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)

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 languageEnglish
Patent numberUS2022392567
IPCG16B 40/ 20 A I
Priority date27/05/22
Publication statusPublished - 8 Dec 2022

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