2024/06/13 by В. М. Виноградов, Ivan Izmailov, Vinogradov, Vlad +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Biosensing Techniques and Applications #Artificial Intelligence (cs.AI) #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Genetics, Bioinformatics, and Biomedical Research #Information Retrieval (cs.IR) #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2406.14572
openalex publication_date 2024/06/13 · openalex created_date 2024/06/25 · openalex updated_date 2026/07/28
Recent successes in virtual screening have been made possible by large models and extensive chemical libraries. However, combining these elements is challenging: the larger the model, the more expensive it is to run, making ultra-large libraries unfeasible. To address this, we developed a target-agnostic, efficacy-based molecule search model, which allows us to find structurally dissimilar molecules with similar biological activities. We used the best practices to design fast retrieval system, based on processor-optimized SIMD instructions, enabling us to screen the ultra-large 40B Enamine REAL library with 100% recall rate. We extensively benchmarked our model and several state-of-the-art models for both speed performance and retrieval quality of novel molecules.