Therapeutic Protein Sequence Generation With Diffusion-Guided Safety Screening
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Solution Overview
Problem
Existing artificial intelligence systems struggle to generate therapeutic proteins with both excellent therapeutic effects and human safety, as they inadequately consider side effects, leading to high costs and prolonged development times due to the difficulty in predicting human immune responses.
Innovation Solution
A system and method using a noise-based diffusion model to iteratively add and remove noise from reference protein sequences, guided by structure and sequence properties, to generate candidate proteins with improved binding affinity and reduced immunogenicity, utilizing an artificial neural network trained to minimize loss functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing AI models are used to generate therapeutic proteins, then binding affinity to target proteins can be improved, but human safety and side effect prediction remain insufficient
Solution Approach 1:
The patent applies preliminary action by conducting in silico clinical trials before actual human administration. The AI model predicts immunogenicity and side effects in advance, allowing researchers to identify and eliminate problematic protein sequences before clinical testing, thus preventing harmful effects while maintaining binding affinity.
Solution Approach 2:
The patent implements feedback mechanisms where predicted side effects and immunogenicity results are fed back into the AI model to iteratively refine protein sequence generation. This continuous feedback loop allows the system to learn from previous predictions and improve its accuracy in identifying safe protein candidates.
2Reliability
If therapeutic protein drugs are developed with large sizes, then therapeutic effects can be enhanced, but unexpected side effects on human immune cells increase
Solution Approach 1:
The system performs preliminary computational screening to predict potential immunogenicity issues before synthesizing large protein drugs. By analyzing sequence characteristics and structural properties in advance, the system identifies candidates that may cause unwanted immune responses and eliminates them from development pipelines.
Solution Approach 2:
The AI model acts as an intermediary between protein design and clinical application. It serves as a virtual testing ground that mediates the relationship between large protein structures and human immune system responses, allowing prediction of interactions without actual biological testing.
3Object-affected harmful factors
If accurate prediction of side effects is performed before administration, then human safety can be improved, but time and cost for evaluation increase
Solution Approach 1:
The patent replaces traditional mechanical and laboratory-based safety evaluation methods with computational AI modeling. Instead of conducting extensive in vitro and in vivo experiments that consume time and resources, the system uses machine learning models trained on vast datasets to predict side effects rapidly and accurately.
Solution Approach 2:
The system creates virtual copies of therapeutic proteins through computational modeling, allowing unlimited iterations of safety testing without physical constraints. These digital replicas can be evaluated simultaneously using multiple prediction algorithms, dramatically accelerating the safety assessment process.
4Ease of manufacture
If conventional methods are used for protein sequence generation, then development process can be simplified, but prediction reliability and safety consideration are insufficient
Solution Approach 1:
The AI model is designed with multi-functionality, simultaneously performing sequence generation, binding affinity prediction, immunogenicity assessment, and side effect analysis. This universal approach consolidates multiple specialized tools into a single integrated system, maintaining ease of use while dramatically improving prediction reliability.
Solution Approach 2:
The system combines multiple data sources, algorithmic approaches, and predictive models into a composite AI framework. By integrating diverse computational methods and training data, the system achieves superior prediction reliability while maintaining user-friendly operation through a unified interface.
Data Source
AI summary
A system and method for generating protein amino acid sequences having a user-desired property are provided. Using a noise-based diffusion model, the system and method can generate amino acid sequences of proteins that have excellent disease treatment effects and are safe for use as therapeutic agents in a human body. The system can function by obtaining reference protein sequence information, generating noise-added protein sequence information, iteratively generating noise-removed protein sequence information and partially noise-added protein sequence information, and generating noise-removed output protein sequence information. Noise may be added to protein sequence information using a Gaussian or other known noise model. Noise may be removed from protein sequence information using an artificial neural network model trained by a method of minimizing a loss function. By incorporating sequence guidance and structure guidance derived from known proteins, users can generate improved candidate protein drugs for testing.