Anti-Infective Design Space GUI for AI Drug Candidate Selection
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Solution Overview
Problem
Conventional drug discovery techniques are inefficient, limited in application, and fail to discover therapeutics for certain diseases due to constrained design spaces and computational inefficiencies, leading to ineffective or dangerous drug candidates.
Innovation Solution
An artificial intelligence engine utilizing various encoding types and machine learning models, including generative deep learning methods, expands the design space to generate candidate drugs efficiently, reducing computational complexity and enhancing the discovery of effective therapeutics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional drug discovery techniques are used, then the process is simple and straightforward, but the effectiveness and productivity of discovering therapeutics for certain diseases is poor
Solution Approach 1:
The patent segments the drug discovery process into multiple computational stages: generating diverse peptide sequences, predicting their activities against different pathogens, filtering based on desired properties, and validating candidates. This segmentation allows comprehensive exploration of the design space while maintaining manageable complexity at each stage.
Solution Approach 2:
The patent introduces computational models and algorithms as intermediaries between the design space and experimental validation. These computational tools predict peptide activities and properties, enabling efficient filtering and selection of candidates before wet lab experiments, thus improving productivity without proportionally increasing experimental complexity.
2Loss of information
If the design space is expanded to include various drug information, activity, and semantic data, then the comprehensiveness of drug candidate evaluation is improved, but the computational complexity increases
Solution Approach 1:
The patent adds multiple dimensions to the design space exploration by incorporating diverse data types (sequence information, structural data, activity profiles against multiple pathogens, semantic annotations) and analyzing them through computational models. This multi-dimensional approach comprehensively evaluates candidates while using algorithms to manage the complexity of integrating and processing such diverse information.
Solution Approach 2:
The patent develops a universal computational framework that handles multiple types of drug information and evaluation criteria simultaneously. The system can predict various activities (antimicrobial, antiviral, anticancer), evaluate multiple properties (toxicity, stability, binding affinity), and integrate semantic data through a single integrated platform, reducing the need for separate analytical tools for each data type.
3Measurement precision
If machine learning models and causal inference are used to generate candidate drug compounds, then the precision of identifying effective drugs is improved, but the device complexity and computational resources required increase
Solution Approach 1:
The patent applies preliminary computational filtering and prediction using machine learning models before experimental validation. The AI engine pre-identifies promising candidates by analyzing sequence features, predicting activities, and evaluating properties in silico, so that only the most promising candidates proceed to wet lab experiments. This preliminary action significantly improves the precision of candidate identification while managing computational complexity through staged processing.
Data Source
AI summary
In one aspect, a method is disclosed for presenting, on a computing device, a graphical user interface (GUI) of a therapeutic tool. The method includes presenting, in a first screen of the GUI, a design space for a protein for an application, where the design space includes a set of sequences, where each sequence contains a respective set of activities pertaining to the application. The method also includes receiving, via a graphical element in the first screen, a selection of one or more query parameters of the design space, and presenting, in a second screen of the GUI, a solution space that includes a subset of the set of sequences, where each sequence contains the respective set of activities, where the subset of the set of sequences is selected based on the one or more query parameters.