AI Synthesis Pathway Planning With Reaction Feasibility Ranking
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Solution Overview
Problem
The process of determining chemical synthesis pathways for drug development is inefficient and prone to errors, requiring chemists to manually review numerous scientific papers, making it a bottleneck in drug discovery.
Innovation Solution
An AI-driven system that rapidly designs chemical syntheses by generating novel intermediate reactions, utilizing deep learning to propose synthesis pathways within seconds, and ranking them based on user-defined criteria, including cost and feasibility, to efficiently determine viable synthesis routes for target molecules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If chemists manually review scientific papers to determine synthesis pathways, then they can find substrates that react to yield target molecules, but the process is highly inefficient and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of chemists reviewing scientific papers with an automated computer-based system that uses machine learning models and natural language processing to extract reaction information from literature, thereby eliminating the time-consuming manual effort while maintaining or improving accuracy
Solution Approach 2:
The patent introduces an intermediary automated system that acts as a bridge between scientific literature and synthesis pathway determination, using trained models to process and interpret reaction data from papers, thus freeing chemists from direct manual review while preserving the ability to accurately identify viable synthesis routes
2Reliability
If chemists manually determine synthesis pathways by reviewing numerous papers, then they can identify viable reactions, but the process is prone to errors
Solution Approach 1:
The patent replaces the error-prone manual process with an automated computer-based system that consistently applies trained machine learning models to evaluate reactions, eliminating human fatigue and inconsistency while improving both accuracy and throughput of synthesis pathway determination
Solution Approach 2:
The patent implements feedback mechanisms where the system learns from training data and continuously improves its ability to identify viable reactions, using iterative optimization to reduce errors and improve reliability of synthesis pathway recommendations
3Productivity
If an automated system is introduced to speed up synthesis pathway determination, then productivity increases, but the complexity of the system increases
Solution Approach 1:
The patent divides the complex automated system into modular components including separate modules for text extraction, reaction identification, feasibility assessment, and pathway optimization, allowing each component to be independently developed, tested, and maintained while collectively achieving high productivity
Solution Approach 2:
The patent creates a multi-functional automated system that can process different types of scientific literature, identify various reaction types, assess feasibility using multiple criteria, and generate comprehensive synthesis pathways, thereby achieving high productivity through a single versatile platform
Data Source
AI summary
Methods and systems provide proposed pathways for synthesizing chemical reactions given a user-proposed target molecule, user-provided reaction constraints, or a combination of both. Embodiments may leverage training the model using both known successful reactions and infeasible reactions, either known or created by a prior use of the model. Chemical reactions for producing the target molecule and substrates are proposed using the model. From the proposed reactions, synthesis pathways are extracted and ranked according to a cost estimation. The ranked synthesis pathways are then provided to the user.