Joint Pharmacophore Space Mining for Drug Discovery
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Solution Overview
Problem
Current pharmacophore-based drug discovery techniques are limited in their ability to analyze compounds on a target-by-target basis and require knowledge of geometric properties of binding pockets, which restricts the search space and is costly in terms of time and resources.
Innovation Solution
The concept of joint pharmacophore space is introduced, where a database of pharmacophores is generated based on geometric arrangements of both active and inactive compounds against multiple targets, allowing for the analysis of entire pharmacophore spaces without assuming any knowledge of binding pocket geometries, and enabling the identification of pharmacophoric patterns and subspaces with statistically significant binding activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pharmacophore-based drug discovery techniques are used to analyze compounds on a target-by-target basis, then the analysis can be performed with existing methods, but the search space is restricted and binding pocket geometry knowledge is required
Solution Approach 1:
The patent extracts the essential pharmacophoric features (geometric arrangements of pharmacophoric points) from the complex binding pocket geometry knowledge requirement. By taking out only the critical geometric arrangement information from compound structures and forming a joint pharmacophore space, the method eliminates the need for expensive and complex binding pocket structure data while maintaining the ability to analyze compound-protein interactions effectively
Solution Approach 2:
The joint pharmacophore space serves as an intermediary representation that bridges compound structures and biological activities without requiring direct access to binding pocket geometries. This intermediary space allows the system to capture essential binding characteristics through pharmacophoric feature arrangements, enabling target-by-target analysis without the complexity of full structural docking
2Measurement precision
If binding pocket geometry knowledge is required for pharmacophore analysis, then accurate binding predictions can be made, but the process becomes costly in terms of time and resources
Solution Approach 1:
The patent replaces expensive and time-consuming binding pocket structure determination with a more economical approach using pharmacophoric feature extraction. By using disposable-like simplified geometric representations of pharmacophoric features rather than requiring expensive structural biology data, the method maintains prediction accuracy while dramatically reducing computational and temporal costs
Solution Approach 2:
The method performs preliminary extraction and classification of pharmacophoric features into distinct spaces (e.g., ion channel space, GPCR space) before actual binding prediction. This preliminary organization of pharmacophoric arrangements by target class enables efficient querying and reduces the need for exhaustive analysis of binding pocket geometries, saving significant time and computational resources
Data Source
AI summary
A technique for mining pharmacophore patterns including computer-implemented methods for generating a database of pharmacophores and computer-implemented methods for classifying a query molecule with the database of pharmacophores. Generation of the pharmacophore database includes methods of defining a Joint Pharmacophore Space (JPS) using the three-dimensional geometry of pharmacophoric features of all active molecules against multiple targets.