Rapid and energy-efficient ultra-large library screening for drug discovery on a SpiNNaker2 neuromorphic chip
Abstract
The virtual screening of make-on-demand small molecule libraries can prioritize drug candidates for rapid experimental validation to accelerate pre-clinical drug discovery. As ultra-large libraries grow to billions of compounds, exhaustive screening incurs prohibitive costs and energy consumption. We address this challenge with the neuromorphic SpiNNaker2 system, designed for massively parallel AI tasks. Here, we show the implementation of a ligand-based screening pipeline on a 152-core SpiNNaker2 chip. We adapted feed-forward neural networks trained on 2D molecular descriptors to screen 19 billion molecules from the Enamine REAL space. We benchmarked our approach against an NVIDIA Jetson Orin Nano, a GPU-accelerated low-power AI system. Inference on the SpiNNaker2 chip was approximately 4 times faster, yielding 60% higher overall throughput. Meanwhile, SpiNNaker2 consumed about 86% less energy. These results provide a foundation for the deployment of SpiNNaker2 high performance computing clusters and establish application-specific hardware as a scalable and sustainable avenue for cheminformatics.
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