Predicting How Molecules Interact with Proteins Using Two AI Streams
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.
Original patent title: “System and method for prediction of protein-ligand interactions and their bioactivity”
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. Granted to RO5 in 2021 with 22 claims and 7 forward citations, and it is expected to expire in 2041.
Coverage
What does this patent actually cover?
The system predicts how a target molecule (a ligand) will interact with a target protein segment and what biological effect that interaction will have. It does this by first taking the chemical notation of the target molecule and processing it through a trained graph-based neural network to get a "first vector result" (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). Separately, it takes the chemical notation of the target protein segment and processes it through a trained sequence-based neural network to get a "second vector result" (Claim 1). These two results are then combined, or "concatenated," and used to make a prediction about the bioactivity. For example, a drug researcher could input a new drug candidate molecule and a protein known to be involved in a disease, and the system would predict if the drug is likely to bind to the protein and what effect it might have.
The gap
What does this patent NOT cover?
- Does not cover systems that use only a single machine learning model to analyze both the ligand and protein simultaneously, rather than two separate streams.
- Does not cover prediction systems that do not concatenate distinct vector results from separate ligand and protein processing streams.
- Does not cover systems that predict interactions without using a graph-based neural network for the ligand analysis.
- Does not cover systems that predict interactions without using a sequence-based neural network for the protein segment analysis.
- Does not cover methods that do not receive chemical notation as input for both the target molecule and target protein segment.
- Does not cover systems that predict interactions but do not specifically output a prediction as to the bioactivity.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in combining two specialized machine learning approaches: a graph-based neural network for molecules (which excel at understanding chemical structures) and a sequence-based neural network for proteins (which are good at understanding protein sequences), and then merging their distinct analytical outputs to make a unified prediction.
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
Drug discovery platforms for new pharmaceuticals
Material science research for designing new polymers or catalysts
Toxicology screening for environmental safety assessments
Biotechnology applications for enzyme engineering
Why it matters
The bigger picture
Predicting how molecules interact with proteins is a fundamental challenge in drug discovery and materials science. This system aims to speed up the early stages of drug development by computationally screening potential drug candidates. By accurately predicting bioactivity, researchers can prioritize promising compounds and avoid costly experiments on ineffective ones, potentially leading to new medicines or materials faster.
Filed
February 9, 2021
Granted
November 16, 2021
Market context
Who's building on this
Companies in this space
Companies and research institutions in the pharmaceutical and biotechnology sectors are actively developing and using computational methods for drug discovery. Major players like Schrödinger, Insilico Medicine, and various academic research groups are continuously advancing AI-driven approaches to predict molecular interactions and bioactivity, building on the principles of machine learning for chemical and biological data.
Market impact
This patent contributes to the growing field of AI-driven drug discovery, which aims to reduce the time and cost associated with bringing new drugs to market. By offering a more efficient way to screen potential drug candidates, it supports the development of new therapeutic compounds and has the potential to accelerate research in areas like personalized medicine and novel material design. The approach of combining specialized AI models for different data types has become a common strategy in the industry.
Claim 1 — Plain English
What this patent covers
The system predicts how a target molecule (a ligand) will interact with a target protein segment and what biological effect that interaction will have. It does this by first taking the chemical notation of the target molecule and processing it through a trained graph-based neural network to get a "first vector result" (Claim 1). Separately, it takes the chemical notation of the target protein segment and processes it through a trained sequence-based neural network to get a "second vector result" (Claim 1). These two results are then combined, or "concatenated," and used to make a prediction about the bioactivity. For example, a drug researcher could input a new drug candidate molecule and a protein known to be involved in a disease, and the system would predict if the drug is likely to bind to the protein and what effect it might have.
The clever bit
The novelty lies in combining two specialized machine learning approaches: a graph-based neural network for molecules (which excel at understanding chemical structures) and a sequence-based neural network for proteins (which are good at understanding protein sequences), and then merging their distinct analytical outputs to make a unified prediction.
What it does not cover
- Does not cover systems that use only a single machine learning model to analyze both the ligand and protein simultaneously, rather than two separate streams.
- Does not cover prediction systems that do not concatenate distinct vector results from separate ligand and protein processing streams.
- Does not cover systems that predict interactions without using a graph-based neural network for the ligand analysis.
- Does not cover systems that predict interactions without using a sequence-based neural network for the protein segment analysis.
- Does not cover methods that do not receive chemical notation as input for both the target molecule and target protein segment.
- Does not cover systems that predict interactions but do not specifically output a prediction as to the bioactivity.
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
Moderate
Citation count
18/40
Early citations
Claim breadth
15/20
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
$120K – $383K
Midpoint $240K · 14.6 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
22 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Bucher, A., Joĉys, {. O. (., Pabrinkis, A., Demtchenko, M., Jamieson, C. S., Tal, R., YANG, Z., Bastas, O., & Knuff, C. D. (2021). Predicting How Molecules Interact with Proteins Using Two AI Streams (U.S. Patent No. 11,176,462). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11176462/system-and-method-for-prediction-of-protein-ligand-interactions-and-their-bioact
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 Predicting How Molecules Interact with Proteins Using Two AI Streams cover?
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.
Who owns patent US 11176462?
RO5 owns this patent, granted in 2021.
When does this patent expire?
This patent is expected to expire on February 9, 2041, when the invention enters the public domain.
What is patent US 11176462 cited by?
This patent has been cited by 7 later patents that build on its ideas.
What problem does this patent solve?
Predicting how molecules interact with proteins is a fundamental challenge in drug discovery and materials science. This system aims to speed up the early stages of drug development by computationally screening potential drug candidates. By accurately predicting bioactivity, researchers can prioritize promising compounds and avoid costly experiments on ineffective ones, potentially leading to new medicines or materials faster.
What does this patent NOT cover?
Does not cover systems that use only a single machine learning model to analyze both the ligand and protein simultaneously, rather than two separate streams.
Patent monitoring