Molecular similarity search identifies molecules with similar chemical structures or properties. This technique is critical in drug discovery and material science, where finding analogous compounds can speed up innovation.
The process begins by representing molecules as structured data, such as SMILES strings, fingerprints, or molecular graphs. Fingerprints, often used for similarity search, are binary vectors encoding molecular features like bonds, atom types, and functional groups.
A query molecule’s fingerprint is generated and compared to the fingerprints of molecules in a database. Similarity metrics, such as Tanimoto coefficient or Jaccard index, measure the overlap between the query and database fingerprints. A higher score indicates a closer match.
More advanced methods use graph neural networks (GNNs) to generate embeddings for molecules, capturing both structural and functional properties. These embeddings are compared using vector similarity techniques in a vector database for scalable and accurate searches.
Molecular similarity search enables tasks like identifying potential drug candidates, predicting compound activity, and repurposing existing molecules for new applications. Its effectiveness depends on the quality of molecular representations and the choice of similarity metrics.