ArticleAngewandte Chemie (International ed. in English)2025
Mitigating Molecular Aggregation in Drug Discovery With Predictive Insights From Explainable AI.
Article in Angewandte Chemie (International ed. in English), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Quantitative prediction of siRNA complexation by ionizable drugs enables their codelivery in nanoparticles.Science advances · 2026Article
- Explainable Artificial Intelligence: A Perspective on Drug Discovery.Pharmaceutics · 2025Review
- Mitigating Molecular Aggregation in Drug Discovery With Predictive Insights From Explainable AI.Angewandte Chemie (International ed. in English) · 2025Article
- Innovative perspectives on the discovery of small molecule antibiotics.npj antimicrobials and resistance · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Herein, we present the application of multi-channel graph attention network (MEGAN), our explainable AI (xAI) model, for the identification of small colloidally aggregating molecules (SCAMs). This work offers solutions to the long-standing problem of false positives caused by SCAMs in high-throughput screening for drug discovery and demonstrates the power of xAI in the classification of molecular properties that are not chemically intuitive based on our current understanding. We leverage xAI insights and molecular counterfactuals to design alternatives to problematic compounds in drug screening libraries. Additionally, we experimentally validate the MEGAN prediction classification for one of the counterfactuals and demonstrate the utility of counterfactuals for altering the aggregation properties of a compound through minor structural modifications. The integration of this method in high-throughput screening approaches will help combat and circumvent false positives, providing better lead molecules more rapidly and thus accelerating drug discovery cycles.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.