MODELLING FRAMEWORK FOR EMBEDDING-BASED PREDICTIONS FOR COMPOUND-VIRAL PROTEIN ACTIVITY

Sanjay Chawla; Ehsan Ullah; Raghvendra Mall; Hossam Almeer; Abdurrahman Elbasir

MODELLING FRAMEWORK FOR EMBEDDING-BASED PREDICTIONS FOR COMPOUND-VIRAL PROTEIN ACTIVITY

Sanjay Chawla (Inventor)
, Ehsan Ullah (Inventor)
, Raghvendra Mall (Inventor)
, Hossam Almeer (Inventor)
, Abdurrahman Elbasir (Inventor)

Qatar Foundation for Education, Science, and Community Development

Research output: Patent

Abstract

A global effort is underway to identify compounds to treat emerging virus infections, such as COVID-19. Since de novo compound design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing compounds that can be repurposed for COVID-19 and new viral diseases. The present invention discloses a machine learning representation framework that uses deep learning-induced vector embeddings of compounds and viral proteins as features to predict compound-viral protein activity. The prediction model uses a consensus framework to rank approved compounds against viral proteins of interest.

Original language	English
Patent number	US2022392567
IPC	G16B 40/ 20 A I
Priority date	27/05/22
Publication status	Published - 8 Dec 2022

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title = "MODELLING FRAMEWORK FOR EMBEDDING-BASED PREDICTIONS FOR COMPOUND-VIRAL PROTEIN ACTIVITY",

abstract = "A global effort is underway to identify compounds to treat emerging virus infections, such as COVID-19. Since de novo compound design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing compounds that can be repurposed for COVID-19 and new viral diseases. The present invention discloses a machine learning representation framework that uses deep learning-induced vector embeddings of compounds and viral proteins as features to predict compound-viral protein activity. The prediction model uses a consensus framework to rank approved compounds against viral proteins of interest.",

author = "Sanjay Chawla and Ehsan Ullah and Raghvendra Mall and Hossam Almeer and Abdurrahman Elbasir",

year = "2022",

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language = "English",

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T1 - MODELLING FRAMEWORK FOR EMBEDDING-BASED PREDICTIONS FOR COMPOUND-VIRAL PROTEIN ACTIVITY

AU - Chawla, Sanjay

AU - Ullah, Ehsan

AU - Mall, Raghvendra

AU - Almeer, Hossam

AU - Elbasir, Abdurrahman

PY - 2022/12/8

Y1 - 2022/12/8

N2 - A global effort is underway to identify compounds to treat emerging virus infections, such as COVID-19. Since de novo compound design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing compounds that can be repurposed for COVID-19 and new viral diseases. The present invention discloses a machine learning representation framework that uses deep learning-induced vector embeddings of compounds and viral proteins as features to predict compound-viral protein activity. The prediction model uses a consensus framework to rank approved compounds against viral proteins of interest.

AB - A global effort is underway to identify compounds to treat emerging virus infections, such as COVID-19. Since de novo compound design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing compounds that can be repurposed for COVID-19 and new viral diseases. The present invention discloses a machine learning representation framework that uses deep learning-induced vector embeddings of compounds and viral proteins as features to predict compound-viral protein activity. The prediction model uses a consensus framework to rank approved compounds against viral proteins of interest.

M3 - Patent

M1 - US2022392567

ER -

MODELLING FRAMEWORK FOR EMBEDDING-BASED PREDICTIONS FOR COMPOUND-VIRAL PROTEIN ACTIVITY

Abstract

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