ArticleACS medicinal chemistry letters2025
Evaluating the Diversity and Target Addressability of DELs using Scaffold Analysis and Machine Learning.
Article in ACS medicinal chemistry letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Interpretable QSAR and Complementary Docking for PARP1 Inhibitor Prioritization: Reliability Stratification and Near-Domain Screening.Pharmaceuticals (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DELs enable efficient experimental screening of vast combinatorial libraries, offering a powerful platform for drug discovery. Apart from ensuring the druglike physicochemical properties, other key parameters to maximize the success rate of DEL designs include the scaffold diversity and target addressability. While several tools exist to assess chemical diversity, a dedicated computational approach combining both parameters is currently lacking. Here, we present a cheminformatics tool leveraging scaffold analysis and machine learning to evaluate both scaffold diversity and target-orientedness. Using two in-house libraries as a case study, we demonstrate the workflow's ability to distinguish between generalist and focused libraries. This capability can guide medicinal chemists in selecting libraries tailored for specific objectives, such as hit-finding or hit-optimization. To facilitate utilization, this tool is freely available both as a web application and as a Python script at https://github.com/novalixofficial/NovaWebApp.
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Registered trials
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