Predicted protein targets (top 13)
| gene | UniProt | supporting neighbours | confidence | |
|---|---|---|---|---|
| ▸ | KISS1R | Q969F8 | 6/20 | 0.56 |
| ▸ | BACE1 | P56817 | 6/20 | 0.52 |
| ▸ | OPRM1 | P35372 | 2/20 | 0.51 |
| ▸ | OPRD1 | P41143 | 2/20 | 0.51 |
| ▸ | BACE2 | Q9Y5Z0 | 2/20 | 0.51 |
| ▸ | NTSR2 | O95665 | 3/20 | 0.50 |
| ▸ | NTSR1 | P30989 | 3/20 | 0.50 |
| ▸ | NPY1R | P25929 | 1/20 | 0.49 |
| ▸ | NPY2R | P49146 | 1/20 | 0.49 |
| ▸ | NPY4R | P50391 | 1/20 | 0.49 |
| ▸ | VIPR1 | P32241 | 1/20 | 0.48 |
| ▸ | VIPR2 | P41587 | 1/20 | 0.48 |
| ▸ | OPRK1 | P41145 | 1/20 | 0.48 |
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 | |
|---|---|---|---|---|
| SCHEMBL30501948 | 0.94 | BACE1 (0.57) | KISS1RBACE1OPRM1OPRD1BACE2 | |
| SCHEMBL31548019 | 0.90 | BACE1 (0.61) | BACE1BACE2 | |
| SCHEMBL23885018 | 0.88 | BACE1 (0.54) | BACE1BACE2NTSR2NTSR1 | |
| SCHEMBL23885019 | 0.88 | BACE1 (0.54) | BACE1BACE2NTSR2NTSR1 | |
| SCHEMBL31614420 | 0.87 | BACE1 (0.46) | BACE1 | |
| SCHEMBL17867406 | 0.86 | BACE1 (0.57) | BACE1BACE2 | |
| SCHEMBL17867407 | 0.86 | BACE1 (0.57) | BACE1BACE2 | |
| SCHEMBL29438037 | 0.86 | BACE1 (0.60) | BACE1BACE2NTSR2NTSR1VIPR1 | |
| SCHEMBL30512659 | 0.85 | BACE1 (0.56) | BACE1BACE2 | |
| SCHEMBL30058129 | 0.85 | BACE1 (0.61) | BACE1BACE2 |
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 294 patents — showing the first 20. claimed = in the patent's claims; disclosed = body only.
| Patent | Title | Assignee | Published | Priority | Filing | Country | Status |
|---|---|---|---|---|---|---|---|
| US-20250283888-A1 | METHODS FOR PREDICTING THE USEFULNESS OF DISEASE SPECIFIC AMINO ACID MODIFICATIONS FOR IMMUNOTHERAPY | BIONTECH RNA PHARMACEUTICALS GMBH (DE) | 2025-09-11 | — | — | US | claimed |
| EP-4599845-A2 | METHODS FOR PREDICTING THE USEFULNESS OF DISEASE SPECIFIC AMINO ACID MODIFICATIONS FOR IMMUNOTHERAPY | BioNTech SE (DE) | 2025-08-13 | — | — | EP | claimed |
| EP-3635408-B1 | METHODS FOR PREDICTING THE USEFULNESS OF DISEASE SPECIFIC AMINO ACID MODIFICATIONS FOR IMMUNOTHERAPY | BioNTech SE (DE) | 2025-04-30 | — | — | EP | claimed |
| CN-119816320-A | Immunotherapy targeting novel antigenic peptides derived from tumor transposable elements in glioblastomas | 居里研究所 | 2025-04-11 | — | — | CN | claimed |
| US-12270813-B2 | Methods for predicting the usefulness of disease specific amino acid modifications for immunotherapy | BioNTech SE (DE) | 2025-04-08 | — | — | US | claimed |
| US-20250090648-A1 | NEXT GENERATION MRNA VACCINES | FUTR BIO LTDA. (BR) | 2025-03-20 | — | — | US | claimed |
| EP-4482520-A1 | NEXT GENERATION MRNA VACCINES | Futr Bio Ltda. (BR) | 2025-01-01 | — | — | EP | claimed |
