# Computer Model Designs Proteins by Matching Structure to Sequence > IBM's 2025 patent describes a computer system that designs new protein sequences by first understanding a target 3D protein shape and then generating a sequence that would fold into that shape. - **Patent:** US 12412637 - **Original title:** Embedding-based generative model for protein design - **Owner:** International Business Machines - **Granted:** 2025 - **Status:** Active - **Times cited:** 0 - **Field:** biotech, pharmaceutical, software, ai_ml ## What it does This patent details a computer method for designing new protein sequences. It uses advanced AI models called transformers. First, it takes a known protein's 3D structure and converts it into a numerical representation (a 'latent fold representation') in a 'second latent space.' Simultaneously, it takes the protein's amino acid sequence and converts it into a numerical representation (a 'latent sequence representation') in a 'first latent space.' The system then trains a decoder to learn how these two representations relate, essentially creating a bridge between protein shape and protein sequence. When given a new target 3D protein structure, the system encodes it and uses the trained decoder to generate a new amino acid sequence that should fold into that specific target structure. For example, it could be used to design a protein that performs a specific function by first defining the necessary 3D shape for that function. ## What it does NOT cover - Designing biological sequences without using a transformer model encoder - Methods that do not involve generating latent sequence representations - Methods that do not generate latent fold representations from 3D structures - Systems that do not train a decoder to learn a joint latent space between sequence and fold representations - Designing sequences for structures not represented in 3D voxels - Methods that do not involve inverse folding of three-dimensional structures ## The clever bit The innovation lies in using two separate transformer encoders to create distinct numerical 'fingerprints' for both the protein's sequence and its 3D structure, then training a decoder to map between these fingerprints. This allows the system to 'design backwards' from a desired shape to a workable sequence. ## Real-world examples 1. Protein design software for drug discovery 2. AI-driven enzyme engineering platforms 3. Computational biology research tools ## Why it matters Designing novel proteins with specific functions is crucial for developing new medicines, enzymes for industrial processes, and advanced materials. This patent represents a significant step in using AI to automate and accelerate the discovery of proteins with desired structural and functional properties. ## Frequently asked questions ### What does Computer Model Designs Proteins by Matching Structure to Sequence cover? IBM's 2025 patent describes a computer system that designs new protein sequences by first understanding a target 3D protein shape and then generating a sequence that would fold into that shape. ### Who owns patent US 12412637? International Business Machines owns this patent, granted in 2025. ### When does this patent expire? This patent is expected to expire on May 11, 2041, when the invention enters the public domain. ### What problem does this patent solve? Designing novel proteins with specific functions is crucial for developing new medicines, enzymes for industrial processes, and advanced materials. This patent represents a significant step in using AI to automate and accelerate the discovery of proteins with desired structural and functional properties. ### What does this patent NOT cover? Designing biological sequences without using a transformer model encoder **Full plain-English explainer:** https://patentbrief.org/patent/us/12412637/embedding-based-generative-model-for-protein-design **Original patent:** https://patents.google.com/patent/US12412637 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [AI Predicts 3D Protein Shapes from Genetic Code](https://patentbrief.org/patent/us/20240412809/protein-structure-prediction-from-amino-acid-sequences-using-self-attention-neur) — This patent describes how a computer uses self-attention neural networks to predict the complex three-dimensional shape of a protein based on its simple amino acid sequence, a process crucial for understanding biology and developing new medicines. - [Using AI to Predict Protein Shapes for Drug Discovery](https://patentbrief.org/patent/us/12437843/predicting-protein-structures-using-geometry-neural-networks-that-estimate-simil) — This patent describes an AI-driven method for iteratively predicting protein 3D structures using a geometry neural network, then using those predictions to find and synthesize new drug molecules. - [How Computers Predict Protein Shapes Faster Using Smart Templates](https://patentbrief.org/patent/us/20210280268/protein-structure-prediction-system) — This patent describes a computer method to quickly predict the 3D shape of proteins by creating and refining "synthetic templates" from existing protein structures, reducing the heavy computational work usually needed. - [Predicting How Molecules Interact with Proteins Using Two AI Streams](https://patentbrief.org/patent/us/11176462/system-and-method-for-prediction-of-protein-ligand-interactions-and-their-bioact) — This patent describes a computer system that uses two separate artificial intelligence models, one for molecules and one for proteins, to predict how they will interact and what biological effects they might have. - [How to Predict Protein Shapes Better Using Lab Tests and Computers](https://patentbrief.org/patent/us/20210174903/enhanced-protein-structure-prediction-using-protein-homolog-discovery-and-constr) — This patent describes a method to improve predicting a protein's 3D shape by combining computer simulations with actual distance measurements from specific parts of the protein in a lab.