Machine Learning Ranking for Chemical Structure Prioritization
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Solution Overview
Problem
Current methods for ranking chemical structures in drug discovery are inefficient, leading to high costs due to failed molecules in pre-clinical and clinical testing, as existing algorithms fail to accurately prioritize chemical structures based on their potential for clinical success.
Innovation Solution
The development of ranking techniques in machine learning that determine and minimize ranking error by learning a ranking function tailored for chemical structures, using kernel-based algorithms and gradient-based optimization methods to prioritize chemical structures effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ranking algorithms are used to prioritize chemical structures, then the screening process can be completed, but the accuracy of ranking is insufficient leading to high failure rates in pre-clinical and clinical testing
Solution Approach 1:
The patent transforms the ranking problem from traditional classification or regression parameters to ranking-specific parameters. It introduces ranking error as a distinct parameter that measures the discrepancy between predetermined rankings and algorithm-generated rankings, enabling optimization of ranking accuracy independent of traditional predictive metrics.
Solution Approach 2:
The patent replaces traditional machine learning algorithms (classification and regression) with ranking-specific algorithms. It substitutes the mechanical system of conventional predictive modeling with a ranking optimization system that directly minimizes ranking error through gradient-based optimization and kernel methods tailored for ranking problems.
2Productivity
If comprehensive screening of chemical libraries is performed to identify promising candidates, then more potential drug candidates are found, but the cost increases due to failed molecules in expensive pre-clinical and clinical testing
Solution Approach 1:
The patent performs preliminary ranking optimization before the expensive pre-clinical and clinical testing phases. By developing and applying ranking functions that minimize ranking error on training data, the system pre-screens chemical libraries to identify the most promising candidates, thereby reducing the number of molecules that proceed to expensive testing phases and lowering overall development costs.
3Adaptability or versatility
If ranking algorithms are developed specifically for chemical structures, then the relevance to drug discovery is improved, but the algorithm complexity increases compared to general-purpose ranking algorithms
Solution Approach 1:
The patent segments the ranking problem into distinct components: (1) representing chemical structures as data elements, (2) defining ranking error specific to chemical structure prioritization, (3) applying kernel methods adapted for chemical data, and (4) using gradient-based optimization for ranking functions. This segmentation allows domain-specific optimization without requiring a completely new algorithmic framework.
Data Source
AI summary
Methods, systems and media are taught utilizing ranking techniques in machine learning to learn a ranking function. Specifically, ranking algorithms are applied to learn a ranking function that advantageously minimizes ranking error as a function of targeted ranking order discrepancies between a predetermined first ranking of a training plurality of data elements and a second ranking of the training plurality of data elements by the ranking function. The ranking algorithms taught may be applied to ranking representations of chemical structures and may be particularly advantageous in the field of drug discovery, e.g., for prioritizing chemical structures for drug screenings.