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

arXiv 2406.14572 ↗First posted 13 Jun 2024q-bio.QM cs.AI cs.IRcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 13 Jun 2024.

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References · 6

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  4. 2017Large Batch Training of Convolutional Networkscited by 535
  5. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 375
  6. 2023Lo-Hi: Practical ML Drug Discovery Benchmarkcited by 10

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