Binary Similarity Search Using Hamming Threshold Conversion
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Solution Overview
Problem
Similarity searches in molecular databases using the Tanimoto metric are computationally expensive and time-consuming due to the complexity of calculating similarity between molecular fingerprints with thousands of features.
Innovation Solution
A system that converts Tanimoto thresholds to Hamming thresholds, utilizing Hamming distances and Boolean operations to measure similarity between binary vectors, and clusters candidate vectors to reject dissimilar ones before measurement, significantly speeding up the search process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Tanimoto metric is used to measure similarity between molecular fingerprints, then measurement precision is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent transforms the Tanimoto similarity metric into the Hamming distance metric, changing the mathematical parameter used for measurement. This transformation allows the use of efficient bitwise operations (XOR and population count) instead of the computationally intensive Tanimoto calculation, thereby reducing processing time while maintaining measurement capability
Solution Approach 2:
The patent replaces the mechanical computation of Tanimoto similarity with an electrical/digital implementation using bitwise operations. By substituting the traditional similarity calculation mechanism with Hamming distance computation using XOR gates and population count circuits, the system achieves faster processing while preserving the essential similarity measurement function
2Measurement precision
If Tanimoto metric is used to measure similarity between molecular fingerprints, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent changes the measurement parameter from Tanimoto similarity to Hamming distance, which can be computed using simple bitwise operations. This parameter transformation simplifies the computational device requirements while maintaining the ability to measure similarity between molecular fingerprints
Solution Approach 2:
The patent substitutes complex Tanimoto calculation mechanics with simpler Hamming distance mechanics implemented through bitwise XOR operations and population count functions. This substitution reduces device complexity by replacing intensive arithmetic operations with efficient logical operations
3Measurement precision
If all candidate vectors are measured against query vector, then measurement precision is maintained, but productivity decreases
Solution Approach 1:
The patent performs preliminary actions by converting the Tanimoto threshold to a Hamming distance threshold before the actual similarity search. This preliminary transformation enables the use of efficient bitwise operations during the search process, increasing productivity while maintaining the ability to accurately identify similar molecules through the Hamming comparator
Data Source
AI summary
A system for measuring similarity between a binary query vector and a plurality of binary candidate vectors includes a storage unit and a processor. The storage unit stores the binary query vector and the plurality of candidate vectors, and the processor performs Tanimoto calculations in terms of Hamming distances.