Deep Learning Framework for Identifying ADR-Associated Chemical Substructures
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Solution Overview
Problem
Current drug development processes face challenges in predicting and preventing adverse drug reactions (ADRs) due to limited available information, particularly in the early stages, leading to safety concerns and high financial costs.
Innovation Solution
A computer-implemented method using a deep learning framework to analyze drug chemical structures and known ADR associations, enabling the identification of chemical substructures likely to cause ADRs, which can guide the design of safer drug candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional drug development processes are used, then drug candidates can be developed, but adverse drug reactions cannot be predicted in early stages leading to safety concerns and high costs
Solution Approach 1:
The patent applies preliminary action by performing ADR prediction in the early stages of drug development, before clinical trials and expensive failed attempts. The system analyzes chemical structures and predicts potential ADRs beforehand, allowing researchers to modify drug candidates before advancing them to later development stages where failures would be most costly.
Solution Approach 2:
The patent uses chemical structure information as an intermediary to predict ADRs. By analyzing the chemical structure of drug candidates and comparing it with known structures associated with specific ADRs, the system mediates between limited available information and the need for reliable safety prediction, enabling early risk assessment without requiring extensive clinical data.
2Productivity
If drug development proceeds without ADR prediction, then development timeline is maintained, but financial costs increase due to late-stage failures
Solution Approach 1:
The system performs ADR prediction early in the drug development pipeline, before significant financial resources are invested in clinical trials and scale-up. By identifying potential ADRs beforehand, the system prevents wasted investment in drug candidates that would fail later, thereby improving overall development efficiency and reducing financial losses.
Solution Approach 2:
The patent implements feedback by using known drug-ADR associations to train predictive models that provide feedback on new drug candidates. The system learns from historical data about which chemical structures are associated with which ADRs, and uses this learned knowledge to predict risks for new candidates, creating a feedback loop that improves prediction accuracy and guides drug design decisions.
Data Source
AI summary
Embodiments of the present invention are directed to a computer-implemented method for generating a framework for analyzing adverse drug reactions. A non-limiting example of the computer-implemented method includes receiving to a processor, a plurality of drug chemical structures. The non-limiting example also includes receiving, to the processor, a plurality of known drug-adverse drug reaction associations. The non-limiting example also includes constructing, by the processor, a deep learning framework for each of a plurality of adverse drug reactions based at least in part upon the plurality of drug chemical structures and the plurality of known adverse-drug reaction associations.