Supplementary Figures for Transformer-Based Chemical Similarity Search

Written by penicillin | Published 2025/03/06
Tech Story Tags: ai-in-chemistry | chemical-similarity-search | drug-discovery | smiles | prompt-engineering | ai-research | molecular-embeddings | machine-learning

TLDRSupplementary figures showcase key findings from CheSS searches, including Tanimoto coefficients, feature cosine similarities, and token vector comparisons. These visuals highlight how different SMILES canonicalizations impact molecular search behavior, emphasizing structural vs. functional similarity in chemical discovery.via the TL;DR App

Authors:

(1) Clayton W. Kosonocky, Department of Molecular Biosciences, The University of Texas at Austin (clayton.kosonocky@utexas.edu);

(2) Aaron L. Feller, Department of Molecular Biosciences, The University of Texas at Austin (aaron.feller@utexas.edu);

(3) Claus O. Wilke, Department of Integrative Biology, The University of Texas at Austin and Corresponding Author (wilke@austin.utexas.edu);

(4) Andrew D. Ellington, Department of Molecular Biosciences, The University of Texas at Austin (ellingtonlab@gmail.com).

Table of Links

  1. Abstract & Introduction
  2. Methods
  3. Results and Discussion
  4. Determining Whether Canonicalization Impacts Search Behavior
  5. Explanation of Search Behavior & Drawbacks, Future Improvements, and Potential for Misuse
  6. Conclusion, Acknowledgements, Author Contributions, & more.
  7. Supplementary Figures

Supplementary Figures

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.


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Published by HackerNoon on 2025/03/06