Large-Scale Molecular Similarity Search Using Vector Database

Molecular similarity search is a task in the field of cheminformatics that enables researchers to identify molecules with structural or chemical properties similar to a target compound. This study demonstrates an approach of leveraging vector databases for more efficient large-scale molecular similarity search to facilitate fast comparisons using advanced database indexing techniques. Molecules are often represented as high-dimensional vectors that aim to capture their essential chemical features. That makes for a logical application of the vector databases in efficient molecular similarity search. In comparison with traditional similarity search data architecture, this method significantly reduces computational costs by avoiding the prohibitive expense of calculating complex molecular similarities in real-time and by using advanced clustering methods making large-scale molecular databases more accessible for applications in drug discovery. In this paper we implement a vector database for storing molecular data. Our implementation showcases the current status of the vector database technology for cheminformatics applications and provides a solution for storing and efficient handling of vast openly available molecular datasets. The implemented functionality of molecular similarity search displays greater efficiency compared to currently available solutions.