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Accelerated Discovery

Applying novel computing technologies across artificial intelligence, hybrid cloud, quantum and classical computing for biomedical research such as drug and biomarker discovery.

Cleveland Clinic lead: Ahmet Erdemir, PhD - IBM lead: John Smith, PhD

The complexity of biomedical and healthcare data ecosystems requires comprehensive multidisciplinary investigations of disease trajectories, intervention possibilities and healthy homeostasis. Leveraging the expertise of Cleveland Clinic and IBM enables efficient, impactful and trustworthy use-inspired artificial intelligence.

The partnership will accelerate the discovery of knowledge in foundational omics, physiology and imaging, population and behavioral research, drug discovery and clinical care. The goal is to deliver personalized care through new markers, interventions and drug treatments, leading to comprehensive health and disease delivery in every aspect of biological systems, across multiple scales and spanning the complete lifecycle of biomedical discovery and translation.

Our Projects

Hybrid Cloud Research

CCF Lead: Eldon Walker, Dale Houston

IBM Lead: Sophia Wen

This project will explore how hybrid cloud technologies can accelerate Cleveland Clinic’s research. The team will take one of Cleveland Clinic’s important workloads and conduct a series of computing experiments to identify the benefits of using various hybrid cloud techniques. These computational workflow experiments will help to demonstrate the benefits and challenges of each technique, allowing researchers to find which one is best suited to address the workload. The findings from this study will be used to develop other scientific research use cases for hybrid cloud technologies.  

Platform & Infrastructure Initial Planning

CCF Lead: Eldon Walker

IBM Lead: Lauren McHugh

This project will identify the core computational technology capabilities of the Discovery Accelerator Platform through a series of discussions with key stakeholders. These discussions will provide detailed insights on Cleveland Clinic’s unique research computing and analytics needs. The findings will provide the required infrastructure planning needed to deploy IBM’s forthcoming Discovery Accelerator technologies.  

Alzheimer’s Disease Drug Repurposing

CCF Lead: Feixiong Cheng

IBM and Cleveland Clinic aim to identify existing drugs that have the potential of improving management, slowing deterioration and/or delaying the onset for neurodegenerative diseases including Alzheimer’s disease. This study will leverage network medicine analysis technologies of Cleveland Clinic and IBM’s prototype toolset called the drug repurposing engine (IBM DRE) which will be applied to a large data set. The team will focus on Alzheimer’s disease as the first instance to show acceleration of discovery with the combined tools.  

Next Generation Cancer Immunotherapies

CCF Lead: Tim Chan

IBM Lead: Wendy Cornell

This project will find new insights and develop new technologies that accelerate next-generation cancer immunotherapy discovery. Cleveland Clinic and IBM will use artificial intelligence and molecular simulations to develop better ways to identify targets for cancer vaccines. By creating and leveraging a combination of mechanistic simulations and AI models, we will expand our knowledge related to immunotherapy, vaccine design and clinical response to cancer immunotherapies.

Artificial Intelligence for 3D/4D Small Molecule Drug Discovery

CCF Lead: Shaun Stauffer

IBM Lead: Wendy Cornell

This project will be a unique opportunity for Cleveland Clinic to work with IBM’s computational experts, advanced computing platforms and molecular modeling tools for small molecule discovery/optimization in the areas of COVID antiviral drug development and inhibitors of a protein implicated in cancer development. The project will have chemists, structural biologists and computational scientists working together as a closely aligned team. The effort, if successful, will result in discoveries in chemistry that can ultimately lead towards novel drug development candidates for clinical testing.

Molecular Dynamics Simulations Combined with Machine Learning to Generate a Catalog of Molecular Defects for all Mutations Causing Possible Voltage-Gated Sodium Channel Disorders

IBM and Cleveland Clinic researchers are developing new techniques to perform larger capacity molecular simulations using quantum computing. Currently, simulations can only be run on small molecules, but the project aims to overcome present limitations and enhance the ability to analyze complex systems. Performing quantum simulations on larger molecular systems can improve the understanding of cellular mechanisms and potential therapeutic targets, which could have important applications in drug discovery.

IBM Deep Search Technology for Clinic

CCF Lead: Tara Karamlou

IBM Lead: Peter Staar

IBM will provide Cleveland Clinic with IBM Deep Search technology with the aim of enabling researchers to search for, and within, medical studies and publications. Additionally, IBM aims to further enhance and develop Deep Search to improve its functionalities to allow Cleveland Clinic to more efficiently utilize published literature and datasets to design clinical trials, develop clinical prediction tools, and identify patterns/associations.

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