Protein Embeddings For Biological Analysis With AMD GPUs
AMD GPUs Powering AI-Driven Biology: Reprogramming Discovery
Contribution from IPA Therapeutics
Part 2: Protein Embeddings Improve Biology Analysis
The second chapter of the benchmarking series compares AMD Instinct MI300X and NVIDIA H100 GPUs for drug development AI activities.
Part 1: NLP-based biomedical research knowledge extraction. It now examines the protein layer to determine how well both GPUs handle large-scale protein language models (pLLMs), which help understand structure, function, and mutational impacts.
These benchmarks were conducted by ImmunoPrecise Antibodies (IPA) and its AI subsidiary BioStrand, makers of LENSai, an AI-native platform that uses sequencing, structure, and functional reasoning to advance biologics discovery. Every test employed Vultr's high-performance cloud architecture for reproducible, side-by-side comparisons in a production environment.
HYFT biological fingerprinting by LENSai combines conserved sequence, structure, and function into one index. HYFTs were created to solve AI's incapacity to understand biological processes. HYFTs integrate biological reasoning into computational fabric to let AI models reason biologically rather than only calculate.
AMD currently studies protein embeddings to understand binding interactions, mutation consequences, and molecular function. These embeddings underpin structural models and therapeutic target prioritisation. It compares different ESM-2 models and examines 'anchored embeddings' in LENSai and HYFT to show AMD GPUs' performance in a sector where memory capacity and biological accuracy are critical.
Using functional and evolutionary information, Protein Language Models (pLLMs) decode amino acid sequences into manageable vectors, improving biological data processing.
They let researchers ask: How similar is this sequence to druggable targets? Which structure will this unnamed protein adopt? How might a mutation affect binding or function? Embeddings improve immunogenicity screening, antibody identification, and multi-omics interpretation by reducing data for machine learning algorithms.
Benchmarks for ESM-2 Protein Language Model
AMD's throughput and scaling advantages were examined using ESM-2 benchmarks with various model sizes:
AMD GPUs handled larger batches smoothly, reducing costs and improving throughput.
Combined Drug Discovery With HYFT/LENSai
LENSai uses "HYFT anchored embeddings," which pick residues inside conserved HYFT patterns to reduce noise and improve biological signal clarity.
Biologically significant HYFTs are conserved structure or function motifs. LENSai embeds HYFTs to decrease noise and focus on the sequence's most functionally informative parts.
HYFT embedding support:
Estimating structural mutation consequences precisely.
Finding conserved motifs.
Effective semantic search across treatment libraries.
Easy Transition to AMD Graphics Processors
AMD GPU protein embeddings use a single-line Dockerfile update to minimise disruption:
FROM ROCM6.3.1_ubuntu22.04_py3.10_pytorch rocm/pytorch
No major code modifications are needed.
Protein embeddings connect unprocessed sequences to function. The experiment demonstrates that AMD MI300X has the memory headroom and performance for the most advanced protein models.
AMD compares RFdiffusion, a generative model that can imagine and produce new proteins, in the final chapter of this series to push AI-driven design.
Conclusion
AMD MI300X GPUs revolutionise AI-driven biological research, says this article. The MI300X's enormous memory and bandwidth let researchers process complicated biological data faster, boosting genomics and drug discovery. These powerful GPUs enable computational biology AI models and analytics to advance.