| US-12153041-B2 | Methods for predicting the usefulness of proteins or protein fragments for immunotherapy | TRON-TRANSLATIONALE ONKOLOGIE AN DER UNIVERSITÄTSMEDIZIN DER JOHANNES GUTENBERG-UNIVERSITÄT MAINZ GGMBH (DE) | 2024-11-26 | — | — | US | claimed |
| CN-118557717-A | Anti-tumor vaccine combining diphtheria toxoid epitope with alpha GalCer agonist, and preparation and application thereof | 湖北理工学院 | 2024-08-30 | — | — | CN | claimed |
| EP-4411376-A2 | METHODS FOR PREDICTING THE USEFULNESS OF NEOANTIGENS FOR IMMUNOTHERAPY | BioNTech SE (DE) | 2024-08-07 | — | — | EP | claimed |
| WO-2023161715-A1 | NEXT GENERATION MRNA VACCINES | FUTR BIO LTDA. (BR) | 2023-08-31 | — | — | WO | claimed |
| EP-4171751-A2 | MHC MULTIMER EXPRESSION CONSTRUCTS AND USES THEREOF | Repertoire Immune Medicines, Inc. (US) | 2023-05-03 | — | — | EP | claimed |
| CN-115427079-A | Immune inducer comprising polynucleotide-peptide conjugate and pharmaceutical composition comprising same | 公立大学法人北九州市立大学 | 2022-12-02 | — | — | CN | claimed |
| CN-115397519-A | Antigenic polypeptides and methods of use thereof | 艾吉纳斯公司 | 2022-11-25 | — | — | CN | claimed |
| WO-2022216974-A1 | PEPTIDE-MHC-IMMUNOGLOBULIN MULTIMERS AND METHODS OF USE THEREOF | Repertoire Immune Medicines, Inc. (US) | 2022-10-13 | — | — | WO | claimed |
| WO-2022182872-A2 | COMPOSITIONS AND METHODS FOR IDENTIFICATION OF CROSS-REACTIVE ALLERGENIC PROTEINS AND TREATMENT OF ALLERGIES | ALLADAPT IMMUNOTHERAPEUTICS, INC. (US) | 2022-09-01 | — | — | WO | claimed |
| CN-114630837-A | Antigenic polypeptides and methods of use thereof | 艾吉纳斯公司 | 2022-06-14 | — | — | CN | claimed |
| EP-4004031-A2 | ANTIGENIC POLYPEPTIDES AND METHODS OF USE THEREOF | Agenus Inc. (US) | 2022-06-01 | — | — | EP | claimed |
| EP-4003536-A1 | ANTIGENIC POLYPEPTIDES AND METHODS OF USE THEREOF | Agenus Inc. (US) | 2022-06-01 | — | — | EP | claimed |
| US-20220073999-A1 | METHODS FOR GENERATING THERAPEUTIC DELIVERY PLATFORMS | KANSAS STATE UNIVERSITY RESEARCH FOUNDATION | 2022-03-10 | — | — | US | claimed |
Patent text — is the patent's own abstract consistent with the prediction?
For each of this compound's patents that has machine-readable text (2 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-20220073999-A1 | METHODS FOR GENERATING THERAPEUTIC DELIVERY PLATFORMS | EXOSC4, EXOSC5, EXOSC9 | KISS1R 2246/4885BACE1 547/4885OPRM1 4018/4885 |
| US-20250090648-A1 | NEXT GENERATION MRNA VACCINES | MICA, HLA-DRB1, RBM3 | KISS1R 3046/4885BACE1 2523/4885OPRM1 1791/4885 |
“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.