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Efficient protein structure prediction fromcompact computers to datacenters withOpenFold-TRT

Didi, K.; Sohani, P.; Berressem, F.; Nesterovskiy, A.; Fomitchev, B.; Ohannessian, R.; Elbalkini, M.; Cogan, J.; Costa, A. B.; Vahdat, A.; Kallenborn, F.; Schmidt, B.; Mirdita, M.; Steinegger, M.; Dallago, C.; Chacon, A.

2026-03-15 bioinformatics
10.64898/2026.03.11.711233 bioRxiv
Show abstract

We introduce accelerations for deep learning inference with OpenFold and TensorRT that, combined with MMseqs2-GPU on an x86 system with one NVIDIA RTX PRO 6000 Blackwell Server Edition GPU, reach up to 131x faster inference compared to AlphaFold2. ARM-optimizations enable homology search on power efficient DGX Spark, and allow search beyond available GPU RAM on NVIDIA Grace Hopper Superchip at comparable overall folding speed. These accelerations enable high-throughput protein structure inference at no accuracy cost using familiar tools.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.