This content originally appeared on DEV Community and was authored by Maurizio Morri
A new open source library called BioJAX has entered the scene, offering blazing fast tools for AI in molecular biology by leveraging the JAX framework. Developed by researchers at the Broad Institute and community contributors, BioJAX allows deep integration of biological simulation and machine learning at scale.
What Is BioJAX
BioJAX brings GPU acceleration and automatic differentiation to key bioinformatics and molecular modeling operations. It builds on top of JAX’s NumPy-like interface and XLA-compiled execution to deliver high speed, hardware-agnostic performance.
The core modules currently support
- DNA and RNA sequence encoding and transformation
- Efficient distance matrices for structural biology
- Differentiable scoring functions for sequence alignment
- Backbone geometry modeling for proteins
- Integration with AlphaFold-style models
Example Usage
import biojax as bj
from jax import random
seq = "ACGTAGCTAG"
onehot = bj.sequence.one_hot(seq)
print(onehot.shape)
dist = bj.structure.pairwise_distance(onehot)
print(dist)
Why It Matters
Training machine learning models on biological data often requires custom tensor transformations, sequence encodings, and domain-specific differentiable operations. Traditional Python libraries like Biopython do not scale well with GPUs or TPUs.
With BioJAX, researchers can now prototype and train neural networks on biological inputs using the same ecosystem as models for vision and language. JAX compatibility also means that gradient computation through complex biological processes becomes easy and efficient.
How To Install
pip install biojax
Roadmap
The maintainers plan to expand BioJAX to include
- Transformer-based encoders for biological sequences
- Differentiable structure prediction heads
- Support for integration with DeepMind’s OpenFold
- Pretrained models for tasks like gene expression prediction
Final Thoughts
BioJAX may become a key bridge between computational biology and modern AI tooling. Its use of JAX makes it suitable for large-scale training while still remaining intuitive for bioinformaticians. As interest grows in modeling the molecular world with AI, tools like this will be critical for enabling the next wave of discoveries.
Sources
https://broad.io/biojax
https://github.com/broadinstitute/biojax
https://jax.readthedocs.io
This content originally appeared on DEV Community and was authored by Maurizio Morri
Maurizio Morri | Sciencx (2025-06-27T00:45:06+00:00) # BioJAX Unlocks High Performance Biology Workflows with JAX. Retrieved from https://www.scien.cx/2025/06/27/biojax-unlocks-high-performance-biology-workflows-with-jax/
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