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Bioptic B1: A Target-Agnostic Potency-Based Small Molecules Search Engine

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.

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BIOPTIC B1 Identifies Novel Miro1 ligands for Friedreich's ataxia — Stanford-led Cell Chemical Biology Study

Publications

BIOPTIC Agent Hunt Globally — Wide Search AI Agents for Drug Asset Scouting in Investing, Business Development, and Competitive Intelligence (arXiv, 2026)

Publications

BIOPTIC B1 Ultra-High-Throughput Virtual Screening System Discovers LRRK2 Ligands in Vast Chemical Space

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