Information Processing System for Peptide Pharmacokinetics Prediction
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Solution Overview
Problem
Existing technologies do not effectively predict the pharmacokinetics of peptides, which is crucial for their development and application as medium molecule drugs.
Innovation Solution
An information processing system that includes a device capable of receiving requests, generating prediction information related to the pharmacokinetics of peptides, and transmitting this information to a terminal for display. This system utilizes a combination of machine learning models and molecular dynamics simulations to provide accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing molecular dynamics simulation technologies are used, then structural analysis of biopolymers can be performed, but pharmacokinetics prediction of peptides cannot be achieved
Solution Approach 1:
The patent extends the application range of molecular dynamics simulation technologies from structural analysis to pharmacokinetics prediction by integrating multiple simulation models. The system can perform both structural analysis and pharmacokinetics prediction using the same computational framework, achieving multi-functionality that resolves the limitation of existing single-purpose tools.
Solution Approach 2:
The patent changes the prediction parameters from structural properties to pharmacokinetic properties by adjusting the simulation models and evaluation metrics. This parameter transformation enables the system to predict absorption, distribution, metabolism, and excretion characteristics while maintaining the underlying molecular dynamics simulation approach.
2Ease of manufacture
If peptide drugs are developed without pharmacokinetics prediction, then development process is simpler, but effectiveness and safety cannot be reliably assessed
Solution Approach 1:
The patent performs pharmacokinetics prediction before actual clinical trials and drug administration. By conducting in silico simulations in advance, the system can forecast potential issues and optimize peptide designs prior to manufacturing and clinical testing, enabling early identification of pharmacokinetic challenges without delaying development.
Solution Approach 2:
The patent introduces computational simulation models as an intermediary between peptide design and clinical application. These virtual models serve as a bridge that provides detailed pharmacokinetic insights without requiring immediate physical experimentation, allowing for virtual screening and optimization before real-world testing.
Data Source
AI summary
In response to request signals transmitted from a terminal, a server generates prediction information relating to pharmacokinetics of a peptide. The server then transmits the prediction information to the terminal.


