MBZUAI hosted a two-day workshop on "Big Model AI in Drug Design" starting February 20, 2023. The workshop featured presentations from researchers in public and private institutions working on AI and health. MBZUAI Adjunct Professor Eran Segal opened the workshop with a talk on the Human Phenotype Project. Why it matters: The event highlights the growing interest and activity in applying AI, particularly large models, to advance drug discovery and personalized medicine within the UAE's research ecosystem.
A KAUST alumnus presented research on using large language models for complex disease modeling and drug discovery. LLMs were trained on insurance claims of 123 million US people to model diseases and predict genetic parameters. Protein language models were developed to discover remote homologs and functional biomolecules, while RNA language models were used for RNA structure prediction and reverse design. Why it matters: This work highlights the potential of LLMs to accelerate computational biology research and drug development, with a KAUST connection.
MBZUAI's Professor Le Song is developing an AI-driven simulation to model the human body at societal, organ, tissue, cellular, and molecular levels. The goal is to reduce the time and cost associated with bringing new medicines to market by removing the need for wet lab biological research. Song aims to create a comprehensive model using machine learning. Why it matters: This research could revolutionize drug discovery in the region by accelerating the development process and reducing reliance on traditional research methods.
Daisuke Kihara from Purdue University presented a seminar at MBZUAI on using deep learning for biomolecular structure modeling. His lab is developing 3D structure modeling methods, especially for cryo-electron microscopy (cryo-EM) data. They are also working on RNA structure prediction and peptide docking using deep neural networks inspired by AlphaFold2. Why it matters: Applying advanced deep learning techniques to biomolecular structure prediction can accelerate drug discovery and our understanding of molecular functions.
This article discusses the use of artificial intelligence in precision oncology, particularly in understanding individual tumor mechanisms and aiding clinical decision-making. Dr. Xinghua Lu, with extensive experience in medicine and biomedical informatics, will present research on individualized Bayesian causal inference methods for investigating oncogenic mechanisms. These methods aim to provide clinical decision support at the cellular, tumor, and patient levels. Why it matters: AI-driven precision oncology can enable more personalized and effective cancer treatments, improving patient outcomes in the region and globally.