Predicted protein targets (top 3)
| gene | UniProt | supporting neighbours | confidence | |
|---|---|---|---|---|
| ▸ | APP | P05067 | 20/20 | 1.00 |
| ▸ | MAPT | P10636 | 1/20 | 1.00 |
| ▸ | SNCA | P37840 | 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 | |
|---|---|---|---|---|
| Florbetapir SCHEMBL3356976 | 1.00 | APP (1.00) | APPMAPTSNCA | |
| Florbetapir F 18 SCHEMBL14826096 | 1.00 | APP (1.00) | APPMAPTSNCA | |
| Florbetapir F 18 SCHEMBL29517039 | 1.00 | APP (1.00) | APPMAPTSNCA | |
| Florbetapir F 18 SCHEMBL3356981 | 1.00 | APP (1.00) | APPMAPTSNCA | |
| SCHEMBL13863151 | 0.92 | APP (0.85) | APPMAPTSNCA | |
| SCHEMBL15307628 | 0.92 | APP (0.85) | APPMAPTSNCA | |
| SCHEMBL15731546 | 0.90 | APP (0.81) | APPMAPTSNCA | |
| SCHEMBL13827030 | 0.90 | APP (0.81) | APPMAPTSNCA | |
| SCHEMBL12318871 | 0.90 | APP (0.81) | APPMAPTSNCA | |
| SCHEMBL13827059 | 0.88 | APP (0.79) | APPMAPTSNCA |
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 107 patents — showing the first 20. claimed = in the patent's claims; disclosed = body only.
| Patent | Title | Assignee | Published | Priority | Filing | Country | Status |
|---|---|---|---|---|---|---|---|
| CN-120011879-A | Alzheimer's disease multi-modal classification method based on electroencephalogram, brain image and genetic information | 西北工业大学 | 2025-05-16 | — | — | CN | claimed |
| CN-116245833-B | Prediction method for brain multi-mode PET image SUVR | 复旦大学 | 2024-12-27 | — | — | CN | claimed |
| CN-116245833-A | Prediction method for brain multi-mode PET image SUVR | 复旦大学 | 2023-06-09 | — | — | CN | claimed |
| CN-114862774-A | PET image cross-modal reconstruction method and device based on deep learning | 浙江大学滨江研究院 | 2022-08-05 | — | — | CN | claimed |
| CN-114366824-A | Probe for isotope targeted imaging and preparation method and application thereof | 中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所 | 2022-04-19 | — | — | CN | claimed |
| CN-114202075-A | Guided multi-mode image genetics data feature analysis method | 河北工业大学 | 2022-03-18 | — | — | CN | claimed |
| CN-113978450-A | Anti-heeling path tracking game control method for commercial vehicle | 聊城大学 | 2022-01-28 | — | — | CN | claimed |
| CN-122071458-A | Light-emitting element and amine compound for light-emitting element | 三星显示有限公司 | 2026-05-22 | — | — | CN | disclosed |
| US-20260143965-A1 | LIGHT EMITTING ELEMENT, AMINE COMPOUND FOR THE LIGHT EMITTING ELEMENT, AND DISPLAY DEVICE INCLUDING THE LIGHT EMITTING ELEMENT | SAMSUNG DISPLAY CO LTD (KR) | 2026-05-21 | — | — | US | disclosed |
| US-12629094-B2 | System, software and methods of using software for predicting efficacy of alzheimer's disease treatments | THE REGENTS OF THE UNIVERSITY OF CALIFORNIA (US) | 2026-05-19 | — | — | US | disclosed |
| US-20260091109-A1 | Method for Treating Alzheimer's Disease | BIOGEN INT NEUROSCIENCE GMBH (CH) | 2026-04-02 | — | — | US | disclosed |
| US-20250384559-A1 | SYSTEM AND METHOD FOR SUBJECT-SPECIFIC AMYLOID POSITION EMISSION TOMOGRAPHY TRANSLATION USING CONDITIONED DIFFUSION-BASED GENERATIVE MODEL | TATA CONSULTANCY SERVICES LIMITED (IN) | 2025-12-18 | — | — | US | disclosed |
| EP-4663127-A1 | SYSTEM AND METHOD FOR SUBJECT-SPECIFIC AMYLOID POSITRON EMISSION TOMOGRAPHY TRANSLATION USING CONDITIONED DIFFUSION-BASED GENERATIVE MODEL | Tata Consultancy Services Limited (IN) | 2025-12-17 | — | — | EP | disclosed |
| WO-2022072738-A1 | SYSTEM, SOFTWARE AND METHODS OF USING SOFTWARE FOR PREDICTING EFFICACY OF ALZHEIMER'S DISEASE TREATMENTS | THE REGENTS OF THE UNIVERSITY OF CALIFORNIA (US) | 2022-04-07 | — | — | WO | disclosed |
| US-11292838-B2 | Method for generating antibodies against T cell receptor | MEDIGENE IMMUNOTHERAPIES GMBH (DE) | 2022-04-05 | — | — | US | disclosed |
| CN-114202075-A | Guided multi-mode image genetics data feature analysis method | 河北工业大学 | 2022-03-18 | — | — | CN | disclosed |
| US-20220064271-A1 | METHODS FOR TREATING TAUOPATHIES | WASHINGTON UNIVERSITY | 2022-03-03 | — | — | US | disclosed |
| US-20220051801-A1 | CLASSIFYING NEUROLOGICAL DISEASE STATUS USING DEEP LEARNING | THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK | 2022-02-17 | — | — | US | disclosed |
| CN-114010641-A | Methods for treating neurological disorders | 爱普制药有限责任公司 | 2022-02-08 | — | — | CN | disclosed |
| CN-113978450-A | Anti-heeling path tracking game control method for commercial vehicle | 聊城大学 | 2022-01-28 | — | — | CN | 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 (3 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-20260091109-A1 | Method for Treating Alzheimer's Disease | IGLV6-57, FCGR2A, BACE1 | APP 4/4885MAPT 64/4885SNCA 66/4885 |
| US-12629094-B2 | System, software and methods of using software for predicting efficacy of alzheimer's disease treatments | MAPT, APP, PSEN2 | APP 2/4885MAPT 1/4885SNCA 13/4885 |
| US-20260143965-A1 | LIGHT EMITTING ELEMENT, AMINE COMPOUND FOR THE LIGHT EMITTING ELEMENT, AND DISPLAY DEVICE INCLUDING THE LIGHT EMITTING ELEMENT | NR2E3, NR2E1, NR0B2 | APP 3323/4885MAPT 2692/4885SNCA 914/4885 |
“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.