Chemical Structure DSL Conversion for Accurate AI Interpretation

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Solution Overview

Problem

AI systems struggle to accurately interpret and generate predictions based on chemical structure representations due to their complexity and variability.

Innovation Solution

A system utilizing multiple machine learning models to transform chemical structure representations into a domain-specific language format, such as CMDL, for improved interpretation and storage in a database, enabling accurate predictions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If multiple machine learning models are used to transform chemical structure representations into domain-specific language, then accuracy and efficiency in generating chemical structure descriptions is improved, but device complexity increases

Engineering Contradiction:

Improveaccuracy in generating chemical structure descriptionsVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system divides the chemical structure processing task into multiple specialized machine learning models, each handling specific aspects of the transformation from chemical structure representations to domain-specific language. This segmentation allows each model to specialize in particular patterns or features, improving overall accuracy while maintaining manageable complexity through modular architecture.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediate representation layer between the input chemical structure data and the final domain-specific language output. This intermediate layer serves as a mediator that standardizes diverse chemical structure formats before processing, enabling the machine learning models to work with unified data structures and improving processing accuracy without requiring overly complex model architectures.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If chemical structures are represented in various notations and formats, then adaptability and versatility are improved, but difficulty of detecting and measuring increases

Engineering Contradiction:

Improveability to handle different chemical structure formatsVSAvoiddifficulty in processing and interpreting chemical structures

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The system employs a universal processing framework that can handle multiple chemical structure notations and formats (SMILES, MOLBLOCK, SMARTS, images, natural language) through a single integrated architecture. The machine learning models are designed to accept diverse input types and transform them into a common intermediate representation, enabling the system to process various formats without requiring separate specialized processing paths for each format type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

An intermediate representation layer is introduced that serves as a mediator between diverse input formats and the processing models. This intermediate layer standardizes different chemical structure representations into a unified format that is easier to process and analyze, reducing the difficulty of detecting and measuring chemical structure features while maintaining the ability to handle multiple input notations.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If AI systems directly process chemical structure representations, then productivity is improved, but reliability of predictions deteriorates due to complexity and variability

Engineering Contradiction:

Improvespeed of processing chemical structuresVSAvoidaccuracy of predictions

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary processing and transformation of chemical structure representations into standardized intermediate formats before the main prediction tasks are executed. This preliminary action includes normalizing diverse input formats, extracting key features, and preparing data in a consistent structure, which improves the reliability of subsequent predictions while maintaining processing efficiency through automated preprocessing pipelines.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

An intermediate representation layer acts as a mediator between the raw chemical structure data and the prediction models. This intermediate layer processes and standardizes the input data, reducing variability and complexity that would otherwise directly impact prediction reliability. The mediator enables faster processing by pre-processing data into optimized formats while improving prediction accuracy through consistent data representation.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12481658B1Creating domain-specific language representations of chemical structures

Publication Date: 2025.11.25 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A method for receiving a textual description of a first chemical structure; applying a first machine learning model to the textual description to generate a first data structure indicating components of the first chemical structure and bonds between the components of the first chemical structure; applying a second machine learning model to the first data structure to generate a first description of the first chemical structure using a domain specific language; and querying a database using the first description such that the database outputs a second description of a second chemical structure using the domain specific language stored in the database.