Comparative Analysis of Molecular Embeddings for Efficient Compound Similarity Search Using Vector Databases

Compound similarity is fundamental to various cheminformatics analyses, particularly in the drug discovery industry, where the structure-activity principle is central to medicinal chemistry. Historically, binary fingerprints combined with Tanimoto and “Tanimoto-related metrics” (such as Dice, Sørensen–Dice, and Tversky) have been the gold standard for compound similarity measures. However, with the rise of large language models (LLMs) and retrieval- augmented generation (RAG), the use of embeddings for molecular representation, storage, and search has gained attention. In this study, we systematically compare molecular embeddings generated by different models, including autoencoders (AE), graph convolutional neural networks (GCNN), BERT-like models, word2vec-like and molecular attention transformers (MAT). We evaluate the performance of these embeddings against the widely used ECFP fingerprints in terms of similarity searching and clustering. To facilitate efficient searches, we leverage vector databases, which have been well established in the fields of natural language processing and RAG. Our findings demonstrate that Continuous Data-Driven Descriptors (CDDD) and MolFormer outperform tradi- tional methods in terms of similarity search efficiency and speed. This research contributes to advancing molecular representation techniques in drug discovery, potentially accelerating the identification of promising drug candidates.