Using AI to Predict Protein Shapes for Drug Discovery
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.
Original patent title: “Predicting protein structures using geometry neural networks that estimate similarity between predicted protein structures and actual protein structures”
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. Granted to GDM Holding in 2025 with 19 claims, and it is expected to expire in 2039.
Coverage
What does this patent actually cover?
The patent details a method for predicting the 3D shape (structure) of a protein, which is then used to identify and create new drug molecules, called ligands. The core process involves an iterative refinement loop (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). At each step, the system maintains a "current predicted structure" and generates an "alternative predicted structure." A "geometry neural network" then processes the protein's amino acid sequence and the alternative structure's parameters to produce a "geometry score" (Claim 1). This score estimates how similar the alternative structure is to the protein's actual shape. Based on this score, the system decides whether to update its current prediction. Once a sufficiently high-quality predicted structure is determined, the method evaluates how well various "candidate ligands" (potential drugs) might interact with this predicted protein shape. It selects candidates predicted to bind effectively and then proceeds to synthesize them (Claim 1).
The gap
What does this patent NOT cover?
- Does not cover protein structure prediction methods that do not use a geometry neural network to estimate similarity to an actual structure (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover methods for predicting protein structures that do not involve an iterative update process (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover protein structure prediction that isn't ultimately used for evaluating and selecting candidate drug ligands (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover methods that predict protein structures but do not include the step of synthesizing the selected ligands (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover protein structure prediction without considering the sequence of amino acid residues (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The core innovation is using a "geometry neural network" within an iterative refinement loop to estimate how similar a *predicted* protein structure is to its *actual* structure. This "geometry score" guides the system to improve its predictions, effectively learning to evaluate the quality of its own generated structures.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
AlphaFold (DeepMind)
RoseTTAFold (University of Washington)
Computational drug discovery platforms
AI-driven pharmaceutical research
Why it matters
The bigger picture
Accurately predicting a protein's 3D shape is a fundamental challenge in biology and crucial for designing new medicines. This patent describes an AI-driven approach to tackle this problem, which can significantly speed up drug discovery by computationally screening potential drug candidates against predicted protein targets before costly lab experiments.
Filed
September 16, 2019
Granted
October 7, 2025
Market context
Who's building on this
Companies in this space
Companies like DeepMind (Google's AI division, which developed AlphaFold), Insilico Medicine, Recursion Pharmaceuticals, and other biotech and pharmaceutical companies are actively using and developing AI for protein structure prediction and drug discovery. The assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, GDM Holding LLC, is likely a holding company for DeepMind's intellectual property.
Market impact
This technology has the potential to dramatically accelerate the drug discovery process, enabling the identification of new drug candidates more efficiently and at a lower cost. It could lead to a new era of "in silico" drug design, where computational methods play a much larger role in the early stages of pharmaceutical development, potentially creating entirely new classes of therapeutics.
Claim 1 — Plain English
What this patent covers
The patent details a method for predicting the 3D shape (structure) of a protein, which is then used to identify and create new drug molecules, called ligands. The core process involves an iterative refinement loop (Claim 1). At each step, the system maintains a "current predicted structure" and generates an "alternative predicted structure." A "geometry neural network" then processes the protein's amino acid sequence and the alternative structure's parameters to produce a "geometry score" (Claim 1). This score estimates how similar the alternative structure is to the protein's actual shape. Based on this score, the system decides whether to update its current prediction. Once a sufficiently high-quality predicted structure is determined, the method evaluates how well various "candidate ligands" (potential drugs) might interact with this predicted protein shape. It selects candidates predicted to bind effectively and then proceeds to synthesize them (Claim 1).
The clever bit
The core innovation is using a "geometry neural network" within an iterative refinement loop to estimate how similar a *predicted* protein structure is to its *actual* structure. This "geometry score" guides the system to improve its predictions, effectively learning to evaluate the quality of its own generated structures.
What it does not cover
- Does not cover protein structure prediction methods that do not use a geometry neural network to estimate similarity to an actual structure (Claim 1).
- Does not cover methods for predicting protein structures that do not involve an iterative update process (Claim 1).
- Does not cover protein structure prediction that isn't ultimately used for evaluating and selecting candidate drug ligands (Claim 1).
- Does not cover methods that predict protein structures but do not include the step of synthesizing the selected ligands (Claim 1).
- Does not cover protein structure prediction without considering the sequence of amino acid residues (Claim 1).
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
0/40
No citations yet
Claim breadth
13/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$47K – $150K
Midpoint $94K · 13.0 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
19 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Jumper, J., Penedones, H., Senior, A. W., Qin, C., Simonyan, K., Evans, R. A., Kirkpatrick, J., Sifre, L., & Sun, R. (2025). Using AI to Predict Protein Shapes for Drug Discovery (U.S. Patent No. 12,437,843). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12437843/predicting-protein-structures-using-geometry-neural-networks-that-estimate-simil
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does Using AI to Predict Protein Shapes for Drug Discovery cover?
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.
Who owns patent US 12437843?
GDM Holding owns this patent, granted in 2025.
When does this patent expire?
This patent is expected to expire on September 16, 2039, when the invention enters the public domain.
What problem does this patent solve?
Accurately predicting a protein's 3D shape is a fundamental challenge in biology and crucial for designing new medicines. This patent describes an AI-driven approach to tackle this problem, which can significantly speed up drug discovery by computationally screening potential drug candidates against predicted protein targets before costly lab experiments.
What does this patent NOT cover?
Does not cover protein structure prediction methods that do not use a geometry neural network to estimate similarity to an actual structure (Claim 1).
Patent monitoring