in a single inhibition mode.

In 2026, the number of 7TM and PK targets in the EvE pharmome mapping dataset will increase ~3×. This will include the addition of G‑protein as well as β‑arrestin data for 7TMs, which will allow for modeling of biased signaling via these two pathways. (Modern pharmacology experts consider the quantification of biased signaling a key opportunity for improved drug design.)

In addition to NRs, 7TMs, and PKs, the data includes measurements of cell viability for each compound (labeled as target class “Viability”), based on an assay that measured ATP produc… (text truncated).