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Building SOTA Foundation Models for Precision and Preventive Medicine

Research Project Biological Sciences

Building SOTA Foundation Models for Precision and Preventive Medicine

COMbining Deep-Learning with Physics-Based AffinIty EstimatiOn 4 (COMPBIO4)”

Research Project Biological Sciences

The IMPECCABLE workflow integrates machine learning (ML) and physics-based (PB) methods to accelerate drug discovery. By incorporating multiple ML and PB approaches, it leverages their strengths while mitigating individual limitations. The workflow is being scaled on exascale machines to efficiently identify potential drug candidates for various target proteins.

FRAME-IDP: Framework for Reasoning Analytics & Modeling of Emergent biologics targeting Intrinsically Disordered Proteins

Research Project Biological Sciences

FRAME-IDP

Jessica Moorshead

Person

Bubble Coalescence Dynamics in a High-Reynolds Number Decaying Turbulent Flow

Publications Journal of Fluid Mechanics

High-Temperature Memristors Enabled by Interfacial Engineering

Publications Science

CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning

Publications Nature Communications

Moiré Excitons in Generalized Wigner Crystals

Publications Applied Physical Sciences

High-Precision Scale Setting with the Ω-Baryon Mass and Gradient Flow

Publications Physical Review D

Practical Machine Learning Autotuning for Large-Scale Collective Communication

Publications IEEE Transactions on Parallel and Distributed Systems

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