An artistic representation of the IMPECCABLE workflow that constructively combines physics-based simulations and machine learning approaches to accelerate the process of compound screening in drug discovery. Image: Alex W. Wade, University College London
To speed up the procedures involved with drug discovery, this team is using state-of-the-art supercomputers to make personalized predictions about treatment outcomes.
This INCITE project will use exascale supercomputers for developing a personalized digital twin of the human body. This team is developing simulations of the entire cardiovascular system of the human body, and thus will afford clinicians the ability to make personalized predictions about treatment outcomes. Further they will combine machine learning and physics-based methods to accelerate the process of drug discovery.
The advent of exascale computing has opened up immense possibilities to realize a fully personalized digital twin (DT) of the human body. DT technology will not only enable clinicians to make reliable and actionable predictions to support clinical decision making, but it will also facilitate the adoption of informed lifestyle choices that support healthy ageing and wellbeing. To address any challenges, this team’s goal is to advance the modeling and simulation of the human body in health and disease such that they are at the forefront of the development of human DTs for applications in personalized medicine and healthcare. This requires access to large-scale computing resources. This team developed a method called IMPECCABLE that couples ML and PB methods to accelerate the drug discovery process, each compensating for the limitations of the other. The main goal of this project is to implement IMPECCABLE at scale on exascale machines and identify potential drug candidates for various target proteins. In addition to the drug design aspect, the team also aims to develop a related method enabling them to assess drug resistance in target proteins.
