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Related Experiment Video

Updated: Jul 8, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms
10:41

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

ScrambleBench: a workflow for comparative assessment of structure-based de novo generative models.

Veincent Yap1, Pan Xu1, Frankie S Mak1

  • 1Experimental Drug Development Centre, Agency for Science, Technology and Research (A*STAR), 10 Biopolis Road, Chromos, Singapore, 138670, Singapore.

Journal of Cheminformatics
|July 7, 2026
PubMed
Summary

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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Generative AI for drug discovery needs standardized evaluation. ScrambleBench offers a robust workflow to assess AI models, revealing limitations in generalization and highlighting the need for improved chemical diversity and binding accuracy in de novo molecular design.

Area of Science:

  • Computational chemistry and drug discovery.
  • Artificial intelligence in molecular design.

Background:

  • Generative artificial intelligence (AI) shows promise for de novo small molecule design in drug discovery.
  • Existing AI models for drug design often lack standardized evaluation, hindering reliable integration into medicinal chemistry workflows.
  • Assessing the robustness and reliability of structure-based generative AI models is crucial for their practical application.

Purpose of the Study:

  • To introduce ScrambleBench, a standardized benchmarking workflow for evaluating structure-based generative AI models in drug discovery.
  • To assess the performance of six representative generative AI models across diverse protein targets.
  • To identify key areas for improvement in AI-driven molecular design, focusing on chemical diversity, binding conformation, and docking affinity.
Keywords:
BenchmarkCheminformaticsDe novo drug designGenerative AISBDDScrambleBench

Related Experiment Videos

Last Updated: Jul 8, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms
10:41

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

Main Methods:

  • Developed ScrambleBench, a unified workflow integrating diversity analysis, conformational validity, docking reproducibility, pharmacophore matching, and virtual hit rate.
  • Evaluated six generative AI models (Pocket2Mol, PocketFlow, Lingo3DMol, DiffSBDD, PMDM, Chemistry42) against GPCRs, kinases, and hydrolases.
  • Utilized metrics including Hamiltonian Diversity (HamDiv) for assessing molecular set quality and dissimilarity.

Main Results:

  • No single generative AI model demonstrated overall dominance across all evaluated criteria.
  • Models exhibited limited generalization to target binding sites, even for proteins present in training data, resulting in high redocking RMSD and low virtual hit rates.
  • Explicit evaluation of chemical diversity and improved loss functions emphasizing physicochemical properties and pharmacophore recognition are necessary.

Conclusions:

  • ScrambleBench provides a transparent and reproducible framework for evaluating structure-based generative AI models in drug discovery.
  • Current generative models show limitations in generalization and require enhancements for practical medicinal chemistry applications.
  • Future AI frameworks should prioritize drug-like properties, accurate pharmacophore recognition, and robust chemical diversity assessment.