PEPTIDE BINDING MOTIF GENERATION

Methods and systems for peptide generation include training (204) a peptide mutation policy neural network using reinforcement learning that includes a peptide presentation score as a reward. New peptides are generated (212) using the peptide mutation policy. A binding motif of a major histocompatib...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: MIN, Renqiang, GRAF, Hans Peter
Format: Patent
Sprache:eng ; fre
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator MIN, Renqiang
GRAF, Hans Peter
description Methods and systems for peptide generation include training (204) a peptide mutation policy neural network using reinforcement learning that includes a peptide presentation score as a reward. New peptides are generated (212) using the peptide mutation policy. A binding motif of a major histocompatibility complex is calculated (214) using the new peptides. Library peptides are screened (216) in accordance with the binding motif. Des procédés et des systèmes de génération de peptide comprennent l'apprentissage (204) d'un réseau neuronal de politique de mutation peptidique à l'aide d'un apprentissage par renforcement qui comprend un score de présentation de peptide en tant que récompense. De nouveaux peptides sont générés (212) à l'aide de la politique de mutation peptidique. Un motif de liaison d'un complexe majeur d'histocompatibilité est calculé (214) à l'aide des nouveaux peptides. Des peptides de banque sont criblés (216) conformément au motif de liaison.
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_WO2023225250A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>WO2023225250A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_WO2023225250A13</originalsourceid><addsrcrecordid>eNrjZFAIcA0I8XRxVXDy9HPx9HNX8PUP8XRTcHf1cw1yDPH09-NhYE1LzClO5YXS3AzKbq4hzh66qQX58anFBYnJqXmpJfHh_kYGRsZGRqZGpgaOhsbEqQIAdfQitg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>PEPTIDE BINDING MOTIF GENERATION</title><source>esp@cenet</source><creator>MIN, Renqiang ; GRAF, Hans Peter</creator><creatorcontrib>MIN, Renqiang ; GRAF, Hans Peter</creatorcontrib><description>Methods and systems for peptide generation include training (204) a peptide mutation policy neural network using reinforcement learning that includes a peptide presentation score as a reward. New peptides are generated (212) using the peptide mutation policy. A binding motif of a major histocompatibility complex is calculated (214) using the new peptides. Library peptides are screened (216) in accordance with the binding motif. Des procédés et des systèmes de génération de peptide comprennent l'apprentissage (204) d'un réseau neuronal de politique de mutation peptidique à l'aide d'un apprentissage par renforcement qui comprend un score de présentation de peptide en tant que récompense. De nouveaux peptides sont générés (212) à l'aide de la politique de mutation peptidique. Un motif de liaison d'un complexe majeur d'histocompatibilité est calculé (214) à l'aide des nouveaux peptides. Des peptides de banque sont criblés (216) conformément au motif de liaison.</description><language>eng ; fre</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA ; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS ; PHYSICS</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20231123&amp;DB=EPODOC&amp;CC=WO&amp;NR=2023225250A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20231123&amp;DB=EPODOC&amp;CC=WO&amp;NR=2023225250A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>MIN, Renqiang</creatorcontrib><creatorcontrib>GRAF, Hans Peter</creatorcontrib><title>PEPTIDE BINDING MOTIF GENERATION</title><description>Methods and systems for peptide generation include training (204) a peptide mutation policy neural network using reinforcement learning that includes a peptide presentation score as a reward. New peptides are generated (212) using the peptide mutation policy. A binding motif of a major histocompatibility complex is calculated (214) using the new peptides. Library peptides are screened (216) in accordance with the binding motif. Des procédés et des systèmes de génération de peptide comprennent l'apprentissage (204) d'un réseau neuronal de politique de mutation peptidique à l'aide d'un apprentissage par renforcement qui comprend un score de présentation de peptide en tant que récompense. De nouveaux peptides sont générés (212) à l'aide de la politique de mutation peptidique. Un motif de liaison d'un complexe majeur d'histocompatibilité est calculé (214) à l'aide des nouveaux peptides. Des peptides de banque sont criblés (216) conformément au motif de liaison.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA</subject><subject>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZFAIcA0I8XRxVXDy9HPx9HNX8PUP8XRTcHf1cw1yDPH09-NhYE1LzClO5YXS3AzKbq4hzh66qQX58anFBYnJqXmpJfHh_kYGRsZGRqZGpgaOhsbEqQIAdfQitg</recordid><startdate>20231123</startdate><enddate>20231123</enddate><creator>MIN, Renqiang</creator><creator>GRAF, Hans Peter</creator><scope>EVB</scope></search><sort><creationdate>20231123</creationdate><title>PEPTIDE BINDING MOTIF GENERATION</title><author>MIN, Renqiang ; GRAF, Hans Peter</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_WO2023225250A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; fre</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA</topic><topic>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>MIN, Renqiang</creatorcontrib><creatorcontrib>GRAF, Hans Peter</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>MIN, Renqiang</au><au>GRAF, Hans Peter</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>PEPTIDE BINDING MOTIF GENERATION</title><date>2023-11-23</date><risdate>2023</risdate><abstract>Methods and systems for peptide generation include training (204) a peptide mutation policy neural network using reinforcement learning that includes a peptide presentation score as a reward. New peptides are generated (212) using the peptide mutation policy. A binding motif of a major histocompatibility complex is calculated (214) using the new peptides. Library peptides are screened (216) in accordance with the binding motif. Des procédés et des systèmes de génération de peptide comprennent l'apprentissage (204) d'un réseau neuronal de politique de mutation peptidique à l'aide d'un apprentissage par renforcement qui comprend un score de présentation de peptide en tant que récompense. De nouveaux peptides sont générés (212) à l'aide de la politique de mutation peptidique. Un motif de liaison d'un complexe majeur d'histocompatibilité est calculé (214) à l'aide des nouveaux peptides. Des peptides de banque sont criblés (216) conformément au motif de liaison.</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng ; fre
recordid cdi_epo_espacenet_WO2023225250A1
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
title PEPTIDE BINDING MOTIF GENERATION
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T22%3A00%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=MIN,%20Renqiang&rft.date=2023-11-23&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EWO2023225250A1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true