Drug target identification in minutes, not months.
Stop assembling candidate lists by hand. Describe the target you are looking for in plain English. Nuclens screens 15,000+ oncology targets against public evidence and hands you a ranked, fully-sourced shortlist of novel therapeutic targets before your coffee gets cold.
Free to search and rank. Pay only when you generate a full decision report.
What is drug target identification?
Drug target identification is the first step of drug discovery: finding a molecule, usually a protein, whose modulation by a drug can treat a disease. Scientists combine human genetics, functional genomics, expression data and the literature to name candidates. AI target discovery platforms such as Nuclens integrate that evidence automatically and rank targets against a program’s criteria.
Target discovery is mostly tab-switching. We automated the tabs.
A typical target identification project means opening the same six databases for hundreds of genes. Here is what that looks like, and what it looks like with Nuclens.
| Task | The manual way | With Nuclens |
|---|---|---|
| Build a candidate list | Query Open Targets, export, filter by hand in a spreadsheet | Type your thesis. Nuclens filters 15,000+ targets deterministically |
| Check accessibility | Look up each protein in UniProt, one tab at a time | Cell-surface, secreted or intracellular status on every target |
| Check tumor expression | Browse Human Protein Atlas pathology pages gene by gene | Protein expression across 20 cancer types, precomputed |
| Read the literature | Dozens of PubMed searches, hundreds of abstracts | AI-extracted evidence with the PMIDs attached |
| Map the clinic | Search ClinicalTrials.gov target by target | Clinical stage and active trials per target |
| Result | A spreadsheet, weeks later | A ranked, sourced shortlist in under a minute |
What Nuclens screens, every time you ask.
Example: in pancreatic cancer, 4,357 cell-surface proteins show detectable tumor staining in Human Protein Atlas immunohistochemistry. See the ranked pancreatic cancer targets →
AI target discovery in three steps.
Describe what you are looking for
“Cell-surface targets in pancreatic cancer that internalize and have no approved drug.” Plain English, or hard filters if you prefer.
Nuclens screens the whole catalog
Deterministic filters fix the candidate pool, then a model tuned for target discovery scores each candidate across six weighted evidence dimensions.
Get a shortlist you can defend
Every target comes with its scorecard and sources. Open any data point in one click, then generate a full decision report for the ones that matter.
The six ways scientists find new drug targets, and where AI speeds each one up.
Drug target identification is the first step of drug discovery: finding a protein whose modulation can change the course of a disease. Most programs combine several of these approaches.
Human genetics
GWAS, rare-variant studies and Mendelian randomization link genes to disease in people. Targets with human genetic support are about 2.6 times more likely to succeed in the clinic (Minikel et al., Nature 2024).
Functional genomics
CRISPR and RNAi screens reveal which genes cancer cells depend on. DepMap has screened more than a thousand cancer cell lines.
Expression profiling
Comparing tumor with normal tissue (RNA, protein, single-cell) finds proteins that mark disease. It is the core signal for antibody, ADC and radioligand targets.
Literature & knowledge graphs
Decades of findings sit in papers and curated databases. Mining them surfaces links nobody has put together yet.
Phenotypic screening
Find a compound that works, then work backwards to its target with chemoproteomics or genetic deconvolution.
AI target discovery
Machine learning and LLMs integrate all of the above, ranking targets on the combined evidence instead of one signal at a time.
Target discovery vs. target identification. The terms are often used interchangeably. Strictly, target discovery is the broad search for disease-relevant biology, and drug target identification is naming the specific proteins worth pursuing. Both are followed by target prioritization (choosing among candidates) and target validation (proving the target drives disease).
Finding novel therapeutic targets. "Novel" usually means little clinical precedent, and that is the trade-off. Well-validated targets are crowded; novel ones carry more biological risk. Nuclens lets you sort by novelty and see exactly how much evidence each novel target has, so you can pick your level of risk deliberately.
Built for modality-specific discovery. Nuclens is purpose-built for radioligand therapy and cell-surface oncology targets, where the questions are specific: can a ligand reach it, is it on the tumor and not the kidney, does it internalize? A generic target-ID tool won't answer those. Nuclens does.
Give your team its weeks back.
Replace the week of database work at the start of every project with a query. Spend your time judging candidates, not collecting them.
Get a sourced, reproducible first pass across the whole catalog to compare against your own pipelines and hypotheses.
See which targets are crowded and which are open before a partnering meeting, with the evidence attached.
Common questions
What is drug target identification?
Drug target identification is the process of finding a molecule, usually a protein, whose modulation by a drug can treat a disease. It combines human genetics, functional genomics, expression data, literature and increasingly AI to name candidates worth pursuing.
How do you identify a drug target?
Start from disease biology, then gather evidence that a protein is causally linked to the disease (genetics, CRISPR screens), that it is expressed where it matters (tumor vs. normal tissue) and that your modality can reach it. Nuclens assembles that evidence for 15,000+ oncology targets automatically and ranks them against your criteria.
How is AI used in target discovery?
AI integrates evidence that used to be reviewed one database at a time: genetic association, expression, localization, essentiality, literature and clinical activity. It then ranks candidates on the combined picture. Nuclens also uses language models to read PubMed abstracts and extract evidence such as internalization, always with the source PMIDs shown.
How long does target identification take?
Done manually, a first-pass target landscape typically takes weeks of desk research. With Nuclens, the first pass takes under a minute, leaving your team’s time for expert judgement and experiments.
Which data sources does Nuclens use?
Open Targets, UniProt, the Human Protein Atlas, ClinicalTrials.gov, DepMap and PubMed. Every value is shown with its source and version so you can verify it.
Does Nuclens work outside oncology or radioligand therapy?
Nuclens is built for oncology targets and tuned for radioligand therapy, where cell-surface access, tumor expression and internalization decide success. The same evidence is useful for antibody and ADC programs. It does not model small-molecule binding pockets.
Your next target is already in the data.
Run your first target identification query free. No credit card, no sales call, results in under a minute.