Core Hopping for Lead Compound Binding Prediction
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Solution Overview
Problem
Current methods for identifying potential lead compounds with higher binding affinity to biomolecular targets are inefficient, as they often rely on costly empirical testing and struggle to accurately calculate binding free energy differences, especially when changing the core region of an initial lead compound involves forming or annihilating multiple covalent bonds.
Innovation Solution
A computer-based method that partitions an initial lead compound into a core and non-core region, identifies alternative cores to replace the core, and calculates the binding free energy difference using free energy perturbation techniques, predicting whether potential lead compounds will bind to the biomolecular target.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If empirical testing methods are used to identify potential lead compounds with higher binding affinity, then binding affinity prediction accuracy may be improved, but the cost and time required for drug discovery increases significantly
Solution Approach 1:
The patent performs preliminary computational analysis by partitioning the initial lead compound into core and non-core regions before conducting full empirical testing. This preliminary action identifies which regions are most likely to influence binding affinity, allowing researchers to focus subsequent empirical testing on specific modifications rather than testing all possible compounds, thereby reducing overall discovery time while maintaining accuracy
Solution Approach 2:
The patent segments the lead compound into core and non-core regions, allowing independent analysis and modification of each region. This segmentation enables targeted computational screening of core region modifications while keeping non-core regions constant, significantly reducing the number of compounds that need to be tested empirically while still identifying high-affinity binders
2Strength
If core region modifications are made to improve binding affinity, then stronger binding may be achieved, but the complexity of calculating binding free energy increases due to multiple covalent bond changes
Solution Approach 1:
The patent segments the molecular system into core region, non-core region, and biomolecular target components. This segmentation allows the binding free energy calculation to focus primarily on the core region modifications and their direct interactions with the target, rather than calculating energies for entire molecules with multiple variable regions, thereby reducing computational complexity while still capturing the essential binding strength changes
Solution Approach 2:
The patent extracts and isolates the core region modifications from the complete molecule for separate analysis. By taking out the core region as the primary variable and keeping non-core regions constant, the binding free energy calculation becomes focused on the essential bond changes, reducing the overall complexity of the calculation while maintaining accuracy for the modified core structures
3Reliability
If comprehensive empirical testing of all potential lead compounds is performed, then the most active compounds can be identified with high confidence, but the resource consumption and cost increase dramatically
Solution Approach 1:
The patent performs preliminary computational partitioning and core region identification before empirical testing. This preliminary action creates a focused subset of potential compounds by identifying which core modifications are most likely to maintain or improve binding, allowing subsequent empirical testing to be concentrated on this smaller, higher-probability subset rather than testing all possible compounds, thus maintaining reliability while reducing the quantity of substances required
Solution Approach 2:
The patent segments the compound library into core-modified variants and non-core variants, focusing empirical testing primarily on core modifications that show promise in preliminary analysis. This segmentation strategy identifies a smaller subset of compounds (those with modified core regions) that are most likely to contain the most active compounds, reducing the total number of compounds that need to be synthesized and tested while maintaining high confidence in identifying active leads
Data Source
AI summary
A system, device, and method for predicting an active set of compounds that bind to a biomolecular target is disclosed. The system and device contain modules allowing for the prediction of an active set of compounds. A core identification module can identify the core of an initial lead compound. A core hopping module is used to identify potential lead compounds having different cores compared to the core of an initial lead compound. A scoring module can use computational techniques to calculate the relative binding free energy of each identified potential lead compound. An activity prediction module can use the relative binding free energy calculations to predict an active set of compounds that bind to the biomolecular target. Empirical analysis can be used to inform the accuracy and completeness of the predicted active set of compounds.