Molecule Generation Model Using Difference Information
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Solution Overview
Problem
Current methods for identifying molecules with specific properties in drug design are inefficient due to reliance on virtual screening techniques, which struggle to effectively navigate the large chemical space.
Innovation Solution
A method and apparatus for training a molecule generation model by obtaining molecular samples, determining molecular difference information, and training encoding and generation modules to generate optimized molecules with specific properties, utilizing techniques such as multilayer perceptrons and transformer models for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If virtual screening technique is used to identify molecules with specific properties, then the process can be automated, but the precision of molecule generation is insufficient due to the large chemical space
Solution Approach 1:
The patent segments the molecule generation process into distinct modules: an encoding module that processes input molecules into latent representations, and a generation module that synthesizes new molecules. This segmentation allows each module to specialize in specific tasks, improving overall precision while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces a latent space dimension between the input chemical space and output molecule space. By projecting molecules into this intermediate latent representation space and performing operations there, the system can efficiently navigate chemical space and generate precise molecular structures that would be difficult to obtain through direct screening.
2Productivity
If traditional virtual screening is used, then existing molecules can be evaluated, but the productivity of discovering new molecules with desired properties is low
Solution Approach 1:
The patent implements a self-service mechanism where the model generates its own training data through iterative optimization. The system uses the trained modules to automatically generate new molecules with desired properties, which are then used to further refine the model, creating a self-improving cycle that increases both productivity and reliability without requiring extensive manual intervention.
Solution Approach 2:
The patent incorporates feedback loops where the generation module's output is evaluated against target properties, and this feedback is used to adjust the latent representations and subsequent generation steps. This feedback mechanism ensures that the generated molecules progressively converge toward the desired properties, improving reliability while maintaining high productivity through automated iterative optimization.
Data Source
AI summary
The present disclosure provides a method for training a model, a method and an apparatus for generating molecules, and relates to the technical field of computer technology, particularly the technical field of artificial intelligence. The particular implementation may include: obtaining first molecular samples and second molecular samples; determining molecular difference information based on the first molecular samples and the second molecular samples; training an initial encoding module and an initial generation module based on the molecular difference information to obtain a target encoding module and a target generation module; and determining a molecule generation model based on the target encoding module and the target generation module.