Bioptic B1: A Target-Agnostic Potency-Based Small Molecules Search Engine
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916782536654848 |
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| author | Vinogradov, Vlad Izmailov, Ivan Steshin, Simon Nguyen, Kong T. |
| author_facet | Vinogradov, Vlad Izmailov, Ivan Steshin, Simon Nguyen, Kong T. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_14572 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bioptic B1: A Target-Agnostic Potency-Based Small Molecules Search Engine Vinogradov, Vlad Izmailov, Ivan Steshin, Simon Nguyen, Kong T. Quantitative Methods Artificial Intelligence Information Retrieval 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. |
| title | Bioptic B1: A Target-Agnostic Potency-Based Small Molecules Search Engine |
| topic | Quantitative Methods Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2406.14572 |