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Interactive Systems and Methods for Identifying Target Proteins in Drug Discovery (Case No. 2025-098)

Summary: UCLA researchers from the Department of Electrical and Computer Engineering have developed a novel computational system for target protein identification, enabling integrative drug discovery. Background: Target identification (Target ID) in drug discovery involves the identification and evaluation of protein candidates that could interact...

Published: 10/1/2025 | Inventor(s): Xiang Chen, Youngseung Jeon, Christopher Hwang, Ziwen Li, Jesus Campagna, Varghese John, Whitaker Cohn, Eunice Jun

Keywords(s): AI-driven drug discovery, Alzheimer’s disease target discovery, Bioinformatics software platform, Drug, Drug Discovery, Functional annotation of proteins, High-throughput docking, large language models (LLMs), Ligand-protein docking, Mechanism-of-action prediction, Molecular docking simulation, Multi-criteria decision support, Neurodegenerative disease therapeutics, Pathway-centric drug targeting, personalized medicine, PPI-Comparator, PPI-Explorer, Protein structure prediction, Protein-protein interaction (PPI) analysis, Rational drug design, Retrieval-augmented generation (RAG), Semantic similarity modeling, Small molecule screening, Systems biology interface, Target identification (Target ID), Therapeutic impact modeling, User-guided AI exploration

Category(s): Software & Algorithms, Software & Algorithms > AI Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Software & Algorithms > Digital Health, Life Science Research Tools, Life Science Research Tools > Research Methods, Platforms, Platforms > Drug Delivery