Molecular similarity search identifies compounds with similar structures or properties to a given molecule. It is a crucial tool in drug discovery, chemical research, and material science.
The process begins by representing molecules as data structures, such as fingerprints, SMILES strings, or molecular graphs. Fingerprints are binary vectors that encode key molecular features, including atom types, bonds, and functional groups.
The system generates a fingerprint for the query molecule and compares it with fingerprints in a database. Similarity is measured using metrics like the Tanimoto coefficient, which quantifies the overlap between two fingerprints.
Advanced approaches use graph neural networks (GNNs) to create embeddings that capture both structural and functional properties of molecules. These embeddings are stored in vector databases, enabling scalable and efficient similarity searches.
Molecular similarity search helps researchers identify potential drug candidates, repurpose existing compounds, or predict chemical activity. Its accuracy depends on the quality of molecular representations and the chosen similarity metric.