Known targets — ChEMBL curated mechanism
The experimentally established mechanism targets of Argatroban. The predicted profile below is derived independently by chemical similarity — agreement is a validation signal, a miss is honest.
Predicted protein targets (top 7)
| gene | UniProt | supporting neighbours | confidence | |
|---|---|---|---|---|
| ▸ | F2 known ✓ | P00734 | 4/20 | 1.00 |
| ▸ | PRSS1 | P07477 | 2/20 | 1.00 |
| ▸ | F10 | P00742 | 2/20 | 1.00 |
| ▸ | LMNA | P02545 | 1/20 | 1.00 |
| ▸ | PLAT | P00750 | 1/20 | 1.00 |
| ▸ | PRSS2 | P07478 | 1/20 | 1.00 |
| ▸ | PRSS3 | P35030 | 1/20 | 1.00 |
Click a target to see other patent compounds predicted against it — the reverse direction, in place.
Similar compounds — the chemically nearest patent molecules
Nearest neighbours by Morgan-fingerprint cosine across the patent-compound collection, with each neighbour's top predicted target and the predicted targets it shares with this molecule.
| Compound | similarity | top predicted | shared targets | |
|---|---|---|---|---|
| Argatroban SCHEMBL135173 | 1.00 | F2 (1.00) | F2PRSS1F10LMNAPLAT | |
| Argatroban SCHEMBL29741503 | 1.00 | F2 (1.00) | F2PRSS1F10LMNAPLAT | |
| Argatroban SCHEMBL22974994 | 1.00 | F2 (1.00) | F2PRSS1F10LMNAPLAT | |
| Argatroban SCHEMBL18895186 | 1.00 | F2 (1.00) | F2PRSS1F10LMNAPLAT | |
| Argatroban SCHEMBL4375 | 1.00 | F2 (1.00) | F2PRSS1F10LMNAPLAT | |
| Argatroban SCHEMBL15019460 | 1.00 | F2 (1.00) | F2PRSS1F10LMNAPLAT | |
| Argatroban SCHEMBL29618936 | 0.99 | F2 (0.99) | F2PRSS1F10LMNAPLAT | |
| Argatroban SCHEMBL20787652 | 0.99 | F2 (0.99) | F2PRSS1F10LMNAPLAT | |
| SCHEMBL24209448 | 0.95 | F2 (0.90) | F2PRSS1F10LMNAPLAT | |
| SCHEMBL28440979 | 0.90 | F2 (0.84) | F2PRSS1F10LMNAPLAT |
Similarity is cosine over the 2,048-bit Morgan fingerprint (≈ Tanimoto). Identical fingerprints score 1.00.
Patent provenance — the patents this molecule appears in, and who filed them
Claimed or disclosed in 64 patents — showing the first 20. claimed = in the patent's claims; disclosed = body only.
| Patent | Title | Assignee | Published | Priority | Filing | Country | Status |
|---|---|---|---|---|---|---|---|
| CN-116541484-A | Multi-feature stock trend prediction method for combining emotion and stock event of stratagizers | 北京工商大学 | 2023-08-04 | — | — | CN | claimed |
| CN-121996384-A | FPGA-based adaptive hybrid scheduling accelerator and implementation method thereof | 哈尔滨工程大学 | 2026-05-08 | — | — | CN | disclosed |
| US-12618040-B2 | Product quality attribute measurement | GENZYME CORPORATION (US) | 2026-05-05 | — | — | US | disclosed |
| CN-120429106-A | Reconfigurable-based multi-scene intelligent terminal control method | 芯仓智能科技(苏州)有限公司 | 2025-08-05 | — | — | CN | disclosed |
| US-12340175-B2 | Automated classification of emotio-cogniton | ELABORATION, INC. (US) | 2025-06-24 | — | — | US | disclosed |
| CN-120044170-A | Product quality attribute measurement | 建新公司 | 2025-05-27 | — | — | CN | disclosed |
| CN-119990137-A | Text emotion analysis method and system based on prompt multi-scale learning | 南京邮电大学 | 2025-05-13 | — | — | CN | disclosed |
| US-12271694-B1 | Machine learning-based automated narrative text scoring including emotion arc characterization | EDUCATIONAL TESTING SERVICE (US) | 2025-04-08 | — | — | US | disclosed |
| CN-119514602-A | Role dialogue generation method, device, equipment and medium | 平安科技（深圳）有限公司 | 2025-02-25 | — | — | CN | disclosed |
| CN-114729916-B | Product quality attribute measurement | 建新公司 | 2025-02-14 | — | — | CN | disclosed |
| CN-110489522-B | Emotional dictionary construction method based on user score | 湖南大学 | 2022-04-12 | — | — | CN | disclosed |
| CN-105320960-B | Voting-based cross-language subjective and objective emotion classification method | 北京航空航天大学 | 2022-04-05 | — | — | CN | disclosed |
| CN-113476398-B | Stable and safe argatroban injection and preparation method thereof | 康普药业股份有限公司 | 2022-03-29 | — | — | CN | disclosed |
| CN-112380345-B | COVID-19 scientific literature fine-grained classification method based on GNN | 山东省计算中心（国家超级计算济南中心） | 2022-03-29 | — | — | CN | disclosed |
| US-11279041-B2 | Socially assistive robot | Dream Face Technologies, Inc. (US) | 2022-03-22 | — | — | US | disclosed |
| CN-114186047-A | Food safety public opinion analysis technology extraction method | 食品安全与营养（贵州）信息科技有限公司 | 2022-03-15 | — | — | CN | disclosed |
| CN-114169316-A | Financial market income prediction model construction method and device and electronic equipment | 中国工商银行股份有限公司 | 2022-03-11 | — | — | CN | disclosed |
| CN-114117057-A | Keyword extraction method of product feedback information and terminal equipment | 武汉TCL集团工业研究院有限公司 | 2022-03-01 | — | — | CN | disclosed |
| US-20220047213-A1 | Grouping Neuropsychotypes of Patients with Chronic Pain for Personalized Medicine | NORTHWESTERN UNIVERSITY | 2022-02-17 | — | — | US | disclosed |
| US-20180340163-A1 | RECOMBINANT SERINE PROTEASES | ACADEMISCH ZIEKENHUIS LEIDEN (NL) | 2018-11-29 | — | — | US | disclosed |
Patent text — is the patent's own abstract consistent with the prediction?
For each of this compound's patents that has machine-readable text (1 of them — usually the abstract, not the full specification), we ask MedCPT which protein the text reads most about, and where the chemistry-predicted target lands among 4885 human targets. A high rank means the patent's own wording is consistent with the prediction — a weak, independent signal, not proof of activity.
| Patent | Title | Text reads most about | Predicted target · text-rank |
|---|---|---|---|
| US-12618040-B2 | Product quality attribute measurement | PGF, PIGS, SRMS | F2 42/4885PRSS1 2131/4885F10 20/4885 |
“Text reads most about” is the patent abstract's nearest protein in MedCPT space (background-debiased). Only ~1.4% of patents have machine-readable text, so most compounds won't have this panel.