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.
@article{arxiv.2406.14572,
title = {Bioptic B1: A Target-Agnostic Potency-Based Small Molecules Search Engine},
author = {Vlad Vinogradov and Ivan Izmailov and Simon Steshin and Kong T. Nguyen},
journal= {arXiv preprint arXiv:2406.14572},
year = {2025}
}