Inference Device for Lipid Molecule Design
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Solution Overview
Problem
The design and selection of lipid molecules for drug delivery systems are largely dependent on experience and know-how, making it time-consuming and difficult to find suitable chemical structures for various active ingredients and purposes, as the process involves repeated experimentation and evaluation.
Innovation Solution
A learning model is developed that associates lipid molecule chemical structure information with transfection efficiency and cell survival rate, allowing for the inference of optimal lipid molecule structures using input data and measurement results, thereby streamlining the design and selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual design and selection of lipid molecule chemical structures is performed based on experience and know-how, then the process can be performed with existing expertise, but it takes a long time to search for appropriate chemical structures due to repeated experimentation and evaluation
Solution Approach 1:
The patent replaces the manual mechanical process of lipid molecule design and selection with an automated machine learning system. The learning model automatically processes chemical structure data, predicts transfection efficiency and cell survival rate, and identifies optimal lipid molecules without requiring repeated manual experimentation, thereby dramatically reducing the time required while maintaining design quality.
Solution Approach 2:
The patent creates a virtual model (learning model) that copies and simulates the complex relationships between lipid molecule chemical structures and their biological effects. This virtual copy allows for rapid prediction and evaluation of multiple candidate molecules without physical experimentation, enabling efficient screening while preserving the expertise embedded in the training data.
2Measurement precision
If repeated experimentation and evaluation are performed to find appropriate lipid molecules, then accurate assessment of transfection efficiency and cell survival rate can be achieved, but the design and selection process becomes time-consuming
Solution Approach 1:
The patent performs preliminary computational evaluation of lipid molecule candidates using the trained learning model before conducting actual experiments. By predicting transfection efficiency and cell survival rate in silico first, the system prioritizes the most promising candidates for physical experimentation, thereby reducing the number of iterative experiments needed while maintaining evaluation accuracy.
Solution Approach 2:
The patent substitutes repeated physical experimentation with computational prediction using machine learning. The learning model, trained on experimental data, rapidly evaluates candidate molecules in silico, preserving the measurement precision of traditional methods while eliminating the time-consuming nature of repeated wet-lab experiments.
3Adaptability or versatility
If lipid molecules are designed and selected based on limited types of active ingredients and purposes, then the design process can be completed with available data, but it becomes difficult to accumulate know-how for various active ingredients and purposes
Solution Approach 1:
The patent develops a universal learning model that can handle multiple types of active ingredients and application purposes simultaneously. The model is trained on diverse datasets encompassing various lipid molecules, active ingredients, and evaluation contexts, enabling it to generalize knowledge across different scenarios and accumulate design know-how that applies broadly rather than being limited to specific cases.
Solution Approach 2:
The patent creates a composite knowledge base by integrating data from multiple sources, active ingredient types, and evaluation contexts into a unified learning model. This composite approach allows the system to leverage patterns and relationships across diverse datasets, accumulating versatile design know-how that can be applied to various active ingredients and purposes.
Data Source
AI summary
An operation of designing or selecting chemical structure information on a lipid molecule forming a particle encapsulating an active ingredient is supported. An inference device includes an acquiring unit configured to acquire input data including at least chemical structure information on a lipid molecule, and a learned model generated by performing a learning process on a learning model that associates input data including at least chemical structure information on a lipid molecule with a transfection efficiency of an active ingredient encapsulated in a particle containing the lipid molecule into a cell and/or a cell survival rate. The learned model infers a transfection efficiency and/or a cell survival rate associated with the input data newly acquired by the acquiring unit.