Chemical fingerprints are compact, computer-readable representations of molecular structures, typically encoded as binary strings or bit vectors. Each bit in a fingerprint corresponds to the presence or absence of specific substructures, patterns, or molecular features, enabling rapid comparison, searching, and clustering of large chemical libraries. Fingerprints are compared through similarity coefficients like the Tanimoto coefficient that calculates the ratio of the number of shared features to the total number of unique features. Examples of fingerprints are MACCS keys where a bit corresponds to a specific substructure, and ECFP4 that encodes atomic environments up to a given radius.
Importance in Computational Drug Discovery:
- Enables efficient similarity searching and clustering of vast compound libraries for hit identification and lead optimization.
- Facilitates virtual screening by allowing rapid comparison of candidate molecules to known actives or reference structures.
- Supports diversity analysis and compound selection for screening campaigns.
- Underpins machine learning and cheminformatics workflows by providing standardized molecular descriptors.
- Assists in scaffold hopping, analog searching, and structure–activity relationship (SAR) analysis.
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