Multi-fingerprint Browser

Multi-fingerprint Browser identifies close analogs of query molecules in the ZINC database by retrieving nearest neighbors across four complementary chemical fingerprints to support structure-activity relationship exploration.

Key Features:

• Database (ZINC): Searches the ZINC database containing over 13 million molecules.
• sFP (Substructure Fingerprint): Captures specific molecular substructures to identify compounds with similar building blocks.
• ECFP4 (Extended Connectivity Fingerprint): Encodes molecular structure based on atom connectivity and local chemical environments up to a specified diameter.
• MQNs (Molecular Quantum Numbers): Represents molecules using quantum-number descriptors derived from electronic properties.
• SMIfp (SMILES Fingerprint): Encodes SMILES linear notation into a fingerprint format for structural comparison.
• Similarity Metric: Computes similarity using the city-block distance metric and reports performance relative to the Tanimoto similarity measure.
• Nearest-Neighbor Retrieval: Retrieves up to 1,000 nearest neighbors per query across fingerprint-defined chemical spaces.
• Clustering: Organizes retrieved neighbors into clusters using the K-means algorithm.

Scientific Applications:

• Hit confirmation and SAR exploration: Confirms activity of initial small-molecule hit compounds from activity screening by identifying and testing analogs to explore structure-activity relationships.
• Analog prioritization for experimental testing: Generates focused lists of analogs likely to exhibit similar bioactivity for selection and evaluation.
• Drug discovery and medicinal chemistry support: Assists compound selection and prioritization in drug discovery and medicinal chemistry research through multi-fingerprint chemical similarity analysis.

Methodology:

Nearest neighbors are retrieved from the ZINC database across four fingerprints (sFP, ECFP4, MQNs, SMIfp) using the city-block distance metric (up to 1,000 neighbors), and retrieved neighbors can be organized into clusters via K-means.