AlphaFold accessibility: an optimized open-source OOD app for Protein Structure Prediction
Vinay Saji Mathew [Pennsylvania State University], William Lai [Cornell]
The AlphaFold AI system won the 2024 Chemistry Nobel Prize because of its predictive achievements poised to revolutionize disease understanding and drug discovery. Initially released as open-source (and now proprietary), researchers are working to improve the code to require less resources and maintain open-source accessibility. We present an open-source implementation of AlphaFold 2 & 3 that optimizes computational resource allocation by intelligently separating CPU and GPU phases within a single OOD instance. This addresses a critical challenge to make AlphaFold more accessible by minimizing idle GPU cycles. Benchmarking across three major clusters (NCSA Delta, Jetstream2, and ROAR), we developed a user-friendly OOD application that operates with maximum resource efficiency.
Application Track [featuring AI OnDemand]
Tsai Auditorium (CGIS S010)