From AlphaFold3 to Protenix: Making Biomolecular Modeling More Practical

In this blog post, we are going to explore Protenix, an implementation of AlphaFold3. Protenix is a reproduction of the original AlphaFold3 model released by Google's DeepMind, but it offers several practical advantages. It comes with a commercial-friendly Apache-2 license, making it easier for both researchers and companies to use. Protenix also improves access by open-sourcing model weights, inference code, and training code—key components often missing in other tools.

Protenix predictions VS Experimental results, taken from Protenix repository

Protenix is fully open-source, giving researchers the chance to use, improve, and build upon a powerful molecular prediction tool. By fixing errors, refining methods, and optimizing workflows, Protenix ensures reliable results and ease of use. In this post, we will look at its features, performance, and how it can drive progress in scientific and commercial applications.

One of Protenix's key strengths is its ability to predict complex molecular interactions with great accuracy. From protein-ligand interactions to nucleic acid complexes, Protenix matches or even outperforms AlphaFold3 in several areas. It offers:

Protenix VS AlphaFold3, taken from Protenix technical report

• Protein-Ligand Interactions: Strong performance in ligand binding predictions, validated on PoseBusters V2.
• Protein-Protein Complexes: Accurate modeling of protein-protein and protein-antibody interactions, often beating AlphaFold-Multimer 2.3.
• Nucleic Acid Complexes: High-quality RNA and DNA structure predictions, surpassing models like RoseTTAFold2NA.

These abilities make Protenix a powerful tool for structural biology, drug discovery, and protein design.

What Makes Protenix Unique?

1. Open-Source and Transparent AlphaFold3 is groundbreaking, but its closed nature makes it hard to use and improve. Protenix fixes this by offering:

• Model Weights: Full access to trained weights for instant use.
• Training and Inference Code: Tools to customize or fine-tune the model for specific needs.
• Pre-processed Datasets: Ready-to-use datasets that save time and effort.

This approach makes Protenix accessible to anyone wanting to build or experiment with it.

2. Easy to Use for Researchers Protenix simplifies molecular modeling, even for those with little machine learning experience. It comes with clear documentation, processed MSAs, and organized datasets, making it easier to start working quickly.

3. Optimized for Efficiency Protenix includes technical improvements that allow it to run faster and on less powerful hardware:

• Custom CUDA Kernels: Speed up key components like LayerNorm.
• BF16 Training: Reduces memory use without losing accuracy.
• Memory Optimization: Techniques like gradient checkpointing and chunking help manage large models.
• Smart Cropping: Handles large molecules efficiently while keeping important details intact.

These improvements make Protenix more accessible for small research teams or labs with limited resources.

4. Reliable Performance Protenix performs exceptionally well across standard benchmarks:

• PoseBusters V2: Protenix achieves high accuracy in protein-ligand binding predictions, with strong RMSD (Root Mean Square Deviation) success rates. The model not only identifies correct binding conformations but also generates physically valid structures, passing key checks like steric clashes and chirality.

• Low Homology PDB Sets: On protein-protein and protein-antibody complexes, Protenix outperforms AlphaFold-Multimer 2.3. It demonstrates higher DockQ scores, which measure the quality of predicted interface contacts, even for structures with low sequence similarity.

CASP15 RNA prediction accuracy, taken from Protenix technical report

• CASP15 RNA Targets: Protenix matches AlphaFold3's performance on RNA structure prediction tasks, achieving competitive LDDT (Local Distance Difference Test) and TM-scores while surpassing RoseTTAFold2NA.
• Nucleic Acid Complexes: Protenix excels at predicting both RNA-protein and DNA-protein structures, delivering accurate interface scores and structural alignments. The model effectively handles novel structures, showcasing its generalization ability.

These results show that Protenix can tackle a variety of biomolecular tasks with confidence.

Applications of Protenix

Protenix can be used across many areas of molecular research and development:

• Drug Discovery: Predict how proteins bind to small molecules, speeding up virtual screening and drug design.
• Protein Engineering: Model protein-protein and nucleic acid interactions to design new enzymes, antibodies, or therapeutic proteins.
• RNA and DNA Research: Study detailed RNA and DNA structures, helping advance gene therapy and synthetic biology.
• Academic Research: Protenix’s open-source nature allows researchers to explore new ideas, improve predictions, and develop better tools.

Neurosnap and Protenix: Simplified Access for Your Research

At Neurosnap, we make Protenix easy to use with a simple web platform. Whether you're working on drug discovery, protein engineering, or molecular biology, you can focus on your research while we handle the technical setup and infrastructure.

Access our Protenix service using our easy to use web UI.